• Band numbers are easy to mistype.
• Nested where(...) expressions make it easy to miss a parenthesis.
• Once conditions become complex, it is hard to tell whether the logic has been reversed.
• The desired result may be "population density", "risk level", or "elevation zone", but the business rule is clearer in your head than the formula syntax.

The Raster Calculator and Field Calculator in iXGIS now support AI-assisted formula writing. In practical terms, you can describe the requirement in plain language first, then let AI turn it into an expression that can run in the current window.

This article focuses on three questions:

1. Which scenarios are suitable for AI-generated formulas?
2. How can you write prompts that are more likely to produce correct results on the first try?
3. How do you use this workflow in the Raster Calculator, Field Calculator, and attribute table field calculation?

When AI Formula Writing Is Useful

If the expression is as simple as a + b, writing it manually is usually faster.

AI saves the most time in cases like these:

• You know the business rule, but do not want to translate it into formula syntax by hand.
• You need to write multi-level where(...) classification logic.
• You need to handle several conditions at once and are worried about parentheses or operator precedence.
• You already have a formula but want to rewrite it into a clearer multi-line structure.
• You need to use area, length, bands, or null checks, which are easy to mix up.

A simple rule is: once you start wondering whether you wrote something incorrectly, it is a good time to let AI draft a first version.

How to Use AI to Generate Formulas

1. State What You Want to Calculate

Do not only say "write a formula for me".

Better prompts include:

• "Calculate NDVI."
• "Classify elevation into four classes."
• "Calculate population density from population and area."
• "Classify roads by length."

2. Specify the Fields, Rasters, or Bands to Use

The more specific you are, the better.

For example:

• image_a.tif.band4 and image_a.tif.band3
• Population and AREA
• slope, rain

If the names are vague or incorrect, the generated result is likely to be off as well.

3. Define Boundary Conditions Clearly

This is the part most likely to go wrong.

Instead of saying only "divide it into four classes", write something like:

Values less than or equal to 1610 are 1; values greater than 1610 and less than or equal to 2415 are 2; values greater than 2415 and less than or equal to 3468 are 3; all other values are 4.

This helps AI write complete interval boundaries and reduces overlaps or gaps.

4. Explain How Null Values Should Be Handled

This is important and often overlooked.

You can tell AI directly:

• "If any input is null, output null."
• "Treat null values as 0."
• "Output nan when the denominator is 0."

If this is not stated clearly, the result may run successfully but have the wrong business meaning.

Using AI in the Raster Calculator

The Raster Calculator is suitable for cell-by-cell operations, such as indices, masks, reclassification, weighted overlay, and multi-band combinations.

Common use cases include:

• Remote sensing indices such as NDVI and NDBI
• Threshold extraction from elevation, slope, rainfall, and similar rasters
• Reclassification by value range
• Combining multiple rasters into a weighted evaluation result

You can load a template that already contains prepared data.

Example 1: Ask AI to Write NDVI

You can say:

Use band4 and band2 from air-survey-multiband-raster-100m.tif to calculate NDVI.

AI will likely return a formula close to this:

result = (air-survey-multiband-raster-100m.tif.band4 - air-survey-multiband-raster-100m.tif.band2) / (air-survey-multiband-raster-100m.tif.band4 + air-survey-multiband-raster-100m.tif.band2)

With null-value handling:

# prompt
Use band4 and band2 from air-survey-multiband-raster-100m.tif to calculate NDVI. If the denominator is 0 or any input is null, output nan.
# formula
nir = air-survey-multiband-raster-100m.tif.band4
red = air-survey-multiband-raster-100m.tif.band2
num = nir - red
den = nir + red
result = where(logical_or(den == 0, logical_or(isnan(nir), isnan(red))), nan, num / den)

The advantage of this approach is that AI does not only write the formula; it also helps account for exceptional cases.

Note: Boolean functions cannot rely on default null handling. Null handling must be written explicitly.

Example 2: Ask AI to Write Reclassification Logic

Classify dem.tif into four classes: values less than or equal to 100 are 1; values greater than 100 and less than or equal to 500 are 2; values greater than 500 and less than or equal to 1500 are 3; all other values are 4. Keep null values as null.

Possible result:

result = where(isnan(dem.tif), nan, where(dem.tif <= 100, 1, where(logical_and(dem.tif > 100, dem.tif <= 500), 2, where(logical_and(dem.tif > 500, dem.tif <= 1500), 3, 4))))

This is especially suitable for AI because nested where formulas are where people most often miss a parenthesis in the second or third layer.

Example 3: Ask AI to Write a Risk Mask

Generate a risk mask from slope.tif and rain.tif: cells with slope greater than 15 and rainfall greater than 500 should be 1; all other cells should be 0. If either input is null, output null.

Possible result:

result = where(logical_or(isnan(slope.tif), isnan(rain.tif)), nan, where(logical_and(slope.tif > 15, rain.tif > 500), 1, 0))

Using AI in the Field Calculator

The Field Calculator focuses on attribute calculation. It is suitable for generating or updating field values in batches for vector features.

Common scenarios include:

• Calculating indicators such as population density, output density, and coverage rate
• Generating level fields based on rules
• Converting values with geometry keywords such as AREA and LENGTH_GEODESIC
• Calculating an intermediate field first, then deriving a second field from it

Example 1: Generate a Population Density Field

Add a density field and calculate population density as population divided by area.

Possible result:

density = Population / AREA_GEODESIC

Note: For vector data in a geographic coordinate system, use the geodesic area keyword AREA_GEODESIC to calculate each polygon's geodesic area. The unit is square meters.

Example 2: Generate Two Fields at Once

Set density = Population / Area, with the unit as people per square kilometer. Then add a density_level field: values less than or equal to 1000 are 'Low', values from 1000 to 3000 are 'Medium', and values greater than 3000 are 'High'.

Possible result:

density = Population * 1000000 / AREA_GEODESIC
density_level = where(density <= 1000, 'Low', where(density <= 3000, 'Medium', 'High'))

This kind of multi-step logic is well suited to AI because it can automatically split the calculation into more readable lines.

Example 3: Convert Length to Kilometers

Add a length_km field and calculate length in kilometers.

Possible result:

length_km = PERIMETER_LENGTH_GEODESIC / 1000

Calculate Field in the Attribute Table

In addition to the Field Calculator in the toolbox, the Calculate Field command in a vector layer's attribute table also works well with AI-assisted formula writing.

It is especially useful when:

• You have already opened the attribute table and want to add a business field immediately.
• You do not want to switch away and open the toolbox.
• You only need to run one attribute calculation on the current layer.

For example, if you are in the attribute table and want to add a risk level field, you can describe it in natural language:

Add a risk_level field: when Population is greater than 700000 and Rainfall is greater than 700, set it to 'High'; when Population is greater than 600000 or Rainfall is greater than 600, set it to 'Medium'; otherwise set it to 'Low'.

AI may return:

risk_level = where(logical_and(Population > 700000, Rainfall > 700), 'High', where(logical_or(Population > 600000, Rainfall > 600), 'Medium', 'Low'))

When calculating fields in the attribute table, pay extra attention to two things:

• Field names must exactly match the current attribute table.
• If area or length is involved, confirm that the current layer's coordinate system and geometry units match the business requirement.

Human Review

AI can save a lot of repetitive typing, but it is not recommended to copy and run a formula without reading it. Keep a human in the loop.

At minimum, check the following:

• Whether raster names, band numbers, and field names are correct
• Whether condition boundaries include the intended equality signs
• Whether intervals overlap or leave gaps
• Whether null values are handled as expected
• Whether area and length use the correct geometry keywords
• Whether output field names and result values match your naming and business rules

For classification tasks in particular, review each class yourself even if the AI-generated formula looks smooth.

Tip

If you already have a formula but it is hard to read, you can ask AI to rewrite it:

Rewrite the following one-line formula into a multi-line version that is easier to check, and give the intermediate variables clearer names.

This is usually more reliable than asking AI to generate everything from scratch, because you have already controlled the core logic. AI is only helping you organize the structure.

Related Articles

• Raster Calculator guide
• Calculate Field (Vector Calculator) guide
• Field calculation in the attribute table
• Editing vector attribute tables

• Satellite Imagery Download Tool
• Surface Data Download Tool
• Administrative Boundary Download Tool

These three tools have clear roles: one downloads basemaps, one downloads thematic rasters, and one downloads administrative boundaries. Many users may find the names a little confusing at first, but in practice you can choose based on the result you want to obtain.

Tool Overview

If you only want to create a background map, start with the Satellite Imagery Download Tool. If you need rasters that can be used directly in analysis, the Surface Data Download Tool is usually the right choice. If you need administrative boundary polygons, use the Administrative Boundary Download Tool.

Satellite Imagery Download Tool

This tool essentially saves an online basemap as a local GeoTIFF. It does not download raw remote sensing data. Instead, it downloads already rendered online tiles and automatically mosaics them into an output file.

According to the current help documentation, the common layers in the interface are Tianditu-related layers:

• Vector basemap (without labels / with labels)
• Imagery basemap (without labels / with labels)
• Terrain hillshade
• Global boundaries

There are several points worth clarifying first:

• The download result is an RGB raster suitable for display, comparison, and map production, not editable vector features.
• The tool works with Tianditu TileMatrixSet = w, which corresponds to Web Mercator EPSG:3857.
• The larger the area and the higher the zoom level, the more tiles are required and the longer the download takes.

This tool is therefore better suited for tasks such as:

• Preparing a stable offline basemap for a project
• Comparing current project data with an online basemap
• Preparing background imagery or road network labels before thematic map production

Related article:

• Satellite Imagery Download Tool Guide

Administrative Boundary Download Tool

This tool retrieves boundary data from the Tianditu administrative region API and outputs a vector file. Unlike the basemap download tool above, it produces polygon features that can be used directly for clipping, statistics, or overlay analysis.

According to the current documentation, the tool supports step-by-step selection by administrative level:

• Province
• City
• County or district

The output usually includes these fields:

• gb: administrative division code
• pgb: parent administrative division code
• name: administrative region name
• level: administrative level
• center_lng / center_lat: center point coordinates

The coordinate reference system is EPSG:4490, which is the CGCS2000 geographic coordinate system.

The three most common uses of this tool are:

• Quickly downloading administrative boundaries for a project area
• Using the boundaries as raster clipping extents or statistical zones
• Joining business tables by administrative region code

Related article:

• Administrative Boundary Download Tool Guide

Surface Data Download Tool

This tool is the easiest one to misunderstand. "Surface data" here does not refer only to land surface data in the narrow sense. It is a unified download entry for a set of base raster datasets in the platform. Typically, you select a data type first, then choose a source, year, or version, and finally export a GeoTIFF by area.

Based on the current help documentation, several commonly used data categories have already been organized in this tool.

Downloadable Data

1. Vegetation Index Data: NDVI

The documentation explicitly mentions annual NDVI data.

• Landsat 8 NDVI
  • Data source: Microsoft Planetary Computer landsat-c2-l2
  • Platform filter: landsat-8
  • Spatial resolution: 30 m
  • Result format: annual average NDVI
• Sentinel-2 NDVI
  • Data source: Microsoft Planetary Computer sentinel-2-l2a
  • Spatial resolution: 10 m
  • Result format: annual average NDVI

This type of data is generally used for vegetation cover change, regional comparison, or preliminary assessment of ecological conditions.

2. Land Cover Data

The land cover section is currently the most complete and already lists several common public data sources.

• ESA WorldCover
  • Resolution: 10 m
  • Coverage: global
  • Years: the current documentation lists 2020 and 2021
  • Characteristics: high resolution, suitable for interpreting current land cover conditions
• GlobeLand30
  • Resolution: 30 m
  • Years: 2000, 2010, 2020
  • Current platform note: downloads are currently available for China
  • Characteristics: a mature Chinese product system, suitable for domestic regional applications
• MODIS MCD12Q1
  • Resolution: approximately 500 m
  • Time range: annual series
  • Characteristics: long time span, suitable for long-term change analysis

If resolution is more important, WorldCover or GlobeLand30 is usually the first choice. If multi-year change matters more, MODIS is often easier to use.

For more information, see:

• Land Use Data Download and Usage Guide

3. Soil Data

The soil dataset explicitly mentioned in the current help documentation is HWSD v2. This type of data is generally not just a single-band result. Instead, it is organized by soil attribute and depth layer.

According to the documentation, common cases include:

• Downloadable parameters such as clay, sand, silt, organic carbon, and calcium carbonate
• Typically 7 depth layers
• Output is usually a multiband raster, with different bands corresponding to different soil layers

This type of data is better suited for soil attribute analysis or as base input for ecological and agricultural environment topics.

4. Terrain Data

The current documentation lists two terrain datasets:

• ASTER GDEM v3
• AW3D30 DSM

Both are base elevation datasets. Common use cases include:

• Terrain analysis
• Slope and aspect calculation
• Hillshade analysis
• Data preparation before hydrological analysis

If you only need to prepare elevation data for a project area, this tool is much more convenient than downloading tiles manually and mosaicking them yourself.

5. Climate and Meteorological Data

The example given in the current help documentation is the MODIS annual evapotranspiration product:

• Example product: MOD16A3GF.061
• Example layer: ET_500m
• Resolution: approximately 500 m
• Temporal resolution: annual

This type of data is commonly used as background indicators in ecology, hydrology, or regional assessment.

Tianditu Administrative Regions

Administrative region vector data usually includes these fields:

• gb: administrative division code
• pgb: parent administrative division code
• name: administrative region name
• level: administrative level
• center_lng / center_lat: center point coordinates

The coordinate reference system is EPSG:4490, which is the CGCS2000 geographic coordinate system.

What to Check Before Use

Although the workflow has been simplified, it is still recommended to confirm the following points before using the data in a project:

• Whether you need a rendered basemap or an analysis-ready raster
• Whether the data coordinate reference system matches the project CRS
• Whether the resolution meets the business scale requirements
• Whether the data coverage is global, China-wide, or limited to a local area
• Whether the year and version meet the project's temporal requirements

The simplest way to decide can be summarized in three sentences:

• For display basemaps, use the Satellite Imagery Download Tool.
• For raster analysis, use the Surface Data Download Tool.
• For administrative clipping and zonal statistics, download administrative boundaries first.

Related documentation:

• Satellite Imagery Download Tool Guide
• Surface Data Download Tool Guide
• Administrative Boundary Download Tool Guide

The POI Search feature in iXGIS is mainly used to retrieve these places within a specified spatial range and display the results directly on the map. The feature is implemented with reference to Tianditu's Place Name Search V2.0 service, so returned results usually include place name, address, longitude and latitude, phone number, and type. It is suitable for site selection research, facility surveys, thematic mapping, and basemap supplementation.

Compared with place search in ordinary web maps, POI Search in iXGIS focuses more on GIS workflows:

• You can limit the search range to the current view, an administrative region, or a project layer extent.
• You can place results directly on the map to inspect their spatial distribution.
• You can save selected results as point feature files in the project for further mapping and analysis.

1. What POI Search Can Do

POI Search is commonly used for four tasks:

1. Quickly find a type of facility in the current map window, such as hospitals, schools, or bus stops.
2. Systematically retrieve facility distribution within an administrative region, such as parks, community service stations, or parking lots in a district.
3. Supplement point-of-interest data within the coverage of existing project layers, such as collecting nearby facilities around a planning area, sample area, or survey area.
4. Save search results as point features for later buffer analysis, overlay analysis, layout mapping, or attribute cleanup.

If your goal is to find places and turn them into GIS data, this feature is a good fit. It can also be used directly if you only want to check a place temporarily.

2. Entry Point and Basic Interface

Click POI Search in the iXGIS toolbar. A POI Search panel opens on the right side.

The panel usually contains three parts:

• A keyword input area at the top
• A Search Options area in the middle
• A result list and save controls at the bottom

The top input box supports these operations:

• Press Enter after entering a keyword to search.
• Click the search button to search.
• Clear the keyword to clear both current results and POI points on the map.

This means POI Search uses an explicit input and explicit search workflow, rather than continuously suggesting results while you type.

3. What to Check Before Searching

Before using POI Search, confirm the following:

1. A Project Must Be Open

POI Search depends on the current project environment to manage map extent, result layers, and save paths. If no project is open, the system will not run the search.

2. A Keyword Is Required

POI Search is not a "browse all places" feature. It searches around a keyword. The keyword can be:

• A place name, such as "Beijing South Railway Station" or "Zhongshan Park"
• A place category, such as "hospital", "school", or "hotel"
• A business object, such as "charging station", "parking lot", or "gas station"

In general, the more specific the keyword, the more focused the results. The broader the keyword, the more results you may get, and pagination and filtering may take more effort.

3. Results Come from an Online Service

This feature refers to the Tianditu place-name search service, so result quality depends on the online service's data coverage, classification methods, and naming conventions. Keep these points in mind:

• Names may include abbreviations, aliases, or duplicate names.
• Fields such as phone number and address may be empty or incomplete.
• Some newly built places or small POIs may not be found immediately.

Therefore, POI Search is better used as a quick collection and supporting verification tool. For formal results that require strict review, manual verification is still recommended.

4. Choosing Among Three Search Ranges

iXGIS currently provides three search ranges:

• Current view
• Administrative region
• Layer extent

Although Tianditu place-name search also supports nearby search and category search, the iXGIS interface currently focuses on these three ranges because they better match practical GIS project workflows.

1. Current View

Current View is suitable for quickly searching places within the current map window.

The workflow is simple:

1. Zoom and pan the map to the target area.
2. Select Current View.
3. Enter a keyword and run the search.

This method has these characteristics:

• The search range directly follows the map area visible on the screen.
• The larger the map scale and the smaller the visible area, the more focused the results usually are.
• It is suitable for finding facilities around a local parcel, block, or station.

For example, after you have positioned the map to a district, entering "hospital" or "kindergarten" quickly shows the distribution of related facilities in the current visible area.

2. Administrative Region

Administrative Region is suitable for systematic searches by province, city, district, or county.

During operation, the system provides province, city, and district/county selections. It is usually best to select the most detailed available level so the search range is clearer and the results are easier to control.

This method is suitable for:

• Facility surveys such as schools, hospitals, and parks within a district
• Sorting the distribution of a service facility type across a city
• Preparing data before thematic statistics by administrative unit

In administrative-region search mode, the system returns POI results and displays the corresponding administrative boundary on the map. This helps you confirm whether the search boundary is correct. For users who work by administrative boundary, this is very practical.

3. Layer Extent

Layer Extent is suitable for searching POIs around existing data extents in a project.

Here, layer extent means that you already have a survey area, planning area, study area, or data coverage layer in the project, and you want to find POIs near or within that range.

Common uses include:

• Select a polygon layer as the search range.
• Select a raster layer as the search range.

If you select a polygon layer and enable Use Geometry Extent Only, the system tries to search by the geometry boundary. This is usually more precise than using only the layer's bounding rectangle.

There are two practical limits to note:

• When a layer contains multiple geometries, this mode is currently better suited to searching around one main geometry.
• When the geometry is very complex and has many vertices, the system may automatically switch to the layer's bounding extent to keep the search stable.

If you select a raster layer, or do not enable Use Geometry Extent Only, the system generally searches by the overall layer extent. This is more stable in performance, but the spatial constraint is wider.

5. What Information Appears in Search Results

POI search results usually include:

• Name
• Address
• Longitude and latitude
• Phone number
• Type

These fields are useful for quick interpretation in GIS scenarios.

For example:

• Name helps you judge whether the result is the target object.
• Address helps identify places with the same name.
• Longitude and latitude help confirm whether the location is reasonable.
• Type helps distinguish facility categories.
• Phone number can be used as reference information for later investigation or verification.

If some POIs do not have phone numbers, the system may show the field as empty or "None". This is normal and does not mean the search failed.

6. How Results Are Displayed on the Map

After the search is complete, results appear in the list and are loaded onto the map at the same time. Each result is displayed as a point, so users can judge its distribution spatially.

This display method has several direct benefits:

• You can quickly judge whether points fall inside the target area.
• You can identify obvious offsets or same-name places in different locations.
• You can combine basemaps, administrative boundaries, and business layers for integrated interpretation.

When you click a record in the result list, the corresponding point is highlighted. This lets you switch quickly between reading the list and locating on the map, which is especially useful when there are many results.

7. Pagination Rules and Result Limits

To keep the interface responsive, POI Search currently uses paginated display.

• Each page shows 10 results.
• When you switch pages, the system fetches the results for that page again.
• The current interface displays up to the first 200 results.

Therefore, if you search for common keywords such as "hospital", "school", or "residential community" in a large range, results may quickly hit the limit. In that case, optimize the search by:

• Narrowing the search range
• Selecting a more detailed administrative region
• Using a more specific keyword
• Limiting the spatial range with a thematic layer before searching

8. Saving Search Results as Project Data

An important advantage of POI Search is that results can be saved, not just viewed.

In the result list, select one or more places, then click Add to File to save them as point feature data in the project.

1. Add to an Existing File

If the project already contains a usable point layer, you can write selected results directly into that layer. This is suitable when:

• You have already created a collection layer for a facility type.
• You want to merge POI results from multiple batches into the same file.
• You need to edit symbols, fields, and attributes consistently later.

After saving, POI information such as name, address, phone number, and type is written into the feature attributes for later cleanup.

2. Add to a New File

If the current project does not have a suitable target layer, you can create a new point file directly to save the results.

Currently supported save formats include:

• PostGIS vector
• GeoPackage vector
• ESRI Shapefile

This method is better for thematic data collection, staged deliverable cleanup, or temporary working layer creation.

3. What You Can Do After Saving

Once saved as a project layer, these POIs are no longer temporary search results; they become formal GIS data. You can continue with:

• Attribute checking and supplementation
• Category-based symbolization
• Buffer analysis
• Overlay analysis with administrative regions, roads, land use, and other data
• Layout mapping and output

9. Why Administrative Boundaries Are Displayed

In Administrative Region search mode, the system displays the selected administrative region boundary at the same time. This is not just visual decoration; it is a practical aid for judgment.

Its main purpose is to:

• Make the current search boundary clear.
• Help compare whether result points are inside the target administrative region.
• Support later screenshots, checks, and thematic interpretation.

For users who often make district-level or city-level thematic maps, this boundary hint can greatly reduce misjudgment.

10. Recommended Usage Patterns

For practical GIS work, use POI Search in these ways.

Scenario 1: Quick Local Facility Survey

• Position the map to the target area.
• Select Current View.
• Enter keywords such as "hospital", "parking lot", or "bus stop".
• Review the result distribution and select the points you need.
• Save them as a project point layer.

Suitable for existing-condition surveys, site selection review, and local infrastructure surveys.

Scenario 2: Collect Thematic Points by Administrative Region

• Select Administrative Region.
• Choose the target district or county level by level.
• Enter the target keyword.
• Check the results against the administrative boundary.
• Save the results as a thematic layer.

Suitable for public service facility organization, district/county thematic mapping, and industry distribution surveys.

Scenario 3: Supplement External POIs Around a Project Area

• Prepare a study area, planning area, or sample area layer first.
• Select Layer Extent.
• If it is a polygon layer, preferably enable Use Geometry Extent Only.
• Enter a keyword and run the search.
• Save the results and analyze them together with project data.

Suitable for planning research, environmental surveys, regional evaluation, and thematic sample-area analysis.

11. Common Misunderstandings

1. No Results Does Not Necessarily Mean the Feature Failed

Possible reasons include:

• The keyword is too uncommon or uses a different naming convention.
• The search range is too small.
• The online service currently has no corresponding POI data.

Try expanding the range or using a more common name or category keyword.

2. Layer Extent Search May Not Always Follow Polygon Boundaries Exactly

If the geometry is complex, or the selected layer is not a polygon layer, the system may search by the overall extent. For scenarios that require very precise spatial constraints, review and filter the mapped results manually.

3. Many Results Does Not Mean All Results Are Usable

POI Search is suitable for quick collection, but it cannot replace formal verification. For deliverables that need database entry, mapping, or statistics, check at least the following:

• Whether the name is accurate
• Whether the location is offset
• Whether the category matches the thematic requirement
• Whether same-name points are duplicated

12. Summary

POI Search in iXGIS is essentially an online place collection feature for GIS workflows. Based on the Tianditu place-name search service, it combines finding places, placing them on the map, and saving them as features into a relatively complete workflow.

In short, the workflow has three steps:

1. Choose a range.
2. Enter a keyword.
3. Review and save results.

If you only want to locate places quickly, it can do that directly. If you want to turn external place information into GIS point data in your project, it can also meet that need. This is where POI Search is most valuable in GIS software.

The Locate feature in iXGIS is essentially a coordinate positioning tool. It lets you enter longitude and latitude coordinates, or pick a location directly on the map, then pans the map to the target point and displays the address information for that point.

This address information is not entered manually. It is retrieved with reference to the Tianditu reverse geocoding service. Reverse geocoding means converting a longitude and latitude coordinate into a readable address description, such as a city, district, road, and house number, or a location near a road or a certain distance northeast of a POI.

Therefore, the Locate feature in iXGIS does two things:

• Moves the map to a specified coordinate.
• Converts the coordinate into a readable address to help users understand the current location.

1. When to Use Locate

Locate is most useful in these scenarios:

1. You know a longitude and latitude coordinate and want to jump to the exact position on the map.
2. You want to check the approximate address or nearby features for a coordinate point.
3. You are organizing field records, monitoring points, sampling points, or issue points and need to inspect their locations quickly.
4. You need coordinate checking, manual comparison, or map browsing in a project.

If your target is a clearly defined coordinate point, Locate is usually more direct than POI Search.

2. How to Open Locate

Click Locate in the iXGIS toolbar to open the Coordinate Locate window.

When it opens, the system uses the current map center as the initial reference position and automatically displays that position's coordinates and address information. This is useful because:

• You can immediately see the approximate address of the current map center.
• If you only need to adjust the current location slightly, you do not have to start from an empty form.
• You can use the current map center as the starting point for a new locate operation.

3. Supported Coordinate Input Methods

Locate currently supports two input formats:

• Decimal degrees
• Degrees, minutes, and seconds

1. Decimal Degrees

This is the most common input method. Enter longitude and latitude as decimal degree values, for example:

• Longitude: 116.37304
• Latitude: 39.92594

This format is suitable for:

• Coordinates copied directly from GPS devices, web maps, or third-party platforms
• Manual checks of standard longitude and latitude values in batch records
• Daily positioning and quick comparison

2. Degrees, Minutes, and Seconds

If your source material uses traditional degrees, minutes, and seconds, you can switch to that input mode, for example:

• 116° 22′ 22.94″
• 39° 55′ 33.38″

This format is common in:

• Field record sheets
• Paper survey materials
• Traditional surveying or investigation results

In iXGIS, these two formats can be converted to each other. You do not need to convert them manually; just choose the correct input method.

4. Pick a Point on the Map Instead of Typing

In addition to the input boxes, the Locate window provides a map pick button. After enabling it, you can click a position on the map and fill that point's coordinates back into the Locate window.

This is useful when:

• You want to roughly choose a position on the map first, then view its precise coordinates.
• You need to check the center point of a known feature or an intersection.
• You want to obtain coordinates through map interaction instead of manual entry.

Note that clicking a point on the map mainly fills in the coordinates. If you want the map to formally jump to the point and refresh the address information, click the Locate button after picking the point.

5. What Happens After You Click Locate

After you finish entering coordinates and click Locate, the system performs these actions:

1. Converts the entered longitude and latitude to the position required by the current map display.
2. Places a locate marker at the target position.
3. Pans the map view to that position.
4. Queries the corresponding address information and displays it next to the marker.

This means Locate does more than move the map. It also tells you roughly where the point is.

From a user experience perspective, this matters for GIS users because a pair of longitude and latitude values is not intuitive on its own. A combination of coordinates and an address description is easier to judge and communicate.

6. Where the Address Information Comes From

The address description in Locate uses the Tianditu reverse geocoding service.

Reverse geocoding can be understood as looking up an address from coordinates. The input is longitude and latitude, and the output is structured address information. According to the Tianditu reverse geocoding service, returned content usually includes:

• Detailed address
• City or district/county
• Nearest road
• Nearest POI
• Direction and distance relative to nearby roads or POIs

In the current Locate feature in iXGIS, users most directly see the address name corresponding to the locate point. This address usually prefers a more complete address description; if the complete address is not clear enough, a shorter location description may be shown.

This feature can therefore tell you where the point is and help you decide whether it is the position you intended to find.

7. How to Interpret Locate Results

When using locate results, treat them as "coordinate position plus address reference", not as an absolutely unique official street address.

This is because reverse geocoding is essentially semantic matching based on the environment around a coordinate. It may return:

• The nearest road name instead of a specific building name
• A relative position near a POI
• Slightly different address wording in the same area because datasets are updated at different times

For GIS work, a better way to use the result is:

• Use the coordinate position as the primary reference.
• Use the address text as supporting context.
• If an exact street address is required in a formal deliverable, verify it manually.

8. Common Uses

1. Check Field Points

When you receive field sampling points, monitoring points, or inspection point coordinates, you can enter or paste them one by one to quickly check whether they fall in the correct area.

2. Verify Issue Locations

For complaint points, disaster points, anomaly points, or facility issue points with known longitude and latitude, you can locate them first, inspect their surroundings, then judge the situation with basemaps and thematic layers.

3. Support Communication and Reporting

Many non-GIS users are not sensitive to longitude and latitude, but they understand addresses. Displaying the address through Locate makes communication with business users, field staff, or managers easier.

4. Start Further Editing or Analysis

After the map is positioned at the target point, you can continue zooming, take screenshots, measure, overlay thematic layers, or perform further spatial judgment.

9. Things to Watch For

1. The Input Is Longitude and Latitude, Not Projected Coordinates

The values in the Locate window are longitude and latitude in degrees, not projected planar coordinates in meters. Do not paste X and Y values from a projected coordinate system directly into the window.

If your data is in projected coordinates, convert it to longitude and latitude before using Locate.

2. Check Signs for Coordinates Outside China

Within China, most longitude and latitude values are positive. For west longitude or south latitude, pay special attention to the positive and negative signs.

3. Address Information Is Mainly for Supporting Judgment

Reverse geocoding returns an address description. It is not the same as a legally confirmed address. For formal reporting, database entry, or fine-grained management, manual verification is still recommended.

4. Map Picking Is Better for Rough Positioning

If you only click a position on the map with the mouse, accuracy is affected by the current scale and click position. For high-precision coordinate checks, enter accurate coordinate values directly.

10. Recommended Workflow

For daily work, use Locate in this order:

1. Open the project and basemap.
2. Click Locate and check the current map center.
3. Choose Decimal Degrees or Degrees, Minutes, and Seconds.
4. Enter coordinates, or pick a point on the map first.
5. Click Locate.
6. Use the map marker and address information to judge whether the location is correct.
7. If needed, continue zooming, take screenshots, or perform further analysis.

11. Summary

The Locate feature in iXGIS is a coordinate positioning tool designed for practical GIS work. It combines coordinate input, map navigation, and address display so users can place a point on the map quickly and understand its approximate spatial meaning through reverse geocoding results.

In short, its core value is:

• Use coordinates to find a location accurately.
• Use addresses to help read the location.

For coordinate checks, field point organization, issue point review, and map browsing, this is a very useful basic feature.

Hillshade is a common terrain visualization technique in geographic information systems (GIS). It uses a digital elevation model (DEM) to simulate the light and shadow produced by sunlight over terrain, making elevation changes easier to see.

Core Principle

Hillshade applies a simplified lighting model to a DEM. For each pixel, the system estimates the local surface orientation from surrounding elevation changes, determines whether the surface faces toward or away from the light source, and then calculates brightness.

The calculation can be summarized as:

DEM (elevation raster)
-> calculate local slope and aspect
-> compute surface normal N
-> compute light vector L from sun parameters
-> calculate brightness I
-> convert to grayscale Gray

The core brightness formula is:

For an 8-bit grayscale output:

Formula Terms

• $N$: the unit normal vector of the terrain surface, representing slope orientation.
• $L$: the unit light-direction vector, representing the incoming sunlight direction.
• $N \cdot L$: the dot product, or cosine of the angle between the surface and the light; larger values mean the slope faces the light more directly.
• $\max(0, N \cdot L)$: back-facing slopes may produce negative values, which are usually clipped to 0 to indicate no direct light.
• LightIntensity: direct-light intensity, used to control highlight contrast.
• AmbientIntensity: ambient-light intensity, used to preserve detail in shadowed areas and prevent them from becoming completely black.
• $I$: normalized brightness, usually in the range $[0,1]$.
• $Gray$: final display grayscale value, usually in the range $[0,255]$.

In simple terms:

• Direct light determines where terrain is bright and how bright it is.
• Ambient light determines whether details remain visible in shadow.
• Together they form the final hillshade effect.

Parameter Overview

The parameters can be grouped into three categories:

1. Where the light comes from: LightDirection, ZenithAngle
2. How strong the light and shadow are: LightIntensity, AmbientIntensity
3. How visible the terrain relief appears: ElevationFactor, Extrusion

Light Direction: Azimuth and Altitude

These two parameters define the light vector $L$:

Where:

• $A_z$: solar azimuth
• $Z$: solar altitude

In the parameter settings:

• LightDirection (Azimuth): the solar azimuth, indicating the direction from which light comes. For example, 315° means light from the northwest.
• ZenithAngle (Altitude): this parameter actually represents solar altitude. Lower values produce longer shadows; higher values produce shorter shadows.

Visual effects:

• Changing LightDirection mainly changes shadow direction.
• Changing ZenithAngle mainly changes shadow length and perceived relief strength.

Brightness: Ambient and Direct Light

These parameters control the relationship between highlights and shadows:

• LightIntensity: direct-light intensity. Higher values make light-facing slopes brighter and increase contrast.
• AmbientIntensity: ambient-light intensity. Higher values soften shadows and preserve shadow detail.

Adjustment guidance:

• To emphasize relief, increase LightIntensity and moderately lower AmbientIntensity.
• To preserve shadow detail, increase AmbientIntensity.

Geometric Scale: Elevation-to-Planar Conversion

ElevationFactor handles differences between elevation units and horizontal coordinate units.

Examples:

• If both horizontal coordinates and elevation are in meters, it is usually set to 1.
• If horizontal coordinates are longitude/latitude degrees and elevation is in meters, a conversion factor is required; otherwise slope calculations will be distorted.

This parameter controls whether the calculated slope orientation is geometrically realistic. If it is set incorrectly, terrain may look flattened or exaggerated.

Visual Exaggeration: Extrusion

Extrusion is a visual terrain-exaggeration factor used to strengthen relief expression.

In the calculation, it can be understood as:

The two parameters have different roles:

• ElevationFactor handles unit conversion.
• Extrusion handles visual exaggeration.

Therefore:

• ElevationFactor determines whether the calculation is realistic.
• Extrusion determines whether the visual relief is strong enough.

As a rule of thumb:

• For small-scale maps or coarse-resolution DEMs, increase Extrusion moderately, for example to 5-8, to enhance relief.
• For high-resolution DEMs such as 10 m data, 1-2 is usually enough.

Parameter Summary

Four Preset Effects

For easier use, iXGIS provides four preset modes.

Configure Hillshade

When using classified coloring for raster data, hillshade can be enabled. The global land-and-sea terrain raster is used here as an example.

The four effects are, in order: Morning Light, Noon, Sunset, and Night Light.

Concepts

• DEM (Digital Elevation Model): a general raster elevation model; depending on context, it may or may not distinguish terrain from surface objects.
• DTM (Digital Terrain Model): bare-earth elevation, with tree canopy and buildings removed.
• DSM (Digital Surface Model): surface elevation, including the highest surfaces such as tree canopy and buildings.

Data forms and formats

• Raster: the most common form, including GeoTIFF, Cloud-Optimized GeoTIFF, and BIL; pixel value = elevation.
• Point cloud: LAS/LAZ data from LiDAR or photogrammetry; can be used to derive DSM/DTM.
• Contours / TIN: vector contours or triangulated irregular networks for cartography or local modeling.

Typical sources

• Satellite / radar: SRTM, NASADEM, ASTER GDEM, ALOS AW3D, Copernicus DEM (GLO-30/90), and partly public TanDEM-X.
• Aerial survey / UAV photogrammetry (SfM): generates dense point clouds and DSM/DTM.
• Airborne / terrestrial LiDAR: high accuracy and high resolution, commonly used in urban and engineering applications.
• Bathymetry: GEBCO, EMODnet, and national hydrographic surveys; often merged with land DEMs into integrated land-and-sea terrain products such as ETOPO.

China Terrain Data

Introduction

A DEM represents ground relief using elevation values at regular grid points.

DEM data can be used to extract many terrain and geomorphology derivatives, including slope, aspect, drainage networks, 3D models, visibility analysis, and mobile-phone base-station analysis. DEMs are also used for quantitative analysis of terrain characteristics and to support planning and construction.

DEM product introduction from the National Geographic Information Resource Catalog Service System

The source lists typical DEM-related uses:

• Slope: Slope describes the steepness of a surface unit. Slope values can be used to classify landforms, such as plains, hills, and mountains.
• Aspect: Aspect is the direction of the projection of a slope normal on the horizontal plane; more simply, it is the downslope direction. Aspect can support analysis of shaded and sunny slopes, crop and vegetation light preferences, crop zoning, and selecting suitable slopes for returning farmland to forest.
• Site-selection planning analysis: By combining slope, aspect, and basic geographic information layers, overlay analysis can be applied in different industries. For example, urban construction usually needs flatter terrain.
• Hydrological analysis: DEMs can be used with fill-and-cut calculations to extract river networks and delineate watersheds.
• Viewshed analysis: DEM-based viewshed analysis divides the landscape into visible and invisible areas because terrain can block line of sight.

Main data products:

• 1:1,000,000 scale, corresponding to 400 m grid spacing.
• 1:250,000 scale, corresponding to 100 m grid spacing.
• 1:50,000 scale, corresponding to 25 m and 10 m grid spacing.

Download

After opening the page above, the right side lists digital elevation model data at different map scales. At present, however, the data is temporarily unavailable for download.

Coordinate System and Vertical Datum

This terrain data uses the China Geodetic Coordinate System 2000 and the 1985 National Height Datum.

The Yellow Sea height system and the 1985 National Height Datum: In 1956, China defined the long-term mean sea level of the Yellow Sea at Qingdao as the unified datum, called the 1956 Yellow Sea Height System. It was China's first national height system and ended the previous coexistence of many height systems. Because the Qingdao tide-gauge data series used for that datum (1950-1956) was short, China's surveying and mapping authority recalculated the Yellow Sea mean sea level using Qingdao tide observations from 1952 to 1979. This became the 1985 National Height Datum. Precise leveling was used at the national leveling origin in Qingdao, establishing the relationship: 1985 National Height Datum elevation = 1956 Yellow Sea elevation - 0.029 m. The 1985 datum was adopted in May 1987, and the 1956 Yellow Sea system was abolished. The 1956 leveling origin elevation was 72.289 m, and the 1985 leveling origin elevation is 72.260 m. Conventionally, the new datum is said to be 0.029 m lower than the old one. Source: National height system description

The 1985 National Height Datum is China's statutory national unified height system, based on the mean sea surface of the Yellow Sea and used as the standard for measuring ground elevation nationwide. Key points:

1. Definition and concepts

• Datum surface: the Yellow Sea mean sea level calculated from 28 years of Qingdao tide-gauge data from 1952 to 1979.
• Leveling origin: the national leveling origin of the People's Republic of China at Guanxiang Mountain, Qingdao, with an elevation of 72.260 m above the 1985 datum.

2. Technical principle

• Data improvement: compared with the 1956 Yellow Sea system, which used only 1950-1956 data, the 1985 datum uses a longer tide record and sliding-average calculations to reduce short-term sea-level fluctuation error.
• Conversion: 1985 datum elevation = 1956 Yellow Sea elevation - 0.029 m. For example, a point at 100 m in the 1956 system is 99.971 m in the 1985 system.

3. History

• Replacement of the old system: the 1985 datum was officially adopted in May 1987 with State Council approval, replacing the 1956 Yellow Sea Height System.
• Naming significance: although still based on the Yellow Sea, the name emphasizes national authority and avoids confusion with local height systems.

4. Applications

• Engineering and surveying: widely used in topographic mapping, construction projects such as bridges and dams, airport obstacle-control surfaces, and related fields.
• Relationship to other systems: Wusong elevation = 1956 Yellow Sea elevation + 1.688 m; Pearl River elevation = 1956 Yellow Sea elevation - 0.586 m. Unified conversions are required for compatibility.

5. Current status

As China's only statutory national height datum, it must be used in national surveying projects, map production, and land planning. Its parameters are governed by Specifications for the basic techniques of national geodesy (GB 22021-2008).

Global Terrain Datasets

Global terrain data digitally describes elevation for the whole Earth, either land-only or integrated land-and-sea. It is usually released as regular grids (raster DEM/DSM/DTM) or vector contour surfaces, representing the elevation or water depth at a location and supporting derivatives such as slope, aspect, viewshed, and hillshade.

Note: terrain data should be distinguished from land cover data.

Main data sources:

Available Datasets

* DSM includes vegetation and buildings. DTM / bare-earth removes trees and buildings and is more suitable for hydrology, flood, and drainage-network simulation.

Selection and Usage Tips

1. First choice at 30 m: Copernicus GLO-30 currently has strong overall accuracy and consistency with a low download threshold. If bare-earth terrain is needed, use FABDEM, which removes building and canopy errors based on GLO-30. ALOS AW3D30 is a good alternative, especially where it complements ASTER in cloudy or snowy areas. IC2-GDEM improves high-latitude mountains but is still mainly a research release.
2. 90 m products: Copernicus GLO-90 and TanDEM-X 90 m are generally better than traditional SRTM in high-latitude and desert areas. MERIT DEM and EarthEnv-DEM90 include systematic error correction and are useful for large-basin hydrology or ecological modeling.
3. Integrated land-and-sea terrain: If analysis includes seafloor terrain, such as global visualization or sea-level-rise scenarios, use GEBCO 15″ global grids for consistent land-and-sea coverage.
4. Polar or high-precision needs: Polar regions can use TanDEM-X 12 m commercially or regional ArcticDEM/REMA 2 m. For ice-sheet thickness and bedrock, use BedMachine datasets for Greenland or Antarctica.
5. Data fusion and error control: Different DEMs may use different vertical references, such as ellipsoid heights versus EGM96 geoid heights, different acquisition periods, and DSM/DTM definitions. Before mosaicking datasets, unify the vertical datum and apply smoothing or bias correction. For large-scale hydrological or flood simulation, use hydrologically corrected products such as MERIT Hydro or FABDEM to reduce false drainage breaks and artificial depressions.

GEBCO Global Land-and-Sea Gridded Terrain Model

What Is GEBCO?

GEBCO (General Bathymetric Chart of the Oceans) is a century-scale project jointly led by the International Hydrographic Organization (IHO) and the Intergovernmental Oceanographic Commission (IOC) of UNESCO. Its goal is to complete high-precision global seafloor mapping by 2030 through the Seabed 2030 initiative. GEBCO's core product is an annually updated global land-and-sea gridded terrain model (GEBCO Grid), released free to the public for research, engineering, mapping, education, and other uses. GEBCO

Latest Versions

Data Components

• Elevation Grid: values are in meters; positive values represent land elevation and negative values represent water depth.
• TID Grid: each raster cell has a Type Identifier showing the data source, such as multibeam, single-beam, satellite-guided reconstruction, or interpolation. GEBCO
• Sub-ice versions: separate grids are available for topography under the Greenland and Antarctic ice sheets.

Download and Format

GeoTIFF is usually recommended. Use the GEBCO_2024 Grid download address.

License and Citation

• Free: available for commercial and academic use.

• Requirement: cite "© GEBCO Year" and include the DOI, such as GEBCO_2025 10.5285/37c52e96-24ea-67ce-e063-7086abc05f29. British Oceanographic Data Centre

• Disclaimer: GEBCO is not responsible for losses caused by data use.

• Detailed documentation for each year is available through the DOI links.

  • GEBCO_2024 Grid
  • GEBCO_2023 Grid
  • GEBCO_2022 Grid
  • GEBCO_2021 Grid
  • GEBCO_2020 Grid
  • GEBCO 2019 Grid

Using the Data

After downloading GeoTIFF data, upload it to the cloud resource manager, or directly use the public-space precached data.

For this dataset, use standard global terrain coloring.

In iXGIS, you can use stretch coloring or classified coloring. Hillshade can also be enabled with classified coloring.

Simplified GEBCO Template

A simplified global land-and-sea terrain map can be made by reducing the original data by a factor of 10.

1. Load the simplified GEBCO global land-and-sea raster terrain model from the public space. The resolution is 45'.
2. Configure symbols and styles. You can use the global land-and-sea terrain colors listed above, with either stretch or classified symbols. The example uses stretch colors. If the project is created from the Global Terrain GEBCO template, the existing symbol settings can be reused.
3. Load the simplified global country distribution layer.
4. Enter map layout and add a north arrow, scale bar, and legend.
5. Select the map frame and set the map scale to 1:150,000,000 in map properties.

ASTER GDEM v3

On June 30, 2009, NASA and Japan's Ministry of Economy, Trade and Industry (METI) jointly released the ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model), produced from detailed observations by NASA's Terra satellite. This global DEM uses 1.3 million stereo images collected by ASTER. ASTER mapping covers all land areas between 83°N and 83°S, about 99% of Earth's land surface. Before this, the most complete terrain data came from NASA's Shuttle Radar Topography Mission (SRTM), which mapped about 80% of Earth's land between 60°N and 57°S.

At one time, SRTM 90 m terrain data could be downloaded for free from the International Scientific Data Service Platform of the Computer Network Information Center, Chinese Academy of Sciences (http://srtm.datamirror.csdb.cn/). It is no longer available there.

Production Workflow

ASTER GDEM was produced by fully automated processing of 1.5 million archived ASTER scenes. This included 1,264,118 scene-based ASTER DEMs generated through stereo correlation, followed by cloud removal, removal of remaining anomalies, averaging to produce final pixel values, correction of remaining abnormal data, and tiling into 1° x 1° global ASTER GDEM files.

Data Access

ASTER GDEM data is currently available online for free. Users can download it from Japan's ERSDAC (Earth Remote Sensing Data Analysis Center) or NASA's LP DAAC (Land Processes Distributed Active Archive Center). Registration and data-request permissions are required before download.

Access from iXGIS

iXGIS has downloaded the data from the sources above and cached it in object storage. Users can download it directly from the Data Download tool in the toolbox. The download format is GeoTIFF.

iXGIS automatically merges and clips the data according to the specified extent.

Global data download is supported.

NASADEM / SRTM V3

NASADEM (NASA Digital Elevation Model) is a high-precision global digital elevation product developed by NASA from SRTM V3 (Shuttle Radar Topography Mission, Version 3) data. SRTM was acquired in 2000 by radar interferometry aboard the Space Shuttle Endeavour, covering most land areas between 60°N and 56°S, or more than 80% of global land.

NASADEM comprehensively reprocesses and enhances SRTM by combining multiple data sources, such as ASTER GDEM, ICESat, NED/3DEP, and GTOPO30, with improved processing algorithms to improve accuracy, completeness, and consistency.

Data Characteristics

• Coverage: global 60°N-56°S, covering most inhabited land areas.
• Spatial resolution: 1 arc-second (~30 m) global coverage for SRTM1; 3 arc-second (~90 m) global coverage for SRTM3 and some historical versions.
• Vertical accuracy: absolute vertical accuracy is about < 10 m, and relative accuracy is better than 5 m. NASADEM reprocessing further reduces errors.
• Projection and format: WGS84 ellipsoid, geographic coordinates EPSG:4326, GeoTIFF tiles of 1° x 1°.
• Processing improvements over SRTM V2 and CGIAR-CSI SRTM: NASA JPL's new algorithm fills voids in the original data; multiple elevation sources such as ASTER GDEM, NED/3DEP, and ICESat are fused for correction; global seamless coverage is improved; and outputs are better suited for integration with other NASA/USGS datasets such as Landsat and MODIS.

Access

• NASA Earthdata (LP DAAC): https://lpdaac.usgs.gov/
• USGS EarthExplorer: https://earthexplorer.usgs.gov/

AW3D30-DSM

AW3D30 is a global digital surface model (DSM) produced by the Japan Aerospace Exploration Agency (JAXA) from imagery acquired by PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) aboard the Advanced Land Observing Satellite ALOS, also called DAICHI.

• Spatial resolution: about 30 m (1 arc-second grid).
• Coverage: global land areas; exact coverage depends on product version.
• License: the dataset can be used free of charge for commercial and non-commercial purposes, subject to the applicable terms of use.

Access

• Register on JAXA's EORC official website and download tile packages, usually provided as *.tar.gz files.
• Access and analyze it in Google Earth Engine, for example using ImageCollection "JAXA/ALOS/AW3D30/V4_1" and the DSM band for visualization or slope calculation.
• Data download
• After email registration, tiled files can be downloaded.

File Organization

AW3D30 data is organized as 1° x 1° longitude/latitude tiles. Main files include:

• DSM file: surface height (elevation), stored as 16-bit signed values.
• MSK file: 8-bit mask marking cloud, snow, seawater, low-quality areas, and similar conditions.
• STK file: indicates the number of scene stacks used to generate the DSM.
• QAI file: quality-assurance information describing product properties and data quality.
• HDR file: metadata file containing projection, data format, and related information.

Access from iXGIS

iXGIS has downloaded the data from the source above and cached it in object storage. Users can download it directly from the Data Download tool in the toolbox. The download format is GeoTIFF.

iXGIS automatically merges and clips the data according to the specified extent.

Global data download is supported.

Note: At the global scale, direct observation of human land use is difficult. Common global "land use" datasets are usually based on remotely sensed land cover maps, with classes that also imply use. For example, "cropland" and "built-up" are both cover types and land-use indicators. The datasets below are therefore mainly global land cover / land use classification products.

Global Land Use / Land Cover Datasets

The following sections introduce several mainstream global land use / land cover datasets, including their characteristics, formats, accuracy, and access methods.

ESA WorldCover 10 m

Overview: WorldCover is a high-resolution global land cover product released by the European Space Agency (ESA). It currently provides annual global maps for 2020 and 2021 at 10 m resolution, produced from Sentinel-1 radar and Sentinel-2 optical imagery. The classification system contains 11 land cover classes: tree cover, shrubland, grassland, cropland, built-up, bare/sparse vegetation, snow and ice, permanent water, herbaceous wetland, mangroves, and moss and lichen. Independent validation reported an overall accuracy of about 74.4% for 2020 and about 76.7% for 2021, meeting the target accuracy level above 75%.

Spatial and temporal resolution: 10 m spatial resolution. Each product is a single-year classification map; currently 2020 and 2021 are available. The target is annual updates, with each product completed about 10 months after the last image acquisition of the year.

Access and license: WorldCover is released under CC BY 4.0. It is free to use without restriction, but attribution is required. Commercial advertising use requires ESA project approval.

Format and Download

1. WorldCover is provided in a longitude/latitude grid projection (WGS84) and split into 3° x 3° Cloud Optimized GeoTIFF (COG) tiles. There are 2,651 global tiles with a total size of about 117 GB. Users can download 7zip-compressed GeoTIFF tiles from the official interactive map, or access integrated versions through Zenodo and Google Earth Engine. The data DOI is referenced on [worldcover2020.esa.int](https://worldcover2020.esa.int/download#:~:text=To cite these maps as,5571936).

Note: the website mentioned below is no longer accessible. If original data is needed, download it from Google Earth Engine.

2. The ESA official 2020 data download page provided 60° x 60° macrotiles:

https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_N30W180.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S30W180.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S90W180.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_N30W120.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S30W120.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S90W120.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_N30W060.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S30W060.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S90W060.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_N30E000.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S30E000.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S90E000.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_N30E060.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S30E060.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S90E060.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_N30E120.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S30E120.zip
https://worldcover2020.esa.int/data/archive/ESA_WorldCover_10m_2020_v100_60deg_macrotile_S90E120.zip

2021 data:

https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_N30W180.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S30W180.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S90W180.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_N30W120.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S30W120.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S90W120.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_N30W060.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S30W060.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S90W060.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_N30E000.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S30E000.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S90E000.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_N30E060.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S30E060.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S90E060.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_N30E120.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S30E120.zip
https://worldcover2021.esa.int/data/archive/ESA_WorldCover_10m_2021_v200_60deg_macrotile_S90E120.zip

The north-south identifiers are N30, S30, and S90; the west-east identifiers are W180, W120, W060, E000, E060, and E120. URLs can be assembled from these identifiers and downloaded in a browser.

File Name Definition

Example file name: ESA_WorldCover_10m_2020_v100_N66E063_Map.tif.

• ESA_WorldCover_10m: ESA WorldCover global 10 m land cover dataset.
• 2020_v100: year 2020, v100 = Version 100, the first official 2020 release.
• 2021_v200: year 2021, v200 = Version 200.
• N66E063: the 3° x 3° tile ID.
• N66: latitude 66°N to 69°N.
• E063: longitude 63°E to 66°E.

Classes and Code Values

ESA WorldCover uses a simplified version of the FAO LCCS (Land Cover Classification System) and stores land cover classes as integer codes. The 2020 and 2021 classifications are consistent and contain 11 classes.

Notes:

• Code values are integer pixel values in the GeoTIFF and can be used directly for classified rendering.
• Built-up (50) includes urban areas, roads, mining and industrial areas, and other artificial impervious surfaces.
• Bare/sparse vegetation (60) is common in deserts, Gobi areas, and sparsely vegetated regions.
• Mangroves (95) are separated from general wetlands (90), mainly along tropical and subtropical coasts.
• Moss and lichen (100) are mainly distributed in polar and high-latitude tundra regions.

Download from iXGIS

iXGIS has downloaded the data from the sources above and cached it in object storage. Users can download it directly from the Data Download tool in the toolbox. The download format is GeoTIFF.

iXGIS automatically merges and clips the data according to the specified extent.

Global data download is supported.

CGLC 100 m

Overview: This 100 m global land cover dataset is provided by the Copernicus Global Land Service (CGLS) and produced from VITO PROBA-V satellite imagery. It is available as annual dynamic products from 2015 to 2019. The classification is relatively detailed and includes major land surface types such as forests, shrubs, herbaceous vegetation, cropland, urban built-up areas, bare areas, snow and ice, permanent and seasonal water, wetlands, and proportional layers for tree cover, grass cover, shrub cover, and bare ground. It emphasizes multi-year consistency for monitoring global land cover change since 2015. Independent validation reports an overall classification accuracy of about 80%; the 2019 product has a thematic accuracy of 80.3%. The project target is to further improve accuracy above 85%.

Spatial and temporal resolution: 100 m spatial resolution in a WGS84 longitude/latitude grid. The period is 2015-2019, with one global map per year. It is an annual product; the latest product is 2019 because PROBA-V stopped operating in 2020, and later updates may use other data sources.

Format and Download

The official product is available as GeoTIFF. Annual global data can be downloaded by region or obtained through an API. Users can download clipped regions from the Copernicus Land portal or use the auxiliary download API to obtain the full original GeoTIFF dataset. The data is open and free, but a Copernicus account is required. OGC Web Services such as WMS are also supported. The dataset follows the EU Copernicus open data policy: it is free to use without restriction, but citation of the data source and DOI is required.

Download page: Land Cover 2019 (raster 100 m), global, yearly - version 3 - Copernicus Land Monitoring Service

Classes and Code Values

Copernicus Global Land Cover 100 m (CGLS-LC100) uses the FAO LCCS and provides 23 global land cover classes. The main code values are:

MODIS Global Land Cover (MCD12Q1)

Overview: NASA provides this global 500 m annual land cover product from MODIS sensors on Terra and Aqua. The product ID is MCD12Q1 (Collection 6.1), covering each year from 2001 to recent years, around 2020 and later. It includes several classification schemes. The primary classification uses the 17-class IGBP scheme, while UMD, LAI/FPAR, NPP biome, and Plant Functional Type (PFT) layers are also provided, along with quality-control layers. MODIS land cover has moderate accuracy; global average overall accuracy for the IGBP primary classification is about 75% in validation studies. It is a hard classification with one class per pixel and also includes confidence information.

The MODIS Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid product provides five land cover classification schemes derived using supervised decision-tree classification:

• Land Cover Type 1: IGBP global vegetation classification scheme.
• Land Cover Type 2: University of Maryland (UMD) scheme.
• Land Cover Type 3: MODIS-derived LAI/fPAR scheme.
• Land Cover Type 4: MODIS-derived Net Primary Production (NPP) scheme.
• Land Cover Type 5: Plant Functional Type (PFT) scheme.

Additional layers include land cover type assessment SDS, land cover percentage SDS, and land cover quality-control SDS.

Spatial and temporal resolution: about 500 m spatial resolution. The product is annual, with one classification for each calendar year from 2001 onward. Collection 6.1 has been extended to 2020 or later, and some derived sources extend to 2022.

Format and access: NASA's original global products are released as HDF-EOS files in Sinusoidal grid tiles. Each year is divided into 36 x 18 tiles and can be downloaded from NASA Earthdata platforms such as LP DAAC or LAADS. Although the official format is not TIFF, derived providers have mosaicked and reprojected annual data into GeoTIFF. OpenGeoHub, for example, provides 2001-2022 global 500 m land cover time series as Cloud Optimized GeoTIFF mosaics. NASA data is in the public domain and free to use, but users should cite NASA/LP DAAC.

Accuracy and limitations: MODIS land cover is coarse and has limited ability to represent heterogeneous surfaces and small patches. Overall accuracy is commonly around 70%, with about 75% for the IGBP scheme. Accuracy varies by class; forests and grasslands are usually better, while urban and mixed classes have more errors. High-precision or fine-scale applications may require refinement or combination with higher-resolution data.

Download

iXGIS does not currently support direct display of HDF data, so GeoTIFF data is recommended. The CERN Data Centre provides GeoTIFF data derived from MCD12Q1 v061, covering 2001 to 2022.

Download page: https://zenodo.org/records/8367523. On the Zenodo page, the DOI is the dataset identifier. Search Zenodo for "MODIS MCD12Q1 Land Cover and Land Use Time Series Global Mosaics 2001-2022" if needed.

The page usually lists files such as:

lc_mcd12q1v061.t1_c_500m_s_20010101_20011231_go_epsg.4326_v20230818.tif
lc_mcd12q1v061.t1_c_500m_s_20020101_20021231_go_epsg.4326_v20230818.tif
...

Click Download all to download all files, or use the individual Download links for specific years.

File name fields:

Other layer identifiers:

• Original classifications t1 / t2 / t5: t1 = IGBP 17-class scheme, t2 = UMD scheme, t5 = Plant Functional Type (PFT).
• Biome property classifications p1 / p2: p1 = LAI/FPAR biome, p2 = NPP biome.
• Reliability layers p1a / p2a: confidence or accuracy layers for p1 and p2.
• p2c: Property 2 Continuous, continuous NPP biome-related variables such as proportions or coverage.
• qc: Quality Control layer indicating classification reliability.

IGBP Classes and Code Values

UMD Classes and Code Values

Land Cover Type 5: PFT Classes and Code Values

FROM-GLC (Tsinghua University)

Overview: FROM-GLC (Finer Resolution Observation and Monitoring of Global Land Cover) was developed by Professor Gong Peng's team at Tsinghua University and is an important Chinese contribution to global land cover mapping. In 2014, the team released the first global 30 m land cover map based on Landsat data around 2010, using a detailed 28-class system. Because of the rich class system, the first 30 m map had a relatively low overall accuracy of about 52.8%. The team later improved algorithms using a 2015 global sample library and transferred 30 m samples to higher-resolution mapping, producing the first global 10 m land cover map from 2017 Sentinel-2 imagery. The FROM-GLC10 (2017) product uses 10 major land cover classes similar to GlobeLand30, improving accuracy through a simpler classification. Validation reported an overall accuracy of 72.76%.

Spatial and temporal resolution: The FROM-GLC series includes multiple products: 2010 30 m (FROM-GLC30) and 2017 10 m (FROM-GLC10). The coverage is global land. Updates are not regular; these are research-stage products for specific time points. Recent work also includes 30 m five-year interval land cover evolution data for 1985-2020 (GLC_FCS30D) and the 2015 fine-class 30 m product (GLC_FCS30-2015), but these may still be academic releases.

Format and access: FROM-GLC data is provided by the Tsinghua Earth System Science data platform as block-based GeoTIFF downloads. Users must register and log in to the Tsinghua ESS data platform (data.ess.tsinghua.edu.cn) to download GeoTIFF files by region. The series is free for global users, with attribution required in publications and results. For potential commercial use, consult the data team.

Update: the Tsinghua data platform can no longer download the data. A Nebula Data service platform provides downloads, but it also appears unavailable.

FAO Global Land Cover SHARE

Overview: GLC-SHARE is a global land cover database released by the Food and Agriculture Organization of the United Nations (FAO) in 2014. It uses a spatial resolution of about 1 km (30 arc-seconds), combining each country's "best available" land cover data with global satellite-image classification results. GLC-SHARE provides 11 thematic layers, each representing the percentage (0-100%) of one major land cover type in each 1 km cell. The classes include artificial surfaces, cropland, grassland, tree-covered areas, shrub-covered areas, natural wetlands, mangroves, sparse vegetation, bare soil, snow and glaciers, and water bodies. This layered method preserves details from national datasets as much as possible and improves global reliability. The temporal range is marked as 1998-2012, representing land cover conditions around the millennium. GLC-SHARE has not been updated annually and remains a version 1.0 beta baseline product.

Spatial and temporal resolution: about 1 square kilometer (30 arc-second longitude/latitude grid). It covers global land and is static, representing average conditions around 2000 rather than later changes.

Detailed documentation: glc-share-doc.pdf

Land Cover Classification System (LCCS): Classification Concepts and User Manual

Format and access: FAO provides GLC-SHARE downloads and map services through GeoNetwork. Data is provided as GeoTIFF raster files, with one GeoTIFF per layer or a combined multiband GeoTIFF, in WGS84. OGC WMS services are also available. The data is free for education, research, personal, and other non-commercial use without prior permission, but FAO and the dataset name must be cited. Commercial resale or similar use requires FAO's written permission.

References:

• ESA, ESA WorldCover 2020 - Info (updated 2025): [worldcover2020.esa.int](https://worldcover2020.esa.int/#:~:text=,overall accuracy), [validation report](https://worldcover2020.esa.int/#:~:text=The ESA WorldCover product has,see the Product Validation Report)
• ESA, WorldCover 2020 Download Instructions (2025): [worldcover2020.esa.int](https://worldcover2020.esa.int/download#:~:text=The ESA WorldCover 10 m,There are 2651 tiles), [terms of use](https://worldcover2020.esa.int/download#:~:text=Terms of use)
• ESA, ESA WorldCover 2021 - Info (updated 2025): [worldcover2021.esa.int](https://worldcover2021.esa.int/#:~:text=,overall accuracy%2C independently validated), validation
• Copernicus Land, Global Land Cover 100m - Characteristics (2019): [land.copernicus.eu](https://land.copernicus.eu/en/products/global-dynamic-land-cover/copernicus-global-land-service-land-cover-100m-collection-3-epoch-2019-globe#:~:text=Spatial resolution%3A), [update frequency](https://land.copernicus.eu/en/products/global-dynamic-land-cover/copernicus-global-land-service-land-cover-100m-collection-3-epoch-2019-globe#:~:text=Update frequency%3A)
• Copernicus Land, 100m Land Cover 2019 - Thematic Accuracy: [land.copernicus.eu](https://land.copernicus.eu/en/products/global-dynamic-land-cover/copernicus-global-land-service-land-cover-100m-collection-3-epoch-2019-globe#:~:text=Thematic accuracy%3A)
• Copernicus Land, Download via API Instructions: [land.copernicus.eu](https://land.copernicus.eu/en/products/global-dynamic-land-cover/copernicus-global-land-service-land-cover-100m-collection-3-epoch-2019-globe#:~:text=Download full dataset)
• NASA LAADS, MODIS MCD12Q1 Product Information: [ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD12Q1/#:~:text=Instrument%3A MODIS Processing Level%3A Level,Number%3A PGE41 File Naming Convention)
• ResearchGate, Accuracy Assessment of MODIS Global Land Cover: researchgate.net
• Zenodo, MODIS 2001-2022 COG Data Description: [zenodo.org](https://zenodo.org/records/8367523#:~:text=The yearly land use and,qc)
• UN-SPIDER, GlobeLand30 Data Source Introduction: [un-spider.org](https://www.un-spider.org/links-and-resources/data-sources/land-cover-map-globeland-30-ngcc#:~:text=Requirements%3ARegistration necessary Data type%3Aland use%2C,2010)
• ESSD, GLC_FCS30-2015 Article Abstract: [essd.copernicus.org](https://essd.copernicus.org/articles/13/2753/2021/#:~:text=and a greater diversity of,paper are free access at), [methods](https://essd.copernicus.org/articles/13/2753/2021/#:~:text=similar image data along with,temporal differences among the Landsat)
• Tsinghua University, News on the First Global 10 m Land Cover Map: [tsinghua.edu.cn](https://www.tsinghua.edu.cn/en/info/1244/2976.htm#:~:text=This work proposed the theory,GLC30 product), [download note](https://www.tsinghua.edu.cn/en/info/1244/2976.htm#:~:text=other years or different sensors,GLC30 product)
• UN-SPIDER, GLC-SHARE (FAO) Data Source Information: [un-spider.org](https://www.un-spider.org/links-and-resources/data-sources/land-cover-map-glc-share-fao#:~:text=File type%3Atiff %2C 62,2012 Technical Specifications), [license](https://www.un-spider.org/links-and-resources/data-sources/land-cover-map-glc-share-fao#:~:text=Copyright%3A Exclusive right to the,means for resale or other)

China Land Use Data: GlobeLand30

GlobeLand30 is an important result of China's National High-Tech Research and Development Program (863 Program) project on global land cover remote sensing mapping and key technologies. It includes ten main land cover types: cultivated land, forest, grassland, shrubland, wetland, water bodies, tundra, artificial surfaces, bareland, and permanent snow and ice.

It was the world's first 30 m global land cover map and was first released in 2014 with independent intellectual property rights. GlobeLand30 currently provides global data for 2000, 2010, and 2020. Compared with earlier 1 km and 300 m products, GlobeLand30 is much more detailed and is widely considered one of the best-quality global land cover datasets. Independent assessments report overall accuracy of about 75-80% for the 2010 product, while the producer reports classification accuracy above 80%. The 2020 data was updated using improved methods and new imagery and is expected to maintain high accuracy.

Land cover and its changes are essential basic information for environmental change studies, geographic monitoring, and sustainable development planning. Existing global land cover datasets with resolutions from 1 km to 300 m cannot fully meet application needs. China's GlobeLand30 is the first global 30 m land cover dataset and provides richer global land cover spatial information, better representing many human land-use activities and the landscape patterns they form.

Comparison of global land cover products at different resolutions

The 2000 and 2010 GlobeLand30 datasets support monitoring and analysis of land cover change. They were produced by the same research team using consistent classification methods, which improves objectivity and comparability.

The information above is from Global Land Cover Data.

Download Process

The National Geographic Information Resource Catalog Service System provides GlobeLand30 datasets globally. After registration, the data can be downloaded for free.

Data Preview

The downloaded data is a compressed package such as N50_35_2010LC030.zip. After decompression, the files with the .tif extension are usually the ones needed.

Each tile package contains five parts: classification result files, coordinate information files, classification image map-index files, metadata files, and explanatory files.

You can upload all files to the cloud resource manager, or upload only n50_35_2010lc030.tif. The data information shows an unsigned integer pixel type and a 30 m resolution.

Download from iXGIS

iXGIS has downloaded the data from the source above and cached it in object storage. Users can download it directly from the Data Download tool in the toolbox. The download format is GeoTIFF.

iXGIS automatically merges and clips the data according to the specified extent.

China-region data download is supported.

Data Loading

GlobeLand30 also defines symbology. The code-to-color table is shown below.

Data Processing

Pixels with a value of 0 are invalid areas, but the data does not explicitly define a NoData value. It is best to set NoData=0 for better display. You can re-export the raster with the raster export tool. Open the toolbox and use Raster Tools > Raster Processing > Assign NoData to assign invalid values. Depending on the dataset, set NoData=0 or NoData=255.

• Natural features: coastlines, international boundaries, rivers, lakes, watersheds, marine regions, terrain-derived lines, and more
• Human features: national/provincial/municipal administrative boundaries, settlements/place names, transportation features such as roads, railways, routes, ports, and airports, protected areas, and more

Common formats/coordinate systems: Shapefile, GeoPackage, GeoJSON, GeoParquet, and similar formats. Most datasets are published in WGS84 (EPSG:4326). Scale/detail: detail is usually described by cartographic scale or minimum mapping unit (MMU), for example 1:110M, 1:50M, or 1:10M.

Common authoritative or widely used data sources at a glance

• Natural Earth (NE): global basemap data, including countries, coastlines, rivers, populated places, and more. It is available at 1:110M, 1:50M, and 1:10M, is clean and suitable for cartography, and is released under a public domain license.
• OpenStreetMap (OSM): crowdsourced global roads, buildings, points of interest, and more. It is detailed and updated quickly, but semantics depend on tags such as highway=*, and quality varies by region. Licensed under ODbL.
• GADM: global administrative boundaries down to county/township levels, with large differences by country and rich attributes. The license is relatively conservative, so review the terms carefully for commercial use.
• FAO GAUL / UN-aligned boundaries: administrative boundaries aligned with United Nations conventions, useful for statistics and governance scenarios. Downloads must comply with the usage statement.
• EEZ (Exclusive Economic Zones, VLIZ): global maritime jurisdiction and sea-area boundaries, commonly used in maritime, fisheries, and ocean planning work.
• HydroBASINS / HydroRIVERS (WWF): global hierarchical watersheds and river networks, convenient for hydrological and freshwater ecology analysis.
• GLWD (Global Lakes & Wetlands): global lake and wetland polygon features.
• WDPA / WD-OECM (Protected Planet databases): global protected-area boundaries and attributes, such as national parks and nature reserves. Licensing and usage statements are strict.
• GSHHG: multi-resolution global coastlines, national boundaries, and rivers, suitable for mapping and coastal analysis.

Data Sources

The following table summarizes major publicly accessible global administrative-boundary vector datasets to help you choose by accuracy, license, and update frequency.

Dataset characteristics and selection guidance

1. Publication-quality cartography and lightweight visualization Prefer Natural Earth. It is public domain, has no copyright burden, and the 1:10M package is only several dozen MB. If you need vector coastlines, capital points, maritime dispute lines, and similar features, they can also be downloaded together. naturalearthdata.com
2. Academic/research analysis
   • GADM: deep administrative hierarchy and detailed shapes, useful for statistical analysis. However, commercial use is prohibited, and some island boundaries have gaps. gadm.org
   • GAUL: maintained by FAO and aligned with UN official boundaries. New versions have been released annually in recent years, making it suitable for agriculture and food-security models. A usage statement is required. FAO Home
3. Open-source engineering or commercial products
   • geoBoundaries: CC BY and usable commercially. It provides both high-precision original data and simplified CGAZ data, and preserves multiple versions for disputed areas such as De facto and US DoS views. geoboundaries.org
   • OSM / Geofabrik: if a project needs daily updates or administrative communities down to street level, OSM relations can be used directly. Pay attention to ODbL share-alike requirements for derived databases. geofabrik.de
4. Humanitarian relief and disaster response
   • OCHA COD-AB (Common Operational Dataset): official boundaries from national governments or UN country offices. These datasets often include P-codes, making them convenient for joining with population or disaster-location data. data.humdata.org

Natural Earth Dataset

Natural Earth is a public domain map dataset designed primarily for cartography and visualization rather than high-precision GIS analysis. It is available at 1:10M, 1:50M, and 1:110M scales.

It supports multiple country POV versions, and the boundaries differ between views. Download the version that fits your use case.

Data download page: Natural Earth - Free vector and raster map data at 1:10m, 1:50m, and 1:110m scales

Download Data

• Global boundaries down to country level, including 258 countries and territories worldwide, at 1:10M scale using v5.1 (Dec 2023) data.

  • Download countries (4.7 MB) version 5.1.1

    Download without boundary lakes (4.87 MB) version 5.1.1

• Global country-level boundaries from different national points of view. The China POV version is broadly consistent with the boundaries in China's public-version vector data.

  • Download countries (China POV) (4.68 MB) version 5.1.1

• Global boundaries down to states and provinces

  • Download states and provinces (14.22 MB) version 5.1.1

    Download as scale ranks (12.83 MB) version 5.1.1

    Download without large lakes (14.36 MB) version 5.1.1

    Download boundary lines (5.72 MB) version 5.1.0

• Populated places: city and town points, from Tokyo to Wasilla and from Cairo to Kandahar

  • Download populated places (2.68 MB) version 5.1.2

• Roads

  • Download roads (8.66 MB) version 5.0.0
  • Download North America supplement (45.57 MB) version 4.0.0

• Railways

  • Download railroads (14.42 MB) version 4.0.0
  • Download North America supplement (405.5 KB) version 4.0.0

• Rivers and lake centerlines

  • Download rivers and lake centerlines (1.98 MB) version 5.0.0
  • Download scale ranks and tapering (2.3 MB) version 5.0.0
  • Download Australia supplement (401.62 KB) version 5.0.0
  • Download Europe supplement (584.96 KB) version 5.0.0
  • Download North America supplement (3.07 MB) version 5.0.0

• Lakes and reservoirs

  • Download lakes (2.24 MB) version 5.0.0
  • Download historic lakes (27.12 KB) version 5.0.0
  • Download pluvial lakes (214.24 KB) version 4.0.0
  • Download Australia supplement (96.06 KB) version 5.0.0
  • Download Europe supplement (180.74 KB) version 5.0.0
  • Download North America supplement (656.21 KB) version 5.0.0

• Time zones, including 24+ time zones with local offsets and map color codes

  • Download time zones (1.88 MB) version 4.0.0

• United States only

  • Download counties (1.83 MB) version 5.1.1
  • Download without large lakes (1.85 MB) version 5.1.1
  • Download as scale ranks (1.33 MB) version 5.1.1
  • Download scale ranks with minor islands (1.33 MB) version 5.1.1

• Full page: Natural Earth > 1:10m Cultural Vectors - 1:10m, 1:50m, and 1:110m

Load Data

Upload data

After extracting the downloaded data, upload it to Cloud Resource Manager. The software recognizes the vector data formats and prompts the user to import the data as the default pgv format or keep the existing shp format.

Load data

Select the uploaded ne_10m_time_zones.pgv data. The Resource Manager dynamic toolbar will show available operations such as Download, Delete, Load, and Rename. Select Load to load the data into the current project.

Essential Notes

When using Natural Earth or other vector datasets that contain China's boundaries, you must pay special attention to boundary accuracy. Except for Download countries (China POV) (4.68 MB) version 5.1.1, other vector datasets contain multiple compilation errors in China's boundaries. Typical examples include Nanggarze County, Lhozhag County, Cuona City, and Medog County.

Use China's public-version 1:1,000,000 vector data to replace China-region data from these foreign sources. The simplest approach is to use a clipping tool to remove the China portion from Natural Earth data, then replace it with the correct data.

Processing Workflow

1. Delete the China, Hong Kong, and related features from the global vector data.

2. Use China's land-area polygon vector data to erase the corresponding region from the global vector data. In the toolbox, open Analysis Tools > Overlay Analysis > Erase.

3. Merge the erased global vector data with the China land-area polygon vector data. Use GIS Toolbox > Analysis Tools > Overlay Analysis > Union to combine the two files.

GAUL Data Source

The GAUL data source is maintained by the Geospatial Unit of the Food and Agriculture Organization of the United Nations (FAO). It is aligned with UN official boundaries, has released new versions annually in recent years, and is suitable for agriculture and food-security models.

Level 1 and Level 2 Data Description and Downloads

Essential Notes

When using the GAUL dataset, you must also pay attention to multiple compilation errors in China's boundaries. In addition to the Natural Earth errors around Nanggarze County, Lhozhag County, Cuona City, and Medog County, there are also compilation errors near the boundary between Ngari Prefecture and Hotan Prefecture.

Use China's public-version 1:1,000,000 vector data to replace China-region data from these foreign sources. The simplest approach is to use a clipping tool to remove the China portion from Natural Earth data, then replace it with the correct data.

To meet public demand for geographic information data resources, the National Geographic Information Resource Catalog Service System, authorized by the Ministry of Natural Resources, provides free downloads of all national 1:1,000,000 public-version feature layers. The downloaded data is distributed by standard 1:1,000,000 map sheets. It includes nine datasets: hydrography, settlements and facilities, transportation, pipelines, boundaries and administrative divisions, landform and soil, vegetation, place names, and annotations. Spatial relationships between features and related attribute information are preserved.

Data download page: 1:1,000,000 Public-Version Basic Geographic Information Data

Data Introduction

Result Data Organization

The national public-version 1:1,000,000 vector data (2021) includes nine datasets: hydrography, settlements and facilities, transportation, pipelines, boundaries and administrative divisions, landform and soil, vegetation, place names, and annotations.

Layer names use four characters. The first character represents the data category, the second and third characters are abbreviations of the data content, and the fourth character represents the geometry type.

Map Sheets

The subdivision and numbering of national basic-scale topographic maps are fixed. They are described in detail in the standard Subdivision and Numbering for National Basic Scale Topographic Maps (GB/T 13989-2012). Download page: National Standard | Subdivision and Numbering for National Basic Scale Topographic Maps, GB/T 13989-2012

For 1:1,000,000 scale, the map-sheet subdivision is as follows:

Download Data

User Registration

Step 1: Register

On the homepage of the National Geographic Information Resource Catalog Service System, click the Register option to create a user account.

Enter the required user information in the pop-up page to complete registration.

After registration, activate the account through the registered email address.

Step 2: Log in

After account activation, return to the login page and sign in.

Step 3: Maintain personal information

After logging in, go to the homepage and click Personal Center. On the personal information page, update basic information, change the password, and complete other account details as needed.

Download Data

Use the locating tool to search by administrative area, road name, hydrographic feature name, place name, or map sheet.

For example, search by administrative area and locate Hebei Province.

Click the successful query result on the right. Four standard map sheets will appear. The table lists the sheet number, production time, geodetic datum, and data format. Add all of them to the result cart.

Then go to the result cart and click checkout.

On the review page, a special-service selection appears at the bottom. You can ignore it and submit the order directly.

After submitting the order, a data download button appears on the order page. When downloading data, you need to fill in information on two pages. Be sure to complete the information accurately rather than entering arbitrary values.

Use Data

Data Overview

The downloaded data is stored in GDB format, a proprietary ArcGIS format. Each file is a ZIP archive named after the map-sheet number.

Here is an example: download the sample.

You can inspect the archive contents with decompression software, but because it is a database file, it cannot be opened directly like a regular document.

Data Upload

Upload the data in ZIP format. iXGIS can directly recognize this data format and convert it to pgv format.

Data Loading

Load the data.

After loading, the dataset is available in the current project.

Layer Content

The mapping between layer names and features is as follows:

• Naming rule: layer names use four characters. The first character corresponds to the feature category, such as C, H, or R. The second and third characters are content abbreviations, such as PT for survey control and HY for hydrography. The fourth character corresponds to the geometry type: P for point, L for line, and A for polygon.
• Feature categories: covers nine major categories based on the original description: positioning foundation (C), hydrography (H), settlements and facilities (R), transportation (L), pipelines (P), boundaries and administrative divisions (B), landform and soil (T), vegetation (V), and place names and annotations (A).

A map review approval number is a unique number granted after a map has passed technical review and administrative approval by the competent natural resources authority responsible for surveying, mapping, and geographic information before public release. It usually consists of a province abbreviation or national marker, review category code, year, and sequence number, such as Yue GS (2025) No. 001. Obtaining the approval number means the map complies with the requirements of the Regulations on Map Management, the Measures for Map Review Management, and other regulations regarding content correctness, scale accuracy, and handling of confidential information.

Reference: Measures for Map Review Management

Public notice of map review administrative approvals from January to March 2025

When Do You Need to Apply for a Map Review Approval Number?

According to Article 15 of the Regulations on Map Management, maps released to the public must be submitted to the surveying and mapping geographic information administrative authority with review authority. If an approved map is edited, including enlargement, reduction, cropping, or adding annotations, and then released publicly, it should also be submitted for review again.

Reference: Nine types of questions about map submission for review - Chinese Society for Geodesy Photogrammetry and Cartography

How to Apply

Determine the competent authority

• National, cross-provincial or cross-regional maps, or maps involving foreign affairs: apply to the Ministry of Natural Resources at the national level.
• Provincial and smaller-scope maps: apply to the natural resources department or bureau of the relevant province, autonomous region, or municipality.

Prepare materials (submitted online or at a service window)

Review process and timeline

• Acceptance: the service window issues a Map Technical Review Notice within one working day. Government of the People's Republic of China
• Technical review: the map technical review center checks coordinates, national boundaries, confidential content, and related items.
• Decision: regular public-version maps are decided within 20 working days; simpler cases such as maps referencing standard maps may take 7 working days or immediate review.
• Collect approval document and approval number: after approval, collect the Map Review Opinion Letter and obtain the map review approval number.

Marking and follow-up obligations

• Printed/electronic publications: mark the approval number on the copyright page or in a prominent position.
• Internet maps: continuously display the approval number in the lower-left corner of the page without blocking it.
• Validity period: approval numbers for internet maps are valid for 2 years and must be resubmitted after expiration. Newly added annotation content must be filed every 6 months.

Preparation Before Applying

For maps involving national boundaries, the map content must comply with the Specifications for Content Representation on Public Maps.

The standard sample maps referenced in map review specify the following:

1. Standard depiction maps for China's national boundary line

2. The Standard Sample Map for Drawing China's National Boundary Line at 1:1,000,000, issued by the Ministry of Foreign Affairs and the National Administration of Surveying and Mapping in 2001, applies to the review of national boundary lines on maps with scales equal to or smaller than 1:1,000,000 and larger than 1:4,000,000.

3. The 1:4,000,000 Topographic Map of the People's Republic of China applies to the review of national boundary lines on maps with scales smaller than 1:4,000,000.

4. Standard depiction maps for provincial administrative boundaries in China

5. The Atlas of Standard Depiction Maps for Provincial Administrative Boundaries of the People's Republic of China at 1:1,000,000 and the Standard Depiction Map for Provincial Administrative Boundaries of the People's Republic of China at 1:4,000,000, issued by the Ministry of Civil Affairs and the National Administration of Surveying and Mapping in 2004, are the standards for depicting provincial administrative boundaries on all types of maps.

6. The Atlas of Standard Depiction Maps for Provincial Administrative Boundaries of the People's Republic of China at 1:1,000,000 applies to maps with scales equal to or smaller than 1:1,000,000 and larger than 1:4,000,000. The Standard Depiction Map for Provincial Administrative Boundaries of the People's Republic of China at 1:4,000,000 applies to maps with scales equal to or smaller than 1:4,000,000.

7. Standard maps for depicting national boundaries of countries around the world

The Sample Map for Drawing National Boundaries of Countries Around the World, issued by the National Administration of Surveying and Mapping in 2007, applies to the review of national boundaries, regional boundaries, military demarcation lines, ceasefire lines, and other boundaries around the world.

Original link: Map Technical Review Center, Ministry of Natural Resources

You can first use the Standard Map Service System to check whether national boundaries and sensitive annotations are correct, reducing the number of revision rounds from the source.

Frequently Asked Questions

For more questions, see Map use and map review: the questions you care about are all here - Chinese Society for Geodesy Photogrammetry and Cartography