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GIS Fundamentals

📄️ Orthorectification and Affine Transformation

Orthorectification and affine transformation are core geometric transformation techniques in geospatial data processing. Orthorectification combines a digital elevation model (DEM) and a sensor model to accurately correct image deformation caused by terrain relief and sensor distortion, producing orthophotos suitable for accurate measurement. Affine transformation is a linear transformation method used for basic geometric correction such as image registration, rotation, and scaling. It preserves parallelism and straight lines, but does not account for terrain effects. The two methods are suited to different scenarios such as high-precision mapping and fast image processing, and are important foundations in remote sensing and GIS.

📄️ Measuring Distance in Coordinate Systems

When measuring distance in GIS, choosing the correct measurement method is essential. This article explains four main distance measurement methods: geodesic lines, which are the shortest paths on an ellipsoid and provide the highest accuracy; projected distance, which is straight-line distance in a planar coordinate system; rhumb lines, which maintain a constant bearing; and great ellipse lines, which approximate paths on ellipsoidal sections. Each method is based on different geometric principles and is suitable for navigation, surveying, aviation, and other scenarios. The article also introduces high-precision distance calculation in iXGIS based on the PROJ and GeographicLib libraries, helping users choose the most appropriate method for specific needs and ensuring the accuracy of geospatial analysis.

📄️ Vector Data

Vector data is central to geographic information systems. It represents spatial features with geometry types such as points, lines, and polygons. It follows international OGC standards, such as Simple Features, to ensure cross-platform data interoperability. Mainstream formats include GeoPackage for lightweight single-file storage, PostGIS for enterprise databases, and KML for network exchange. Vector data supports coordinate system definitions and efficient storage, such as WKB encoding. It is suitable for cartography, spatial analysis, and mobile applications, improving data sharing and visualization efficiency.

📄️ Raster Data

Raster data is a core GIS model for representing spatial phenomena. It divides a geographic area into a regular grid, and each cell stores attribute values such as elevation, temperature, or land cover. Raster data supports multiple data types, including integer and floating-point values, and spatial reference systems such as geographic and projected coordinate systems. Common formats include GeoTIFF, including Cloud Optimized GeoTIFF (COG), ERDAS IMG, and NetCDF. Raster data is widely used for remote sensing imagery, digital elevation models (DEMs), and related scenarios. Raster pyramid (.ovr) technology improves browsing performance for large imagery.