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Reprojection is the process of transforming geographic data from one coordinate reference system (CRS) to another. It is central to GIS, mapping, and geospatial analysis because datasets collected in different CRSs must be aligned for comparison, overlay, and analysis. Reprojection applies to vector data (points, lines, polygons) and raster data (raster images). It starts with identifying the source CRS and the target CRS, defined by standards such as EPSG codes. The mathematics involves forward and inverse projections of latitude/longitude onto a planar coordinate system, or conversion between two planar coordinate systems, often including a datum shift to account for differences in the reference ellipsoid.

In practice, reprojection can introduce distortions. No single projection can preserve all properties; choices optimize area, shape, distance, or direction. For rasters, reprojection is accompanied by resampling (nearest neighbor, bilinear, bicubic) and may change pixel size and alignment. For vector data, coordinates are re-computed and topology must be maintained.

In computer vision, reprojection refers to mapping 3D world points to the 2D image plane using a camera projection matrix, producing reprojected image points. Reprojection error—the difference between observed image points and projected ones—is a common objective in camera calibration and pose estimation.

Common tools include the PROJ library and GDAL, GIS software such as QGIS or ArcGIS. In all cases, accurate CRS definitions and metadata are essential to preserve spatial integrity.