{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"GDAL provides Python bindings to read, write, and manipulate geospatial raster and vector data through a C++ abstraction layer.","skillfed_tags":["geospatial-io","raster-vector","gis"],"use_cases":["Read and write raster data (GeoTIFF, HDF5, NetCDF) and convert to numpy arrays for scientific analysis.","Process vector data (shapefiles, GeoJSON, PostGIS) to query, filter, and transform geographic features.","Perform coordinate system transformations and spatial reference conversions between different projections.","Build GIS utilities and command-line tools that manipulate multiple geospatial formats programmatically.","Integrate geospatial data pipelines into larger scientific or machine-learning workflows requiring raster/vector I/O."],"what_it_does":"GDAL is a mature geospatial data abstraction library that exposes C++ classes and methods for reading, writing, and manipulating raster (gridded) and vector (feature-based) geospatial data through Python. The package wraps GDAL/OGR functionality via SWIG-generated bindings, providing access to five major modules: gdal (raster operations), ogr (vector operations), osr (spatial reference systems), gdal_array (numpy integration), and gdalconst (constants).\n\nInstallation requires system-level GDAL libraries and headers, making it more complex than pure-Python packages. The bindings are tightly coupled to the underlying C++ library version, so your libgdal installation must match or exceed the required version. A key advanced feature is integration with numpy arrays through methods like ReadAsArray(), enabling numerical processing of raster data. The package is actively maintained, widely used in GIS workflows, and has no known security vulnerabilities.","worth_installing":"Yes, with conditions. GDAL is the de facto standard for geospatial data I/O in Python and is actively maintained with no known vulnerabilities. Install it if you need to work with raster or vector geospatial data. However, expect higher installation friction than typical Python packages: you must have system GDAL libraries and headers pre-installed, and building from source requires SWIG and compilation tools. Use conda on Windows and macOS to avoid compilation."},"id":"gdal","links":{"html":"https://skillfed.io/packages/gdal","md":"https://skillfed.io/packages/gdal.md","pypi":"https://pypi.org/project/gdal/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-22","license_spdx":"MIT","license_treatment":"permissive","name":"GDAL","python_support":"supports_current","summary":"GDAL: Geospatial Data Abstraction Library"},"popularity":{"monthly_downloads":384174,"position":7068,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.13.2"}