A pure-Python library for converting between common geospatial data formats like GeoJSON, WKT/WKB, and GeoPackage Binary.
GeoMet is a pure-Python library for converting between common geospatial data formats. It solves the problem of reliably transforming geometry data between standards like GeoJSON, WKT/WKB, and GeoPackage Binary without requiring heavy external dependencies. The library handles 2D, 3D, and 4D geometries with support for spatial reference systems and bounding boxes.
Python developers and geospatial engineers who need lightweight, dependency-free conversion between geospatial formats in their applications, data pipelines, or analysis tools.
Developers choose GeoMet for its pure-Python implementation that avoids complex C/C++ dependencies like GEOS, making it more portable and maintainable. It provides a reliable, focused alternative with an idiomatic interface for common conversion tasks.
GeoMet - Pure Python conversion library for common geospatial data formats
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Handles conversions between GeoJSON, WKT/WKB, EWKT/EWKB, and GeoPackage Binary, covering all common 2D, 3D, and 4D geometry types as shown in the functionality table.
Pure Python with no external C/C++ dependencies like GEOS, making it portable and easier to debug, as highlighted in the key features and project philosophy.
Maintains spatial reference system identifiers and bounding boxes during conversions where supported, with explicit examples in the README for EWKT and GeoPackage formats.
Uses familiar load/loads/dump/dumps functions, similar to JSON handling, for easy integration into Python workflows, as demonstrated in the example usage.
Cannot output empty geometries to binary formats like WKB or GeoPackage, raising a ValueError as explicitly stated in the limitations section due to dimensionality ambiguity.
Provides no spatial analysis capabilities such as distance calculations or overlays; it solely converts between formats, unlike libraries like Shapely.
Pure Python implementation may be slower than C-based alternatives like GEOS for large datasets, trading speed for portability and ease of debugging.