Pyogrio provides a GeoPandas-oriented API to OGR vector data sources, such as ESRI Shapefile, GeoPackage, and GeoJSON. Vector data sources have geometries, such as points, lines, or polygons, and associated records with potentially many columns worth of data. Pyogrio uses a vectorized approach for reading and writing GeoDataFrames to and from OGR vector data sources in order to give you faster interoperability. It uses pre-compiled bindings for GDAL/OGR so that the performance is primarily limited by the underlying I/O speed of data source drivers in GDAL/OGR rather than multiple steps of converting to and from Python data types within Python.

Command line tool for working with OpenStreetMap data based on the Osmium library.

JOSM is an extensible editor for OpenStreetMap (OSM). It supports loading GPX tracks, background imagery and OSM data from local sources as well as from online sources and allows editing the OSM data (nodes, ways, and relations) and their metadata tags.

VirtualPG is a dynamic extension for the SQLite DBMS. It implements the VirtualPostgres driver, allowing to directly exchange data between SQLite and PostgreSQL; if SpatiaLite is available even PostGIS geometries can be exchanged form one Spatial DBMS and the other.

RTree is a Python package with bindings for libspatialindex.

GEOS provides a spatial object model and fundamental geometric functions. It is a C++ port of the Java Topology Suite (JTS). As such, it aims to contain the complete functionality of JTS in C++. This includes all the OpenGIS Simple Features for SQL spatial predicate functions and spatial operators, as well as specific JTS enhanced topology functions.

This package provides python-overpass, a Python wrapper for the OpenStreetMap Overpass API.

The Shapefile C Library provides the ability to write simple C programs for reading, writing and updating (to a limited extent) ESRI Shapefiles, and the associated attribute file (.dbf).

librasterlite2 is a library that stores and retrieves huge raster coverages using a SpatiaLite DBMS.

CDO is a collection of command-line operators to manipulate and analyse climate and NWP model data. Supported data formats are GRIB 1/2, netCDF 3/4, SERVICE, EXTRA and IEG. There are more than 600 operators available.

OWSLib is a Python package for client programming with Open Geospatial Consortium (OGC) web service (hence OWS) interface standards, and their related content models.

geopy is a Python client for several popular geocoding web services. geopy makes it easy for Python developers to locate the coordinates of addresses, cities, countries, and landmarks across the globe using third-party geocoders and other data sources.

OpenGeoSys (OGS) is a scientific open source project for the development of numerical methods for the simulation of thermo-hydro-mechanical-chemical (THMC) processes in porous and fractured media. OGS is implemented in C++, it is object-oriented with an focus on the numerical solution of coupled multi-field problems (multi-physics). Parallel versions of OGS are available relying on both MPI and OpenMP concepts. Application areas of OGS are currently CO2 sequestration, geothermal energy, water resources management, hydrology and waste deposition.

Fiona is GDAL’s neat and nimble vector API for Python programmers. Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to OGR. Fiona can read and write real-world data using multi-layered GIS formats and zipped virtual file systems and integrates readily with other Python GIS packages such as pyproj, Rtree, and Shapely.

This package provides a GTK+ widget (and Python bindings) that when given GPS coordinates,draws a GPS track, and points of interest on a moving map display. Downloads map data from a number of websites, including https://www.openstreetmap.org.

ReadOSM is a library to extract valid data from within an OpenStreetMap input file (in .osm or .osm.pbf format).

The purpose of this library is to provide:

• An extensible framework that will support robust spatial indexing methods.

• Support for sophisticated spatial queries. Range, point location, nearest neighbor and k-nearest neighbor as well as parametric queries (defined by spatial constraints) should be easy to deploy and run.

• Easy to use interfaces for inserting, deleting and updating information.

• Wide variety of customization capabilities. Basic index and storage characteristics like the page size, node capacity, minimum fan-out, splitting algorithm, etc. should be easy to customize.

• Index persistence. Internal memory and external memory structures should be supported. Clustered and non-clustered indices should be easy to be persisted.

The SPLAT (Signal Propagation, Loss, And Terrain) program can use the Longley-Rice path loss and coverage prediction using the Irregular Terrain Model to predict the behaviour and reliability of radio links, and to predict path loss.

The miniSEED library provides a framework for manipulation of SEED data records, a format for commonly used for seismological time series and related data. The library includes the functionality to read and write data records, in addition to reconstructing time series from multiple records.

QGIS is an easy to use Geographical Information System (GIS). It is a GIS data viewer and editor. QGIS supports a number of raster and vector data formats, with new support easily added using the plugin architecture.

Libaec provides fast lossless compression of 1 up to 32 bit wide signed or unsigned integers (samples). The library achieves best results for low entropy data as often encountered in space imaging instrument data or numerical model output from weather or climate simulations. While floating point representations are not directly supported, they can also be efficiently coded by grouping exponents and mantissa.

Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy.

It features:

• object oriented projection definitions

• point, line, polygon and image transformations between projections

• integration to expose advanced mapping in Matplotlib with a simple and intuitive interface

• powerful vector data handling by integrating shapefile reading with Shapely capabilities

This package provides a Python interface to PROJ, a cartographic projections and coordinate transformations library.

Verde is a Python library for processing spatial data (topography, point clouds, bathymetry, geophysics surveys, etc) and interpolating them on a 2D surface (i.e., gridding) with a hint of machine learning.