{"id":13632806,"url":"https://github.com/opengeos/python-geospatial","last_synced_at":"2025-04-12T18:40:05.763Z","repository":{"id":45776323,"uuid":"157938500","full_name":"opengeos/python-geospatial","owner":"opengeos","description":"A collection of Python packages for geospatial analysis with binder-ready notebook examples","archived":false,"fork":false,"pushed_at":"2024-07-04T11:31:10.000Z","size":3125,"stargazers_count":784,"open_issues_count":2,"forks_count":129,"subscribers_count":34,"default_branch":"master","last_synced_at":"2025-04-03T20:12:39.278Z","etag":null,"topics":["binder","binder-ready","geoprocessing","geospatial","geospatial-analysis","gis","pangeo","python","raster","remote-sensing","vector"],"latest_commit_sha":null,"homepage":"","language":"Shell","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/opengeos.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-11-17T01:10:13.000Z","updated_at":"2025-04-02T00:41:51.000Z","dependencies_parsed_at":"2024-09-12T07:14:27.388Z","dependency_job_id":"a6d72f00-43e0-4c22-ac75-128480c01ae6","html_url":"https://github.com/opengeos/python-geospatial","commit_stats":{"total_commits":17,"total_committers":2,"mean_commits":8.5,"dds":0.05882352941176472,"last_synced_commit":"118d4d2eb989dcdf896069d4c1729bee9340f14a"},"previous_names":["giswqs/python-geospatial"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/opengeos%2Fpython-geospatial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/opengeos%2Fpython-geospatial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/opengeos%2Fpython-geospatial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/opengeos%2Fpython-geospatial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/opengeos","download_url":"https://codeload.github.com/opengeos/python-geospatial/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248616311,"owners_count":21134057,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["binder","binder-ready","geoprocessing","geospatial","geospatial-analysis","gis","pangeo","python","raster","remote-sensing","vector"],"created_at":"2024-08-01T22:03:16.186Z","updated_at":"2025-04-12T18:40:05.710Z","avatar_url":"https://github.com/opengeos.png","language":"Shell","readme":"# python-geospatial\n\nA collection of Python packages for geospatial analysis with binder-ready notebook examples. Launch the interactive notebook tutorials with **mybinder.org** or **binder.pangeo.io** test all the pre-installed Python pakcages for geospatial analysis.\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/giswqs/python-geospatial/master)\n[![Pangeo](http://binder.pangeo.io/badge.svg)](http://binder.pangeo.io/v2/gh/giswqs/python-geospatial/master)\n[![MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n\nAuthor: Qiusheng Wu (https://wetlands.io)\n\n## Installation\n\nIt is highly recommended that you use the [conda](https://conda.io/docs/index.html) package manager to install all the requirements. You can either install [Miniconda](https://conda.io/miniconda.html) or the (larger) [Anaconda](https://www.anaconda.com/download/) distribution. It is also recommended that you install [git](https://git-scm.com/downloads) so that you can clone this GitHub reposiotry to your computer. \n\nOnce conda and git are installed, the following commands will create a virtual Python environment named **pygeo** and install all the required packages:\n\n```\ngit clone https://github.com/giswqs/python-geospatial.git\ncd python-geospatial/binder/\nconda env create -f environment.yml\nsource activate pygeo\nipython kernel install --user --name=\"pygeo\"\n```\n\n## Tutorials\n\nLaunch the interactive notebook tutorials with **mybinder.org** or **binder.pangeo.io** now:\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/giswqs/python-geospatial/master)\n[![Pangeo](http://binder.pangeo.io/badge.svg)](http://binder.pangeo.io/v2/gh/giswqs/python-geospatial/master)\n\n## Python Packages\n\nThis list of Python packages is adapted from the Python list of [Awesome Geospatial](https://github.com/sacridini/Awesome-Geospatial#python). All the listed Python packages have been pre-installed in the binder environment.   \n\n### Geospatial Analysis\n\n* [whitebox](https://github.com/giswqs/whitebox) :zap: - A Python package for advanced geospatial data analysis based on [WhiteboxTools](https://github.com/jblindsay/whitebox-tools).\n* [lidar](https://github.com/giswqs/lidar) - lidar is a toolset for terrain and hydrological analysis using digital elevation models (DEMs).\n* [pygis](https://github.com/giswqs/pygis) - pygis is a collection of Python snippets for geospatial analysis.\n* [ArcGIS Python API](https://developers.arcgis.com/python/) - Esri's Python library for working with maps and geospatial data, powered by web GIS.\n* [dask-rasterio](https://github.com/dymaxionlabs/dask-rasterio) - Read and write rasters in parallel using Rasterio and Dask.\n* [earthengine-api](https://anaconda.org/conda-forge/earthengine-api) :zap: - The Earth Engine Python API allows developers to interact with Google Earth Engine.\n* [EarthPy](https://github.com/earthlab/earthpy) - EarthPy is a python package that makes it easier to plot and work with spatial raster and vector data. \n* [Fiona](http://toblerity.org/fiona/) :zap: - For making it easy to read/write geospatial data formats.\n* [GDAL](https://anaconda.org/conda-forge/gdal) - The Geospatial Data Abstraction Library for reading and writing raster and vector geospatial data formats. \n* [geeup](https://github.com/samapriya/geeup) - Simple CLI for Earth Engine Uploads.\n* [geojson-area](https://github.com/scisco/area) - Calculate the area inside of any GeoJSON geometry. This is a port of Mapbox's geojson-area for Python.\n* [geojsonio](https://github.com/jwass/geojsonio.py) - Open GeoJSON data on geojson.io from Python. \n* [GeoPandas](https://github.com/geopandas/geopandas) - Python tools for geographic data.\n* [GIPPY](https://github.com/gipit/gippy) - Geospatial Image Processing for Python.\n* [gpdvega](https://github.com/iliatimofeev/gpdvega) - gpdvega is a bridge between GeoPandas and Altair that allows to seamlessly chart geospatial data.\n* [mapboxgl-jupyter](https://github.com/mapbox/mapboxgl-jupyter) - Use Mapbox GL JS to visualize data in a Python Jupyter notebook.\n* [networkx](http://networkx.github.io/) - To work with networks.\n* [OSMnet](https://github.com/UDST/osmnet) - Tools for the extraction of OpenStreetMap street network data.\n* [pandana](https://github.com/UDST/pandana) - Pandas Network Analysis - dataframes of network queries, quickly.\n* [Peartree](https://github.com/kuanb/peartree) - Peartree: A library for converting transit data into a directed graph for network analysis.\n* [pygdal](https://pypi.org/project/pygdal/) - Virtualenv and setuptools friendly version of standard GDAL python bindings.\n* [pymap3d](https://github.com/scivision/pymap3d) - Python 3D coordinate conversions for geospace ecef enu eci.\n* [Pyncf](https://github.com/karimbahgat/pyncf) - Pure Python NetCDF file reading and writing.\n* [PyProj](https://github.com/jswhit/pyproj) - For conversions between projections.\n* [PySAL](http://pysal.readthedocs.io/en/latest/) - For all your spatial econometrics needs.\n* [PyShp](https://code.google.com/archive/p/pyshp/) - For reading and writing shapefiles.\n* [rasterio](https://github.com/mapbox/rasterio) :zap: - rasterio employs GDAL under the hood for file I/O and raster formatting.\n* [rasterstats](https://github.com/perrygeo/python-rasterstats/) - Python module for summarizing geospatial raster datasets based on vector geometries.\n* [rio-cogeo](https://github.com/mapbox/rio-cogeo) - CloudOptimized GeoTIFF creation plugin for rasterio.   \n* [rio-color](https://github.com/mapbox/rio-color) - Color correction plugin for rasterio.\n* [rio-hist](https://github.com/mapbox/rio-hist) - Histogram matching plugin for rasterio.\n* [rio-tiler](https://github.com/mapbox/rio-tiler) - Get mercator tile from landsat, sentinel or other AWS hosted raster.\n* [Rtree](http://toblerity.org/rtree/) - For efficiently querying spatial data.\n* [sentinelhub](https://github.com/sentinel-hub/sentinelhub-py) - Download and process satellite imagery in Python scripts using Sentinel Hub services.\n* [sentinelsat](https://github.com/sentinelsat/sentinelsat) - Search and download Copernicus Sentinel satellite images.\n* [Shapely](https://pypi.python.org/pypi/Shapely) - Manipulation and analysis of geometric objects in the Cartesian plane.\n* [ts-raster](https://github.com/adbeda/ts-raster) - ts-raster is a python package for analyzing time-series characteristics from raster data. \n* [urbansim](https://github.com/UDST/urbansim) - New version of UrbanSim, a platform for modeling metropolitan real estate markets.\n* [USGS API](https://github.com/kapadia/usgs) - USGS is a python module for interfacing with the US Geological Survey's API.\n* [Verde](https://github.com/fatiando/verde) - Verde is a Python library for processing spatial data and interpolating it on regular grids.\n* [xarray](http://xarray.pydata.org/en/stable/) - An open source project that aims to bring the labeled data power of pandas to the physical sciences.\n\n### Mapping/Plotting\n\n* [basemap](https://github.com/matplotlib/basemap) - Plot on map projections (with coastlines and political boundaries) using matplotlib.\n* [bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.\n* [Cartopy](http://scitools.org.uk/cartopy/) - A library providing cartographic tools for python for plotting spatial data.\n* [Descartes](https://pypi.python.org/pypi/descartes) - Plot geometries in matplotlib.\n* [geoplot](https://github.com/ResidentMario/geoplot) - geoplot is a high-level Python geospatial plotting library.\n* [geopy](https://github.com/geopy/geopy) - geopy is a Python 2 and 3 client for several popular geocoding web services.\n* [folium](https://github.com/python-visualization/folium) - Python Data, Leaflet.js Maps.\n* [matplotlib](http://matplotlib.org/) - Python 2D plotting library.\n* [mplleaflet](https://github.com/jwass/mplleaflet) - mplleaflet converts a matplotlib plot into a webpage containing a pannable, zoomable Leaflet map.\n* [pyWPS](http://pywps.org/) - An implementation of the Web Processing Service standard from the Open Geospatial Consortium. \n* [pyCSW](http://pycsw.org/) - Fully implements the OpenGIS Catalogue Service Implementation Specification.\n* [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) - A Jupyter / Leaflet bridge enabling interactive maps in the Jupyter notebook.\n* [here-map-widget-for-jupyter](https://github.com/heremaps/here-map-widget-for-jupyter) - Use HERE Maps API for JavaScript in your Jupyter Notebook.\n\n### Deep Learning\n\n* [label-maker](https://github.com/developmentseed/label-maker) - Data Preparation for Satellite Machine Learning.\n* [label-maker-binder](https://github.com/giswqs/label-maker-binder/pulse) - Using label-maker in an interactive notebook on the cloud.\n* [Keras](https://keras.io/) - Keras is a high-level neural networks API capable of running on top of TensorFlow, CNTK, or Theano.\n* [TensorFlow](https://www.tensorflow.org/) - TensorFlow is an open source software library for numerical computation using data flow graphs.\n\n### General Python\n\n* [dask](https://github.com/dask/dask) - Dask is a flexible parallel computing library for analytics. \n* [imageio](https://imageio.github.io/) - imageio provides an easy interface to read and write a wide range of image data.\n* [Mahotas](https://github.com/luispedro/mahotas) - Mahotas is a library of fast computer vision algorithms operating over numpy arrays.\n* [NumPy](http://www.numpy.org/) - NumPy is the fundamental package for scientific computing with Python.\n* [Pandas](http://pandas.pydata.org/) - Open source library providing high-performance, easy-to-use data structures and data analysis tools.\n* [scikit-image](http://scikit-image.org/) - Scikit-image is a collection of algorithms for image processing.\n* [scikit-learn](https://github.com/scikit-learn/scikit-learn) - scikit-learn is a Python module for machine learning built on top of SciPy.\n* [SciPy](https://github.com/scipy/scipy) - SciPy is open-source software for mathematics, science, and engineering.\n* [Statsmodels](http://statsmodels.sourceforge.net/) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.\n* [geojson-shave](https://github.com/ben-n93/geojson-shave) - A Python command-line tool for reducing the size of GeoJSON files.\n\n## Cloud Computing Platforms\n\n* [Google Earth Engine](https://earthengine.google.com/) :zap: - Planetary-scale geospatial analysis for everyone.\n* [Pangeo](http://pangeo.io/) - A community platform for Big Data geoscience.\n* [Geospatial Big Data Platform (GBDX)](https://platform.digitalglobe.com/gbdx/) - Cloud computing platform from Digital Globe.\n* [Radiant Earth](https://www.radiant.earth/) - Open-source cloud computing infrastructure for geospatial analysis.\n* [Radiant MLHub](https://www.mlhub.earth/) - Open Repository for Geospatial Training Data.\n* [Sentinel Playground](https://www.sentinel-hub.com/) - Cloud platform for analysis of Sentinel-2A and B and so on.\n* [Vane: Query Language](https://owm.io/vaneLanguage) - Creating Basemaps from different satellite images with online processing and computing.\n\n## References\n\n* [Awesome-Geospatial](https://github.com/sacridini/Awesome-Geospatial)\n* [SpatialPython](https://github.com/SpatialPython/spatial_python)\n* [python-geospatial-ecosystem](https://github.com/loicdtx/python-geospatial-ecosystem)\n* [Automating-GIS-processes](https://github.com/Automating-GIS-processes/2018)\n* [Geo-Python](https://github.com/geo-python/2018)\n* [scipy2018-geospatial-data](https://github.com/geopandas/scipy2018-geospatial-data)\n* [Geospatial_Data_with_Python](https://github.com/SocialDataSci/Geospatial_Data_with_Python)\n* [Essential geospatial Python libraries](https://medium.com/@chrieke/essential-geospatial-python-libraries-5d82fcc38731)\n* [Geo-spatial analysis with Python](https://medium.com/@lisa.mitford/geo-spatial-analysis-with-python-fdddd69eebea)\n* [From Analysis Ready Data to Analysis Engines and Everything in between](https://medium.com/@samapriyaroy/from-analysis-ready-data-to-analysis-engines-and-everything-in-between-676d98792d2e)\n","funding_links":[],"categories":["Shell"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopengeos%2Fpython-geospatial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopengeos%2Fpython-geospatial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopengeos%2Fpython-geospatial/lists"}