Raster libraries¶
This page lists Python GIS and Earth Observation libraries related to working with raster data, categorized into core (data structures), data processing, analysis and visualization. If you see any missing Python tools, please open a PR (see instructions). Tools are sorted alphabetically in each category. The linkages section shows how the tools are connected to the broader Python ecosystem.
Tables below list relevant information about the libraries, including:
links to the Homepage of the package (redirects after clicking the House character)
short Info (description) of the package: You can see the desciprtion by holding your mouse on top of the ⓘ character for a second
License
Latest PyPi and conda-forge version of the package
Number of downloads from PyPi or conda-forge
Latest release date
Libraries¶
from pygieons import Ecosystem
# Initialize
e = Ecosystem(plot_type="raster", log=False)
# Prepare the table and plot it
e.prepare_table().show()
| Name | Homepage | Info | License | PyPi version | PyPi downloads (monthly) | Conda-forge version | Conda-forge downloads | Conda-forge latest release |
|---|---|---|---|---|---|---|---|---|
| gstools | 🏠 | ⓘ |  |
 |
1,668 |  |
 |
 |
| pykrige | 🏠 | ⓘ |  |
 |
17,754 |  |
 |
 |
| pysheds | 🏠 | ⓘ |  |
 |
925 |  |
 |
 |
| pyspatialml | 🏠 | ⓘ | NA |  |
78 | NA | NA | NA |
| rasterstats | 🏠 | ⓘ |  |
 |
61,113 |  |
 |
 |
| richdem | 🏠 | ⓘ |  |
 |
2,187 |  |
 |
 |
| scikit-learn | 🏠 | ⓘ |  |
 |
31,705,557 |  |
 |
 |
| spyndex | 🏠 | ⓘ |  |
 |
287 |  |
 |
 |
| xarray-spatial | 🏠 | ⓘ |  |
 |
1,843 |  |
 |
 |
| Name | Homepage | Info | License | PyPi version | PyPi downloads (monthly) | Conda-forge version | Conda-forge downloads | Conda-forge latest release |
|---|---|---|---|---|---|---|---|---|
| affine | 🏠 | ⓘ |  |
 |
669,468 |  |
 |
 |
| iris | 🏠 | ⓘ |  |
 |
948 |  |
 |
 |
| rasterio | 🏠 | ⓘ |  |
 |
744,408 |  |
 |
 |
| rio-cogeo | 🏠 | ⓘ |  |
 |
13,656 |  |
 |
 |
| rioxarray | 🏠 | ⓘ |  |
 |
38,468 |  |
 |
 |
| sarpy | 🏠 | ⓘ |  |
 |
911 |  |
 |
 |
| sarsen | 🏠 | ⓘ |  |
 |
40 |  |
 |
 |
| xarray | 🏠 | ⓘ |  |
 |
1,626,657 |  |
 |
 |
| Name | Homepage | Info | License | PyPi version | PyPi downloads (monthly) | Conda-forge version | Conda-forge downloads | Conda-forge latest release |
|---|---|---|---|---|---|---|---|---|
| earthengine-api | 🏠 | ⓘ |  |
 |
32,896 |  |
 |
 |
| easystac | 🏠 | ⓘ |  |
 |
87 |  |
 |
 |
| eemont | 🏠 | ⓘ |  |
 |
598 |  |
 |
 |
| eoreader | 🏠 | ⓘ |  |
 |
434 |  |
 |
 |
| lidar | 🏠 | ⓘ |  |
 |
279 |  |
 |
 |
| odc-stac | 🏠 | ⓘ |  |
 |
849 |  |
 |
 |
| planetary-computer | 🏠 | ⓘ |  |
 |
2,874 |  |
 |
 |
| pymap3d | 🏠 | ⓘ |  |
 |
43,146 |  |
 |
 |
| pyrosar | 🏠 | ⓘ |  |
 |
478 |  |
 |
 |
| pystac | 🏠 | ⓘ |  |
 |
31,727 |  |
 |
 |
| pystac-client | 🏠 | ⓘ |  |
 |
9,996 |  |
 |
 |
| radiant-mlhub | 🏠 | ⓘ |  |
 |
6,902 |  |
 |
 |
| rio-hist | 🏠 | ⓘ | NA |  |
168 | NA | NA | NA |
| rio-mucho | 🏠 | ⓘ |  |
 |
8,470 |  |
 |
 |
| rio-tiler | 🏠 | ⓘ |  |
 |
8,373 |  |
 |
 |
| salem | 🏠 | ⓘ |  |
 |
4,972 |  |
 |
 |
| satpy | 🏠 | ⓘ |  |
 |
3,841 |  |
 |
 |
| sentinelsat | 🏠 | ⓘ |  |
 |
12,338 |  |
 |
 |
| stackstac | 🏠 | ⓘ |  |
 |
1,142 |  |
 |
 |
| verde | 🏠 | ⓘ |  |
 |
729 |  |
 |
 |
| xarray-sentinel | 🏠 | ⓘ |  |
 |
70 |  |
 |
 |
| xyzservices | 🏠 | ⓘ |  |
 |
164,884 |  |
 |
 |
| Name | Homepage | Info | License | PyPi version | PyPi downloads (monthly) | Conda-forge version | Conda-forge downloads | Conda-forge latest release |
|---|---|---|---|---|---|---|---|---|
| contextily | 🏠 | ⓘ |  |
 |
64,948 |  |
 |
 |
| pyvista-xarray | 🏠 | ⓘ | NA |  |
362 | NA | NA | NA |
| rio-color | 🏠 | ⓘ |  |
 |
8,260 |  |
 |
 |
| xarray_leaflet | 🏠 | ⓘ |  |
 |
848 |  |
 |
 |
Linkages¶
# Initialize
e = Ecosystem(plot_type="raster+generic", log=False)
# Prepare network and plot
net = e.prepare_net()
net.show()