Related Libraries

TorchGeo is not the only geospatial machine learning library out there, there are a number of alternatives that you can consider using. The goal of this page is to provide an up-to-date listing of these libraries and the features they support in order to help you decide which library is right for you. Criteria for inclusion on this list include:

• geospatial: Must be primarily intended for working with geospatial, remote sensing, or satellite imagery data. This rules out libraries like torchvision, which provides little to no support for multispectral data or geospatial transforms.

• machine learning: Must provide basic machine learning functionality. This rules out libraries like GDAL, which is useful for data loading but offers no support for machine learning.

• library: Must be an actively developed software library with testing and releases on repositories like PyPI or CRAN. This rules out libraries like TorchSat, RoboSat, and Solaris, which have been abandoned and are no longer maintained.

When deciding which library is most useful to you, it is worth considering the features they support, how actively the library is being developed, and how popular the library is, roughly in that order.

Note

Software is a living, breathing organism and is constantly undergoing change. If any of the below information is incorrect or out of date, or if you want to add a new project to this list, please open a PR!

Last updated: November 2025

Features

Key: ✅ full support, 🚧 partial support, ❌ no support

Library	ML Backend	I/O Backend	Spatial Backend	Transform Backend	Datasets	Weights	CLI	GUI	Reprojection	STAC	Time Series
TorchGeo	PyTorch	GDAL, h5py, laspy, NetCDF4, OpenCV, pandas, pillow, scipy, xarray	geopandas, R-tree*	Kornia	141	132	✅	❌	✅	🚧	🚧
TerraTorch	PyTorch	GDAL, h5py, pandas, xarray		Albumentations	27	13	✅	❌	✅	❌	🚧
OTB	LibSVM, OpenCV, Shark	GDAL			0	0	✅	✅	✅	❌	❌
DeepForest	PyTorch, TensorFlow*	GDAL, OpenCV, pandas, pillow, scipy	R-tree	Kornia, Albumentations*	0	4	✅	❌	❌	❌	❌
Raster Vision	PyTorch, TensorFlow*	GDAL, OpenCV, pandas, pillow, scipy, xarray	STAC	Albumentations	0	6	✅	❌	✅	✅	🚧
samgeo	PyTorch	GDAL, OpenCV, pandas, xarray	geopandas	numpy	0	0	❌	✅	✅	❌	❌
spopt	scikit-learn	pandas	shapely	numpy	0	0	❌	❌	✅	❌	❌
GDL	PyTorch	GDAL, pandas		Kornia	0	3	✅	❌	❌	❌	❌
GeoAI	PyTorch	GDAL, xarray	geopandas	Albumentations	0	6	❌	✅	✅	✅	❌
SITS	R Torch	GDAL		tidyverse	22	0	❌	❌	✅	✅	✅
SPy	numpy	numpy		numpy	3	0	❌	❌	❌	❌	❌
srai	PyTorch	pandas, polars, duckdb	geopandas, duckdb-spatial		0	0	❌	❌	❌	❌	❌
ML4Floods	PyTorch	pandas	geopandas	Albumentations	0	0	❌	❌	✅	❌	🚧
GeoTessera	scikit-learn	GDAL		geopandas	0	0	✅	❌	✅	❌	❌
AIDE	PyTorch	pillow		torchvision	0	0	❌	✅	❌	❌	❌
scikit-eo	scikit-learn, TensorFlow	GDAL, pandas, scipy	geopandas	numpy	0	0	❌	❌	❌	❌	🚧
Myria3D	PyTorch	h5py, numpy	PDAL	PyTorch	0	0	🚧	❌	❌	❌	❌
OTBTF	TensorFlow	GDAL, OTB			0	0	✅	❌	✅	❌	❌
GeoDeep	ONNX	GDAL			0	7	✅	❌	✅	❌	❌
rs-embed	PyTorch	Earth Engine, tifffile, xarray, pandas, pillow	pyproj, affine		0	19	✅	❌	❌	❌	✅
torchange	PyTorch	Apache Arrow, skimage		Albumentations	8	8	❌	❌	❌	❌	✅

*Support was dropped in newer releases.

ML Backend: The machine learning libraries used by the project. For example, if you are a scikit-learn user, eo-learn may be perfect for you, but if you need more advanced deep learning support, you may want to choose a different library.

I/O Backend: The I/O libraries used by the project to read data. This gives you a rough idea of which file formats are supported. For example, if you need to work with lidar data, a project that uses laspy may be important to you.

Spatial Backend: The spatial library used to perform spatial joins and compute intersections based on geospatial metadata. This may be important to you if you intend to scale up your simulations.

Transform Backend: The transform library used to perform data augmentation. For example, Kornia performs all augmentations on PyTorch Tensors, allowing you to run your transforms on the GPU for an entire mini-batch at a time.

Datasets: The number of geospatial datasets built into the library. Note that most projects have something similar to TorchGeo’s RasterDataset and VectorDataset, allowing you to work with generic raster and vector files. Collections of datasets are only counted a single time, so data loaders for Landsats 1–9 are a single dataset, and data loaders for SpaceNets 1–8 are also a single dataset.

Weights: The number of model weights pre-trained on geospatial data that are offered by the library. Note that most projects support hundreds of model architectures via a library like PyTorch Image Models, and can use models pre-trained on ImageNet. There are far fewer libraries that provide foundation model weights pre-trained on multispectral satellite imagery.

CLI: Whether or not the library has a command-line interface. This low-code or no-code solution is convenient for users with limited programming experience, and can offer nice features for reproducing research and fast experimentation.

Reprojection: Whether or not the library supports automatic reprojection and resampling of data. Without this, users are forced to manually warp data using a library like GDAL if they want to combine datasets in different coordinate systems or spatial resolutions.

STAC: Whether or not the library supports the spatiotemporal asset catalog. STAC is becoming a popular means of indexing into spatiotemporal data like satellite imagery.

Time-Series: Whether or not the library supports time-series modeling. For many remote sensing applications, time-series data provide important signals.

GitHub

These are metrics that can be scraped from GitHub.

Library	Contributors	Forks	Watchers	Stars	Issues	PRs	Releases	Commits	Test Coverage	License
TorchGeo	122	545	56	3,989	208	2,893	20	3,054	100%	MIT
TerraTorch	47	150	25	773	64	855	46	3,702	55%	Apache-2.0
DeepForest	41	253	17	727	107	683	117	1,281	87%	MIT
OTB	41	123	38	384	3	24	129	34,558	56%	Apache-2.0
Raster Vision	32	396	68	2,212	41	1,465	25	3,640	90%	Apache-2.0
samgeo	25	424	61	3,968	5	233	60	337	13%	MIT
GeoAI	22	420	37	2,853	8	441	80	494	6%	MIT
spopt	20	63	11	371	42	289	20	1,106	77%	BSD-3-Clause
GDL	18	66	14	195	26	287	64	1,391	7%	MIT
SITS	17	88	26	531	23	688	50	7,015	91%	GPL-2.0
SPy	15	147	33	661	25	43	32	559	69%	MIT
srai	15	30	11	364	104	286	44	345	75%	Apache-2.0
ML4Floods	13	44	18	179	1	73	0	859	0%	LGPL-3.0
GeoTessera	12	29	4	292	25	46	15	359	15%	MIT
AIDE	11	60	22	242	28	23	4	782	0%	MIT
scikit-eo	8	29	8	244	3	15	26	582	32%	Apache-2.0
Myria3D	7	35	12	286	20	79	29	980	57%	BSD-3-Clause
OTBTF	5	38	10	168	22	19	34	1,995	55%	Apache-2.0
GeoDeep	4	48	12	448	2	4	12	104	0%	AGPL-3.0
rs-embed	4	12	1	103	10	39	3	359	61%	Apache-2.0
torchange	2	20	6	235	7	16	5	185	0%	Apache-2.0

Contributors: The number of contributors. This is one of the most important metrics for project development. The more developers you have, the higher the bus factor, and the more likely the project is to survive. More contributors also means more new features and bug fixes.

Forks: The number of times the git repository has been forked. This gives you an idea of how many people are attempting to modify the source code, even if they have not (yet) contributed back their changes.

Watchers: The number of people watching activity on the repository. These are people who are interested enough to get notifications for every issue, PR, release, or discussion.

Stars: The number of people who have starred the repository. This is not the best metric for number of users, and instead gives you a better idea about the amount of hype surrounding the project.

Issues: The total number of open issues. Although it may seem counterintuitive, the more issues, the better. Large projects like PyTorch have tens of thousands of open issues. This does not mean that PyTorch is broken, it means that it is popular and has enough issues to discover corner cases or open feature requests.

PRs: The total number of open and closed pull requests. This tells you how active development of the project has been. Note that this metric can be artificially inflated by bots like dependabot.

Releases: The number of software releases. The frequency of releases varies from project to project. The important thing to look for is multiple releases.

Commits: The number of commits on the main development branch. This is another metric for how active development has been. However, this can vary a lot depending on whether PRs are merged with or without squashing first.

Test Coverage: The percentage of the core library that is hit by unit tests. This is especially important for interpreted languages like Python and R where there is no compiler type checking. 100% test coverage is ideal, but 80% is considered good.

License: The license the project is distributed under. For commercial researchers, this may be very important and decide whether or not they are able to use the software.

Downloads

These are download metrics for the project. Note that these numbers can be artificially inflated by mirrors and installs during continuous integration. They give you a better idea of the number of projects that depend on a library than the number of users of that library.

Library	PyPI/CRAN Last Week	PyPI/CRAN Last Month	PyPI/CRAN All Time	Conda All Time	Total All Time
TorchGeo	58,758	286,375	2,947,860	52,603	3,000,463
TerraTorch	27,354	120,297	438,804	1,885	440,689
OTB	0	0	0	0	0
DeepForest	4,055	17,086	1,081,123	111,344	1,192,467
Raster Vision	188	2,765	284,202	6,832	291,034
samgeo	4,150	18,257	391,391	75,523	466,914
spopt	6,064	37,547	1,361,250	304,215	1,665,465
GDL	0	0	0	0	0
GeoAI	4,244	17,975	161,034	41,438	202,472
SITS	135	641	30,541	159,087	189,628
SPy	9,310	41,150	1,933,785	223,471	2,157,256
srai	151	874	64,212	0	64,212
ML4Floods	15	72	12,446	0	12,446
GeoTessera	664	2,486	17,601	0	17,601
AIDE	0	0	0	0	0
scikit-eo	89	319	28,295	0	28,295
Myria3D	11	37	5,359	0	5,359
OTBTF	0	0	0	0	0
GeoDeep	448	2,744	30,636	0	30,636
rs-embed	146	353	1,179	0	1,179
torchange	564	3,508	43,276	2,478	45,754

PyPI Downloads: The number of downloads from the Python Packaging Index. PyPI download metrics are computed by PyPI Stats and PePy.

CRAN Downloads: The number of downloads from the Comprehensive R Archive Network. CRAN download metrics are computed by Meta CRAN.

Conda Downloads: The number of downloads from Conda Forge. Conda download metrics are computed by Conda Forge.