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 This rules out libraries like TorchSat, RoboSat, and Solaris, which have been abandoned and are no longer maintained.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 If any of the below information is incorrect or out of date, or if you want to add a new project to this list, please open a PR!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	✅	❌	✅	❌	🚧
OTB	LibSVM, OpenCV, Shark	GDAL			0	0	✅	✅	✅	❌	❌
TerraTorch	PyTorch	GDAL, h5py, pandas, xarray		Albumentations	27	13	✅	❌	✅	❌	🚧
DeepForest	PyTorch, TensorFlow*	GDAL, OpenCV, pandas, pillow, scipy	R-tree	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	✅	❌	❌	❌	❌
SITS	R Torch	GDAL		tidyverse	22	0	❌	❌	✅	✅	✅
SPy	numpy	numpy		numpy	3	0	❌	❌	❌	❌	❌
srai	PyTorch	pandas, polars, duckdb	geopandas, duckdb-spatial		0	0	❌	❌	❌	❌	❌
GeoAI	PyTorch	GDAL, xarray	geopandas	Albumentations	0	6	❌	✅	✅	✅	❌
ML4Floods	PyTorch	pandas	geopandas	Albumentations	0	0	❌	❌	✅	❌	🚧
GeoTessera	scikit-learn	GDAL		geopandas	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	✅	❌	✅	❌	❌
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	119	513	58	3,868	187	2,695	19	2,899	100%	MIT
TerraTorch	46	131	23	725	44	795	41	3,560	55%	Apache-2.0
OTB	42	123	38	381	3	24	129	34,558	56%	Apache-2.0
DeepForest	36	245	16	702	112	635	116	1,241	87%	MIT
Raster Vision	33	395	68	2,200	41	1,465	25	3,640	90%	Apache-2.0
samgeo	24	413	61	3,879	3	206	56	310	13%	MIT
spopt	20	60	11	368	32	273	20	1,101	77%	BSD-3-Clause
GDL	18	65	13	189	26	287	63	1,359	7%	MIT
GeoAI	17	352	35	2,484	17	323	69	377	6%	MIT
SITS	17	87	26	529	18	676	50	7,015	91%	GPL-2.0
SPy	15	148	33	657	27	42	32	559	69%	MIT
srai	15	26	10	348	104	286	44	345	75%	Apache-2.0
ML4Floods	13	42	18	177	1	73	0	859	0%	LGPL-3.0
GeoTessera	11	26	4	240	25	39	13	331	15%	MIT
scikit-eo	8	29	7	237	3	15	26	581	32%	Apache-2.0
Myria3D	7	33	12	279	20	79	29	980	57%	BSD-3-Clause
OTBTF	5	38	10	168	22	19	34	1,995	55%	Apache-2.0
GeoDeep	4	29	11	354	3	4	12	104	0%	AGPL-3.0
torchange	2	15	7	225	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	84,356	351,396	2,281,263	48,475	2,329,738
TerraTorch	3,594	14,647	204,257	1,126	205,383
OTB	0	0	0	0	0
DeepForest	5,490	8,133	1,041,362	106,990	1,148,352
Raster Vision	1,304	11,839	275,543	6,519	282,062
samgeo	3,499	19,452	350,914	67,450	418,364
spopt	4,968	20,028	1,287,503	286,156	1,573,659
GDL	0	0	0	0	0
GeoAI	3,061	10,817	120,465	32,094	152,559
SITS	109	976	28,791	152,224	181,015
SPy	8,934	35,239	1,841,992	191,837	2,033,829
srai	47	443	61,188	0	61,188
ML4Floods	48	139	11,844	0	11,844
GeoTessera	579	1,750	11,250	0	11,250
scikit-eo	45	660	26,792	0	26,792
Myria3D	3	43	5,083	0	5,083
OTBTF	0	0	0	0	0
GeoDeep	144	577	25,285	0	25,285
torchange	977	5,279	33,010	2,346	35,356

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.