TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

Last update: Dec 30, 2022

Overview

TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

The goal of this library is to make it simple:

for machine learning experts to use geospatial data in their workflows, and
for remote sensing experts to use their data in machine learning workflows.

See our installation instructions, documentation, and examples to learn how to use torchgeo.

External links:

Tests:

Installation instructions

Until the first release, you can install an environment compatible with torchgeo with conda, pip, or spack as shown below.

Conda

Note: if you do not have access to a GPU or are running on macOS, replace pytorch-gpu with pytorch-cpu in the environment.yml file.

$ conda config --set channel_priority strict
$ conda env create --file environment.yml
$ conda activate torchgeo

Pip

With Python 3.6 or later:

$ pip install -r requirements.txt

Spack

$ spack env activate .
$ spack install

Documentation

You can find the documentation for torchgeo on ReadTheDocs.

Example usage

The following sections give basic examples of what you can do with torchgeo. For more examples, check out our tutorials.

Train and test models using our PyTorch Lightning based training script

We provide a script, train.py for training models using a subset of the datasets. We do this with the PyTorch Lightning LightningModules and LightningDataModules implemented under the torchgeo.trainers namespace. The train.py script is configurable via the command line and/or via YAML configuration files. See the conf/ directory for example configuration files that can be customized for different training runs.

$ python train.py config_file=conf/landcoverai.yaml

Download and use the Tropical Cyclone Wind Estimation Competition dataset

This dataset is from a competition hosted by Driven Data in collaboration with Radiant Earth. See here for more information.

Using this dataset in torchgeo is as simple as importing and instantiating the appropriate class.

import torchgeo.datasets

dataset = torchgeo.datasets.TropicalCycloneWindEstimation(split="train", download=True)
print(dataset[0]["image"].shape)
print(dataset[0]["wind_speed"])

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Comments

Increase coverage of trainers

Since we no longer run integration tests on main, our trainer modules are currently the least well-tested code. This PR attempts to add unit tests for these trainers. I'm a bit stuck at the moment, so opening this up for feedback on better ways to test this code while I focus on more important things.
testing trainers

opened by adamjstewart 25
Dependabot for more stable CI

Our CI has been incredibly unstable lately. Every time a new version of a dependency is released, something in our tests breaks, especially mypy.

Using dependabot, we can pin all of our dependencies to a specific version. The bot will then periodically check for updates and open a PR to update the dependency version. That way, only the version update PR will break, not everyone else's PRs.

Side note: a lot of our deps don't yet have wheels for Python 3.10, or have never had wheels for Windows. I was thinking about switching from pip to conda for all CI. Unfortunately, it looks like dependabot only supports pip, not conda.

opened by adamjstewart 19
Datasets: consistent capitalization of band names

There seems to be a 50/50 mix of RGB_BANDS/ALL_BANDS and rgb_bands/all_bands in our datasets. The GeoDataset base class uses lowercase, so this PR changes all other datasets to match. From what I can tell, PEP-8 doesn't seem to distinguish between class attributes and instance attributes, so I'm not sure if these are considered variables or global variables.

@ashnair1 not sure if this affects #687
datasets testing

opened by adamjstewart 18
IDTReeS bugs

Description

While using IDTReeS to train an object detector, I noticed quite a few bugs within this dataset.

1. Coordinates are swapped

Fixed by #683

The above image is a comparison of the actual ground truth of OSBS_27 and the plot produced by torchgeo.

2. Missing polygon

Shapefile contains 5 polygons whereas the plot only shows 4. This issue seems to be at the dataset level.

3. Negative box coordinates in pixel space

This is a weird one. I noticed that some boxes had negative pixel coordinates. Observed this in MLBS_5 but its possible this could be present in others as well.

Comparing QGIS and the corrected torchgeo plot for MLBS_5 didn't reveal much

As you can see, the top 4 polygons are missing. But unlike the previous issue, these polygons do exist. However they seem to be outside the bounds of the image.

This could explain the negative coordinates but I'm not sure as to why there is a difference between the plots.

Steps to reproduce

Most of these issues can be seen by downloading the IDTReeS dataset and comparing the torchgeo plots vs the shapefiles.

Version

0.4.0.dev0
datasets

opened by ashnair1 18

ValueError: empty range for randrange()

When using RandomBatchGeoSampler, 50% of the time the following error will occur. With no code change, this runs perfectly fine the other 50% of the time.

code:

sampler = RandomBatchGeoSampler(ds, size=1024, batch_size=5, length=5 * 5)
dl = DataLoader(ds, batch_sampler=sampler, collate_fn=stack_samples)

for idx, batch in enumerate(dl):
    for idx_s, image in enumerate(batch['image']):
        image = torch.squeeze(image)

error:

  File "/shared/ritwik/miniconda3/envs/dino/lib/python3.7/site-packages/torchgeo/samplers/batch.py", line 115, in __iter__
    bounding_box = get_random_bounding_box(bounds, self.size, self.res)
  File "/shared/ritwik/miniconda3/envs/dino/lib/python3.7/site-packages/torchgeo/samplers/utils.py", line 49, in get_random_bounding_box
    minx = random.randrange(int(width)) * res + bounds.minx
  File "/shared/ritwik/miniconda3/envs/dino/lib/python3.7/random.py", line 190, in randrange
    raise ValueError("empty range for randrange()")
ValueError: empty range for randrange()

samplers

opened by RitwikGupta 18

Indices Transforms
~~This is simply a draft of a transform for computing NDVI and concatenating to the sample["image"] channels. Open to suggestions/feedback.~~

~~Note: I just realized that the requiring the index of the channels for computing the indices doesn't scale. See Enhanced Vegation Index which requires 4+ bands to compute EVI = G * ((NIR - R) / (NIR + C1 * R – C2 * B + L)).~~

Update:

This PR adds the following:

AugmentationSequential wrapper around kornia.augmentation.AugmentationSequential which supports our sample/batch dicts.

ndbi, ndsi, ndvi, ndwi functionals which given specific multispectral band input will compute the associated index. AppendNDBI, AppendNDSI, AppendNDVI, AppendNDWI transform modules which take as input a batch dict, computes the desired index, and appends to the channel dimension.

Unit tests

kornia>=0.5.4 as dependency which is the version when kornia.augmentation.AugmentationSequential was added. Additionally, kornia's only dependency is pytorch.

TODO:

Add jupyter notebook to docs/tutorials display the usage of indices transforms

Fix notebook sphinx errors

Closes #112
transforms
opened by isaaccorley 18
SpaceNet 2
Created a SpaceNet metaclass that works on mlhub collections instead of datasets.

Refactored SpaceNet1

Added SpaceNet 2

Added tests and test data for SpaceNet 2

Note: There is a slight issue with the Vegas collection. Refer radiantearth/radiant-mlhub#65. Simple solution: hard code location of img1 label.
datasets
opened by ashnair1 17

failed conda install on windows10

I was trying to install torchgeo on windows 10 using conda on a new env, I got the following error

>conda create -n torch-geo-test python=3.10

>conda install torchgeo -c conda-forge

Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: -
Found conflicts! Looking for incompatible packages.
This can take several minutes.  Press CTRL-C to abort.
Examining @/win-64::__win==0=0: 100%|████████████████████████████████████████████████████| 5/5 [00:01<00:00,  2.72it/s]|
failed

UnsatisfiableError: The following specifications were found to be incompatible with each other:

Output in format: Requested package -> Available versions
Note that strict channel priority may have removed packages required for satisfiability.

However, a week ago, I installed it on another environment for testing a week ago, and it worked perfectly. I also tried the same thing on WSL2 (ubuntu 22.04), and it worked.

opened by SkirOwen 15

Cant import Sentinel 2 Class

Hi, I have tried with both the normal conda installation of torch and the development one. when I try to import the Sentinel 2 class I get the following error:

from torchgeo.datasets import Sentinel2

cannot import name 'draw_segmentation_masks' from 'torchvision.utils' (/Users/gracecolverd/opt/miniconda3/envs/torch_array/lib/python3.8/site-packages/torchvision/utils.py)

Thanks, Grace

opened by graceebc9 15
Jupyter Notebook tutorials

We need to figure out how to render Jupyter Notebooks in our documentation so that we can provide easy-to-use tutorials for new users. This should work similarly to https://pytorch.org/tutorials/.

Ideally I would like to be able to test these tutorials so that they stay up-to-date.
documentation

opened by adamjstewart 15
GridGeoSampler: change stride of last patch to sample entire ROI

This PR changes the way in which GridGeoSampler samples patches from each tile if the tile size is not a multiple of the stride. We want to cover the entire tile, requiring us to adjust the stride of the final row/column.

This also changes the number of patches returned from each tile. Let $i$ be the size of the input tile. Let $k$ be the requested size of the output patch. Let $s$ be the requested stride. Let $o$ be the number of output rows/columns sampled from each tile.

Before

$$ o = \left\lfloor \frac{i - k}{s} \right\rfloor + 1 $$

After

$$ o = \left\lceil \frac{i - k}{s} \right\rceil + 1 $$

Reboot of #448 Fixes #431
testing samplers

opened by remtav 14
Convert all index transforms to Kornia

This PR converts all of our index transforms to be valid Kornia augmentations. These transforms can now be used with AugmentationSequential just like all of the other transforms we use.
documentation testing transforms backwards-incompatible

opened by adamjstewart 2
DataModules: run all data augmentation on the GPU
This PR overhauls all of our data modules to improve uniformity. This includes the following changes:

[x] Add GeoDataModule and NonGeoDataModule base classes to reduce code duplication

[x] Only instantiate the datasets that are needed for a particular stage

[x] Replace torchvision with kornia (better support for MSI, GPU, inverse)

[x] Replace dataset transforms with on_after_batch_transfer (CPU -> GPU, sample -> batch, faster)

[x] Remove instance methods for preprocessing (fixes #886)

[ ] Documentation

In a future PR, I'm planning on extending this to the rest of our transforms:

Rewrite all index transforms to be compatible with Kornia (#999)

Update tutorials to use Kornia with our transforms

Upstream and remove our custom transforms and AugmentationSequential hacks

documentation datasets testing trainers transforms datamodules backwards-incompatible
opened by adamjstewart 2
Remove tests/datamodules
Summary

I would like to remove (almost) all tests in tests/datamodules and replace them with new tests in tests/trainers that actually ensure our datamodules and trainers are compatible.

Rationale

The current tests simply ensure that the data loaders don't crash. They don't actually test that the datamodules are compatible with our trainers.

Implementation

The bulk of these have already been converted in #329. The remaining tests are:

[ ] chesapeake (tests invalid arguments)

[ ] fair1m (requires rotated ObjectDetectionTask trainer: #840)

[x] inria (#975)

[x] loveda (#966)

[x] nasa_marine_debris (#979)

[ ] oscd (requires ChangeDetectionTask trainer)

[x] potsdam (#929)

[ ] usavars (requires multi-label RegressionTask trainer)

[x] vaihingen (#853)

[ ] xview2 (requires ChangeDetectionTask trainer)

[ ] utils (coverage for test_pct == None, do we need this?)

Alternatives

We may end up keeping some of these that test invalid arguments. The important thing is not that we don't test datamodules standalone, but that we test them with trainers whenever possible.

Additional information

No response
testing datamodules
opened by adamjstewart 0
Add Multi-Weight Support API
This PR closes #762 by adding support for loading pretrained weights from various sources, specifically for Earth Observational Data following torchvision.

Example:

from torchgeo.models import ResNet50_Weights from torchgeo.trainers import ClassificationTask task = ClassificationTask( classification_model="resnet50", loss="ce", num_classes=10, in_channels=3, weights=ResNet50_Weights.SENTINEL2_ALL_MOCO.get_state_dict() )

Some things I have encountered:

I think torchvision is able to make a cleaner API because all the available weights they have are exclusively for models they themselves implement in torchvision. In the Earth Observational Data case, the pretrained weights I have found come from a variety of sources but mostly use timm which aligns with torchgeo. However, there are sometimes some hoops to jump through before being able to actually load the state dict into the timm model. This is currently handled by get_state_dict()

With the few weights I have already run into issues with naming schemes discussed in #804 and it seems like with trying to collect pretrained weights from a variety of sources for specific use cases will make the proper naming scheme unclear, but maybe there is a more clever way to handle it with the accompanying meta data?

Questions:

where should the weights be integrated within the torchgeo file hierarchy

I am not sure what the best way of unit testing this is

documentation models testing trainers
opened by nilsleh 17

Releases(v0.3.1)

v0.3.1(Sep 8, 2022)
TorchGeo 0.3.1 Release Notes

This is a bugfix release. There are no new features or API changes with respect to the 0.3.0 release.

Dependencies

pytorch-lightning: add 1.9 support (#697, #771)

radiant-mlhub: 0.5 not yet supported (#711)

segmentation-models-pytorch: add 0.3 support (#692)

setuptools: add 65 support (#715, #753)

torchvision: fix 0.12 pretrained model support (#761)

DataModules

Fix rounding bugs in train/val/test split sizes (#675, #679, #736)

Datasets

Fix rounding bugs leading to inconsistent image shapes in vector datasets (#674, #675, #679, #736)

IDTReeS: fix (x, y) coordinate swap in boxes (#683, #684)

IDTReeS: clip boxes to bounds of image (#684, #760)

Sentinel-2: add support for files downloaded from USGS EarthExplorer (#505, #754)

Sentinel-2: prevent dataset from loading bands at different resolutions (#754)

Sentinel-2: support loading even when band B02 is not present (#754)

Samplers

GridGeoSampler: adjust stride of last row/col to sample entire ROI (#431, #448, #630)

Transforms

NDVI: fix computation, we were computing the negative (#713, #714)

SWI: fix band names (#714)

Documentation

API docs:

USAVars is a regression dataset (#699)

Tutorials:

Use IntersectionDataset in sampler (#707)

Custom Raster Datasets: complete overhaul with real data (#766, #772)

Trainers: optional datasets required (#759)

Transforms: replace cell magic with shell command (#756)

Transforms: fix GPU usage (#763, #767)

Clean up file names, execution counts, and output (#770)

Contributors

This release is thanks to the following contributors:

@adamjstewart

@ashnair1

@calebrob6

@isaaccorley

@remtav

@TCherici

Source code(tar.gz)
Source code(zip)
v0.3.0(Jul 11, 2022)
TorchGeo 0.3.0 Release Notes

This release contains a number of new features, and brings increased stability to installations and testing.

In previous releases, not all dependencies had a minimum supported version listed, causing issues if users had old versions lying around. Old releases would also install the latest version of all dependencies even if they had never been tested before. TorchGeo now lists a minimum and maximum supported version for all dependencies. Moreover, we now test the minimum supported versions of all dependencies. Dependencies are automatically updated using dependabot to prevent unrelated CI failures from sneaking into PRs. We hope this makes it even easier to contribute to TorchGeo, and ensures that old releases will continue to work even if our dependencies make backwards-incompatible changes.

Backwards-incompatible changes

VisionDataset and VisionClassificationDataset have been renamed to NonGeoDataset and NonGeoClassificationDataset (#627)

Sample size now defaults to pixel units, use units=Units.CRS for old behavior (#294)

RasterDataset no longer has a plot method, subclasses have their own plot methods (#476)

Plot method of RasterDataset subclasses now take sample dicts, not image tensors (#476)

Removed FCEF model, use segmentation_models_pytorch.Unet instead (#345)

SemanticSegmentationTrainer: ignore_zeros renamed to ignore_index (#444, #644)

Dependencies

Python 3.7+ is now required (#413, #482, #486)

Add lower version bounds to all dependencies based on testing (#574)

Add upper version bounds to all dependencies based on semver (#544, #557)

Fix Conda environment installation (#527, #528, #529, #545)

Datamodules

New datamodules:

Inria Aerial Image Labeling (#498)

USAVars (#441)

Changes to existing datamodules:

Improved consistency between datamodules (#657)

Datasets

New datasets:

Aboveground Live Woody Biomass Density (#425)

Aster GDEM (#404)

CMS Global Mangrove Canopy (#391, #427)

DeepGlobe (#578)

DFC 2022 (#354)

EDDMapS (#533)

EnviroAtlas (#364)

Esri 2020 Land Cover (#390, #405)

EU-DEM (#426)

Forest Damage (#461, #499)

GBIF (#507)

GlobBiomass (#395)

iNaturalist (#532)

Inria Aerial Image Labeling (#355)

Million-AID (#455)

OpenBuildings (#68, #402)

ReforesTree (#582)

SpaceNet 3 (#480)

USAVars (#363)

Changes to existing datasets:

Benin Small Holder Cashews: return geospatial metadata (#377)

BigEarthNet: fix checksum (#550)

CBF: add plot method (#410)

CDL: add 2021 download (#418)

CDL: add plot method (#415)

Chesapeake: add plot method (#417)

EuroSat: new bands parameter (#396, #397)

LandCover.ai: update download URL (#559, #579)

Landsat: add support for all Level-1 and Level-2 products (#492, #504)

Landsat: add plot method (#661)

NAIP: add plot method (#407)

Seasonal Contrast: ensure that all images are square (#658)

Sentinel: add plot method (#416, #493)

SEN12MS: avoid casting float to int (#500, #502)

So2Sat: new bands parameter (#394)

Base classes and utilities:

VisionDataset and VisionClassificationDataset have been renamed to NonGeoDataset and NonGeoClassificationDataset (#627)

RasterDataset no longer has a plot method, subclasses have their own plot methods (#476)

Plot method of RasterDataset subclasses now take sample dicts, not image tensors (#476)

BoundingBox has new area and volume attributes (#375)

Don't subtract microsecond from mint (#506)

Models

Changes to existing models:

Removed FCEF model, use segmentation_models_pytorch.Unet instead (#345)

FCSiamConf and FCSiamDiff now inherit from segmentation_models_pytorch.Unet, allowing for easily loading pretrained weights (#345)

Samplers

New samplers:

PreChippedGeoSampler (#479)

Changes to existing samplers:

Allow for point sampling (#477)

Allow for sampling of entire scene (#477)

RandomGeoSampler no longer suffers from area bias (#408, #477)

Sample size now defaults to pixel units, use units=Units.CRS for old behavior (#294)

Trainers

Changes to existing trainers:

BYOLTask: fix in_channels handling (#522)

BYOLTask: fix loading of encoder weights (#524)

SemanticSegmentationTask: ignore_zeros renamed to ignore_index (#444, #644)

Transforms

New spectral indices:

BNDVI (#386)

GBNDVI (#450)

GNDVI (#371)

GRNDVI (#450)

NDRE (#386)

RBNDVI (#450)

SWI (#371)

New base classes:

AppendTriBandNormalizedDifferenceIndex (#414)

Documentation

Improved README (#589, #626)

Add dataset tables (#435, #478, #649)

Shorter dataset/datamodule/model names (#569, #571)

Spectral indices now display mathematical equations (#400)

Fix NAIP download in tutorials (#526, #531)

Add issue templates on GitHub (#584, #590)

Clarify Windows conda installation (#581)

Public type hints (#508)

Tests

Test on Python 3.10 (#457)

Use dependabot to manage dependencies (#488, #551, #647)

Test minimum version of dependencies (#574)

Resolve and test for deprecation warnings (#567)

FCSiam tests no longer require internet access (#495, #497)

Contributors

This release is thanks to the following contributors:

@adamjstewart

@ashnair1

@calebrob6

@gaetanbahl

@iejMac

@isaaccorley

@khdlr

@MATRIX4284

@mehmetgunturkun

@nilsleh

@recursix

@remtav

@RitwikGupta

@saumyasinha

@weiji14

Source code(tar.gz)
Source code(zip)
v0.2.1(Mar 20, 2022)
TorchGeo 0.2.1 Release Notes

This is a bugfix release. There are no new features or API changes with respect to the 0.2.0 release.

Dependencies

Fix minimum supported kornia version (#350)

Support older pytorch-lightning (#347, #351)

Add support for torchmetrics 0.8+ (#361, #382)

DataModules

RESISC45: fix normalization statistics (#440)

Datasets

Fixes for dataset base classes:

GeoDataset: fix len() of empty dataset (#374)

RasterDataset: add support for float dtype (#379, #384)

RasterDataset: don't override custom cmap (#421, #422)

VectorDataset: fix issue with empty query (#399, #454, #467)

Fixes for specific datasets:

CDL: update checksums due to new file formats (#423, #424, #428)

Chesapeake: support extraction of deflate64-compressed zip files (#59, #282)

Chesapeake: allow multiple datasets to share same root (#419, #420)

ChesapeakeCVPR: update prior extension data to version 1.1 (#359)

IDTReeS: fix citation (#389)

LandCover.ai: support already-downloaded dataset (#383)

Sentinel-2: fix regex to support band 8A (#393)

SpaceNet 2: update checksum due to data format consistency fix (#469)

Samplers

Avoid bounding boxes smaller than patch size (#319, #376)

Tutorials

Fix variable name in trainer notebook (#434)

Tests

Fix integration tests on macOS/Windows (#349, #468)

Contributors

This release is thanks to the following contributors:

@adamjstewart

@ashnair1

@calebrob6

@ethanwhite

@nilsleh

@tritolol

@weiji14

Source code(tar.gz)
Source code(zip)
v0.2.0(Jan 2, 2022)
TorchGeo 0.2.0 Release Notes

This release contains a number of new features. The biggest change in this release is a significant overhaul of GeoDataset. It is now possible to intelligently compose multiple GeoDatasets in a variety of ways. For example, users can now:

Combine datasets for multiple image sources and treat them as equivalent (e.g. Landsat 7 and Landsat 8)

Combine datasets for disparate geospatial locations (e.g. Chesapeake NY and PA)

These combinations require that all queries are present in at least one dataset, and can be combined using a UnionDataset:

landsat7 = Landsat7(root="...") landsat8 = Landsat8(root="...", bands=["B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9"]) landsat = landsat7 | landsat8

Users can now also:

Combine image and target labels and sample from both simultaneously (e.g. Landsat and CDL)

Combine datasets for multiple image sources for multimodal learning or data fusion (e.g. Landsat and Sentinel)

These combinations require that all queries are present in both datasets, and can be combined using an IntersectionDataset:

cdl = CDL(root="...", download=True, checksum=True) dataset = landsat & cdl

If files are in different coordinate systems or at different spatial resolutions, TorchGeo now automatically warps all tiles to a common CRS and resolution. As before, all GeoDatasets are compatible with PyTorch DataLoaders using GeoSamplers.

Backwards-incompatible changes

TorchGeo is still in the alpha development phase and our API continues to change as needed. If you are using any of the following features, be sure to update your code to use the new API:

ZipDataset has been renamed to IntersectionDataset (#144)

GeoDataset no longer supports addition (+), use intersection (&) or union (|) instead (#144)

BoundingBox is no longer a subclass of tuple, but can still be cast to a tuple using tuple(bbox) (#144)

collate_dict has been renamed to stack_samples (#144)

Dataset-specific trainers have been removed, use task-specific trainers instead (#205, #286)

All DataModules have been moved from torchgeo.datasets to torchgeo.datamodules (#321)

Functional index transforms have been removed (#285)

Datamodules

This release adds a new torchgeo.datamodules namespace. All DataModules previously defined in torchgeo.datasets now live in torchgeo.datamodules.

In addition, the following datasets have new datamodules:

Chesapeake CVPR prior labels (#202)

ETCI 2021 (#234)

EuroSAT (#246)

FAIR1M (#232)

LoveDA (#270)

NASA Marine Debris (#269)

OSCD (#255, #257, #341)

Potsdam 2D (#247)

Vaihingen 2D (#248)

xView2 (#236)

Many datamodules now have a plot method that wraps around the respective dataset plot method (#286)

Datasets

This release includes many improvements for geospatial datasets:

New IntersectionDataset and UnionDataset classes (#144)

GeoDataset and BoundingBox now support set arithmetic (#144)

New collation functions for stacking, concatenating, merging, and unbinding samples (#144, #286, #328)

Chesapeake CVPR dataset now supports optional prior labels (#202)

This release also includes the following new benchmark datasets:

FAIR1M (#232)

IDTReeS (#201)

LoveDA (#270)

NASA Marine Debris (#269)

OSCD (#233, #254, #258)

Potsdam 2D (#247)

SpaceNet 5 (#263)

SpaceNet 7 (#241)

Vaihingen 2D (#248)

xView2 (#236)

Most existing datasets now have a plot method:

ADVANCE (#264)

Benin Small Holder Cashews (#264)

BigEarthNet (#264)

COWC (#300)

CV4A Kenya Crop Type (#312)

Cyclone (#298)

ETCI 2021 (#234)

EuroSAT (#251)

GID15 (#288)

LandCover.ai (#251)

LEVIR-CD+ (#335)

PatternNet (#314)

RESISC45 (#251)

SeCo (#251)

SEN12MS (#320, #338)

So2Sat (#251)

SpaceNet (#252, #311)

UCMerced (#251)

Zueri Crop (#334)

Losses

This release adds a new torchgeo.losses namespace for loss functions common in or exclusive to geospatial data.

QR and RQ losses (#202, #333)

Models

RCF now has a seed parameter (#193, #250)

Samplers

Samplers now respect ROI (#144, #149, #260)

Trainers

Trainers now plot samples during validation for supported datamodules (#286)

Dataset-specific trainers have been removed (#286)

Transforms

New AppendNormalizedDifferenceIndex transform (#285)

New normalized burn ratio transform (#284)

Documentation

New tutorial for writing custom RasterDatasets (#283)

Tutorials are now properly versioned (#274, #309, #310)

Tutorials now have an "Open in Planetary Computer" button (#316)

Minor updates to Indices tutorial (#339, #348)

Tests

Datamodules are now properly tested with real trainers (#329)

Tests no longer require internet access (#194, #265)

Tests now use significantly less memory (#344)

Contributors

This release is thanks to the following contributors:

@adamjstewart

@ashnair1

@calebrob6

@estherrolf

@iejMac

@isaaccorley

@nilsleh

@RitwikGupta

Source code(tar.gz)
Source code(zip)
v0.1.1(Dec 20, 2021)
TorchGeo 0.1.1 Release Notes

This is a bugfix release. There are no new features or API changes with respect to the 0.1.0 release.

Bug Fixes

Avoid circular import errors (#276)

Rework list of required dependencies (#249, #287)

Relax constraints on Conda environment (#293, #295)

Fix parallel data loading on macOS/Windows (#184, #304)

Fix bug in shuffling of ETCI 2021 dataset (#231)

Support already downloaded files in Chesapeake datasets (#281)

Tutorials now open the same file in Google Colab (#274, #309)

Add pre-trained ResNet models to the docs (#256)

Clean up tutorial imports (#267, #308)

Various improvements to CI stability (#261, #268, #292, #299, #306)

Contributors

This release is thanks to the following contributors:

@adamjstewart

@amrzv

@ashnair1

@calebrob6

@isaaccorley

Source code(tar.gz)
Source code(zip)
v0.1.0(Nov 8, 2021)
TorchGeo 0.1.0 Release Notes

This is the first official release of TorchGeo! This release contains the following features:

Datasets

Added the following new benchmark datasets:

ADVANCE (AuDio Visual Aerial sceNe reCognition datasEt) (#133)

Smallholder Cashew Plantations in Benin (#28)

BigEarthNet (#197, #211)

Cars Overhead With Context (COWC) (#25, #217)

CV4A Kenya Crop Type Competition (#22)

ETCI2021 Flood Detection (#119)

EuroSAT (#167)

GID-15 (Gaofen Image Dataset) (#123)

LandCover.ai (Land Cover from Aerial Imagery) (#19, #48)

LEVIR-CD+ (LEVIR Change Detection +) (#106)

PatternNet (#111)

RESISC45 (Remote Sensing Image Scene Classification) (#126, #179)

Seasonal Contrast (#223)

SEN12MS (#26, #44)

So2Sat (#34, #145)

SpaceNet (#129, #155, #185)

Tropical Cyclone Wind Estimation Competition (8305aa7c5ed0e3d6d823c7cd2d552b137969b35b)

NWPU VHR-10 (6df38094df157aacc4a3f1e9b388b927dcd3515d)

UC Merced (#169, #208)

ZueriCrop (#147)

Added the following new generic datasets:

Canadian Building Footprints (#69)

Chesapeake Bay High-Resolution Land Cover Project (#18, #100, #142)

Cropland Data Layer (CDL) (#37)

Landsat (#37)

National Agriculture Imagery Program (NAIP) (#57, #98)

Sentinel (#37)

Models

Added the following new models:

Change Star (#157)

Foreground-aware Relation Network (FarSeg) (#150)

Fully-convolutional Network (FCN) (#54)

Fully Convolutional Siamese Networks for Change Detection (#108)

Random-convolutional feature (RCF) extractor (#176)

Samplers

Added the following new samplers:

Random Geo Sampler (#37)

Grid Geo Sampler (#37)

Random Batch Geo Sampler (#37)

Trainers

Added the following new trainers:

BYOL (#145)

Classification (#207)

Multi-label Classification (#211)

Regression (#215)

Semantic Segmentation (#224)

Transforms

Added the following new transforms:

Indices: NDBI, NDSI, NDVI, NDWI (#127)

Docs

Added documentation for:

Installation (#153)

Contributing (#141)

Glossary (f6c91f4ebcfa7363bf421592fc36a0f523b9b5a4)

API Docs (00d619e9463ca223e29aa98e4658eb397dd74164)

Added tutorials for:

Getting Started (#93)

Transforms (#127)

Indices (#127)

PyTorch Lightning Trainers (#161)

Benchmarking (#93)

Contributors

This release is thanks to the following contributors:

@adamjstewart

@anthonymlortiz

@ashnair1

@calebrob6

@isaaccorley

@Z-Zheng

Source code(tar.gz)
Source code(zip)

Owner

Microsoft

Open source projects and samples from Microsoft

GitHub Repository

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TorchGeo is a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.

Related tags

Overview

Installation instructions

Conda

Pip

Spack

Documentation

Example usage

Train and test models using our PyTorch Lightning based training script

Download and use the Tropical Cyclone Wind Estimation Competition dataset

Contributing

Trademarks

Comments

Description

1. Coordinates are swapped

2. Missing polygon

3. Negative box coordinates in pixel space

Steps to reproduce

Version

Before

After

Summary

Rationale

Implementation

Alternatives

Additional information

Releases(v0.3.1)

v0.3.1(Sep 8, 2022)

TorchGeo 0.3.1 Release Notes

Dependencies

DataModules

Datasets

Samplers

Transforms

Documentation

Contributors

v0.3.0(Jul 11, 2022)

TorchGeo 0.3.0 Release Notes

Backwards-incompatible changes

Dependencies

Datamodules

Datasets

Models

Samplers

Trainers

Transforms

Documentation

Tests

Contributors

v0.2.1(Mar 20, 2022)

TorchGeo 0.2.1 Release Notes

Dependencies

DataModules

Datasets

Samplers

Tutorials

Tests

Contributors

v0.2.0(Jan 2, 2022)

TorchGeo 0.2.0 Release Notes

Backwards-incompatible changes

Datamodules

Datasets

Losses

Models

Samplers

Trainers

Transforms

Documentation

Tests

Contributors

v0.1.1(Dec 20, 2021)

TorchGeo 0.1.1 Release Notes

Bug Fixes

Contributors

v0.1.0(Nov 8, 2021)

TorchGeo 0.1.0 Release Notes

Datasets

Models