Add dataset table to the documentation (#435)

* Add benchmark dataset table

* Add geospatial datasets

* Work on Data table (#478)

* added to data table

* add links

* fix docs

* Added section for implementing new datasets to the Contributing page

* Removing extra file

* Add EDDMapS and GBIF rows to generic

* Formatting

* Renaming to make sense

* Short names

* Fixes

* Checking references

* Trying links

* Figured out links

* Removing hyphens for empty cells as these are rendered as bullet points

* Update docs/api/non_geo_datasets.csv

Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

* Update docs/api/non_geo_datasets.csv

Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

* Update docs/api/non_geo_datasets.csv

Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

* Update docs/api/non_geo_datasets.csv

Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

* Update docs/user/contributing.rst

Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

* Update docs/api/geo_datasets.csv

* Update geo_datasets.csv

* Update geo_datasets.csv

* Update contributing.rst

* Formatting

* Fix table links

Co-authored-by: Nils Lehmann <35272119+nilsleh@users.noreply.github.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

docs/api/datasets.rst

@@ -12,6 +12,12 @@ Geospatial Datasets
 
 :class:`GeoDataset` is designed for datasets that contain geospatial information, like latitude, longitude, coordinate system, and projection. Datasets containing this kind of information can be combined using :class:`IntersectionDataset` and :class:`UnionDataset`.
 
+.. csv-table::
+   :widths: 30 15 20 20 15
+   :header-rows: 1
+   :align: center
+   :file: geo_datasets.csv
+
 Aboveground Woody Biomass
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -125,6 +131,12 @@ Non-geospatial Datasets
 
 :class:`VisionDataset` is designed for datasets that lack geospatial information. These datasets can still be combined using :class:`ConcatDataset <torch.utils.data.ConcatDataset>`.
 
+.. csv-table:: C = classification,  R = regression, S = semantic segmentation, I = instance segmentation, T = time series, CD = change detection, OD = object detection
+   :widths: 15 7 15 12 11 12 15 13
+   :header-rows: 1
+   :align: center
+   :file: non_geo_datasets.csv
+
 ADVANCE
 ^^^^^^^
 

docs/api/geo_datasets.csv

@@ -0,0 +1,17 @@
+Dataset,Type,Source,Size (px),Resolution (m)
+`Aboveground Woody Biomass`_,Mask,"Landsat, LiDAR","~40,000x40,000",~30
+`Aster Global DEM`_,Mask,Aster,"3,601x3,601",30
+`Canadian Building Footprints`_,Labels,Generated,,
+`Chesapeake Land Cover`_,"Imagery, Labels",,,1
+`Global Mangrove Distribution`_,Mask,Generated,,3
+`Cropland Data Layer`_,Labels,Aerial,,
+`EDDMapS`_,Labels,Vector,,
+`EnviroAtlas`_,"Imagery, Labels",Aerial,,1
+`Esri2020`_,Labels,Sentinel-2,,10
+`EU-DEM`_,Labels,"Aster, SRTM, Russian Topomaps",,25
+`GBIF`_,Labels,Vector,,
+`GlobBiomass`_,Labels,Landsat,"45,000x45,000",~100
+`Landsat`_,Imagery,Landsat,,30
+`NAIP`_,Imagery,Aerial,,1
+`Open Buildings`_,Labels,Generated,,
+`Sentinel`_,Imagery,Sentinel,,10

docs/api/non_geo_datasets.csv

@@ -0,0 +1,33 @@
+Dataset,Task,Source,# Samples,# Classes,Size (px),Resolution (m),Bands
+`ADVANCE`_,C,"Google Earth, Freesound","5,075",13,512x512,0.5,RGB
+`Benin Cashew Plantations`_,S,Airbus Pléiades,70,6,"1,186x1,122",0.5,MSI
+`BigEarthNet`_,C,Sentinel-1/2,"590,326",19--43,120x120,10,"SAR, MSI"
+`COWC`_,"C, R","CSUAV AFRL, ISPRS, LINZ, AGRC","388,435",2,256x256,0.15,RGB
+`Kenya Crop Type`_,S,Sentinel-2,"4,688",7,"3,035x2,016",10,MSI
+`DFC2022`_,S,Aerial,,15,"2,000x2,000",0.5,RGB
+`ETCI2021 Flood Detection`_,S,Sentinel-1,"66,810",2,256x256,5--20,SAR
+`EuroSAT`_,C,Sentinel-2,"27,000",10,64x64,10,MSI
+`FAIR1M`_,OD,Gaofen/Google Earth,"15,000",37,"1,024x1,024",0.3--0.8,RGB
+`Forest Damage`_,OD,Drone imagery,"1,543",4,"1,500x1,500",,RGB
+`GID-15`_,S,Gaofen-2,150,15,"6,800x7,200",3,RGB
+`IDTReeS`_,"OD,C",Aerial,591,33,200x200,0.1--1,RGB
+`Inria Aerial Image Labeling`_,O,Aerial,360,,"5,000x5,000",0.3,RGB
+`LandCover.ai`_,S,Aerial,"10,674",5,512x512,0.25--0.5,RGB
+`LEVIR-CD+`_,CD,Google Earth,985,2,"1,024x1,024",0.5,RGB
+`LoveDA`_,S,Google Earth,"5,987",7,"1,024x1,024",0.3,RGB
+`NASA Marine Debris`_,OD,PlanetScope,707,1,256x256,3,RGB
+`NWPU VHR-10`_,I,"Google Earth, Vaihingen",800,10,"358--1,728",0.08--2,RGB
+`OSCD`_,CD,Sentinel-2,24,2,"40--1,180",60,MSI
+`PatternNet`_,C,Google Earth,"30,400",38,256x256,0.06--5,RGB
+`Potsdam`_,S,Aerial,38,6,"6,000x6,000",0.05,MSI
+`RESISC45`_,C,Google Earth,"31,500",45,256x256,0.2--30,RGB
+`Seasonal Contrast`_,T,Sentinel-2,100K--1M,,264x264,10,MSI
+`SEN12MS`_,S,"Sentinel-1/2, MODIS","180,662",33,256x256,10,"SAR, MSI"
+`So2Sat`_,C,Sentinel-1/2,"400,673",17,32x32,10,"SAR, MSI"
+`SpaceNet`_,I,WorldView-2/3 Planet Lab Dove,"1,889--28,728",2,102--900,0.5--4,MSI
+`Tropical Cyclone`_,R,GOES 8--16,"108,110",,256x256,4K--8K,MSI
+`UC Merced`_,C,USGS National Map,"21,000",21,256x256,0.3,RGB
+`USAVars`_,S,NAIP Aerial,~100K,,,4,"RGB, NIR"
+`Vaihingen`_,S,Aerial,33,6,"1,281--3,816",0.09,RGB
+`xView2`_,CD,Maxar,"3,732",4,"1,024x1,024",0.8,RGB
+`ZueriCrop`_,"I, T",Sentinel-2,116K,48,24x24,10,MSI

docs/user/contributing.rst

@@ -158,3 +158,18 @@ TorchGeo has a number of tutorials included in the documentation that can be run
 .. code-block:: console
 
    $ pytest --nbmake docs/tutorials
+
+
+Datasets
+--------
+
+A major component of TorchGeo is the large collection of :mod:`torchgeo.datasets` that have been implemented. Adding new datasets to this list is a great way to contribute to the library. A brief checklist to follow when implementing a new dataset:
+
+* Implement the dataset extending either :class:`~torchgeo.datasets.GeoDataset` or :class:`~torchgeo.datasets.VisionDataset`
+* Add the dataset definition to ``torchgeo/datasets/__init__.py``
+* Add a ``data.py`` script to ``tests/data/<new dataset>/`` that generates test data with the same directory structure/file naming conventions as the new dataset
+* Add appropriate tests with 100% test coverage to ``tests/datasets/``
+* Add the dataset to ``docs/api/datasets.rst``
+* Add the dataset metadata to either ``docs/api/geo_datasets.csv`` or ``docs/api/non_geo_datasets.csv``
+
+A good way to get started is by looking at some of the existing implementations that are most closely related to the dataset that you are implementing (e.g. if you are implementing a semantic segmentation dataset, looking at the LandCover.ai dataset implementation would be a good starting point).

torchgeo/datasets/advance.py

@@ -20,7 +20,7 @@ from .utils import download_and_extract_archive
 class ADVANCE(VisionDataset):
     """ADVANCE dataset.
 
-    The `ADVANCE <https://akchen.github.io/ADVANCE-DATASET/>`_
+    The `ADVANCE <https://akchen.github.io/ADVANCE-DATASET/>`__
     dataset is a dataset for audio visual scene recognition.
 
     Dataset features:

torchgeo/datasets/bigearthnet.py

@@ -22,7 +22,7 @@ from .utils import download_url, extract_archive, sort_sentinel2_bands
 class BigEarthNet(VisionDataset):
     """BigEarthNet dataset.
 
-    The `BigEarthNet <https://bigearth.net/>`_
+    The `BigEarthNet <https://bigearth.net/>`__
     dataset is a dataset for multilabel remote sensing image scene classification.
 
     Dataset features:

torchgeo/datasets/cbf.py

@@ -17,7 +17,7 @@ class CanadianBuildingFootprints(VectorDataset):
     """Canadian Building Footprints dataset.
 
     The `Canadian Building Footprints
-    <https://github.com/Microsoft/CanadianBuildingFootprints>`_ dataset contains
+    <https://github.com/Microsoft/CanadianBuildingFootprints>`__ dataset contains
     11,842,186 computer generated building footprints in all Canadian provinces and
     territories in GeoJSON format. This data is freely available for download and use.
     """

torchgeo/datasets/cdl.py

@@ -19,7 +19,7 @@ class CDL(RasterDataset):
     """Cropland Data Layer (CDL) dataset.
 
     The `Cropland Data Layer
-    <https://data.nal.usda.gov/dataset/cropscape-cropland-data-layer>`_, hosted on
+    <https://data.nal.usda.gov/dataset/cropscape-cropland-data-layer>`__, hosted on
     `CropScape <https://nassgeodata.gmu.edu/CropScape/>`_, provides a raster,
     geo-referenced, crop-specific land cover map for the continental United States. The
     CDL also includes a crop mask layer and planting frequency layers, as well as

torchgeo/datasets/dfc2022.py

@@ -22,7 +22,7 @@ from .utils import check_integrity, extract_archive, percentile_normalization
 class DFC2022(VisionDataset):
     """DFC2022 dataset.
 
-    The `DFC2022 <https://www.grss-ieee.org/community/technical-committees/2022-ieee-grss-data-fusion-contest/>`_
+    The `DFC2022 <https://www.grss-ieee.org/community/technical-committees/2022-ieee-grss-data-fusion-contest/>`__
     dataset is used as a benchmark dataset for the 2022 IEEE GRSS Data Fusion Contest
     and extends the MiniFrance dataset for semi-supervised semantic segmentation.
     The dataset consists of a train set containing labeled and unlabeled imagery and an

torchgeo/datasets/eddmaps.py

@@ -17,7 +17,7 @@ from .utils import BoundingBox, disambiguate_timestamp
 class EDDMapS(GeoDataset):
     """Dataset for EDDMapS.
 
-    `EDDMapS <https://www.eddmaps.org/>`_, Early Detection and Distribution Mapping
+    `EDDMapS <https://www.eddmaps.org/>`__, Early Detection and Distribution Mapping
     System, is a web-based mapping system for documenting invasive species and pest
     distribution. Launched in 2005 by the Center for Invasive Species and Ecosystem
     Health at the University of Georgia, it was originally designed as a tool for

torchgeo/datasets/enviroatlas.py

@@ -27,7 +27,7 @@ class EnviroAtlas(GeoDataset):
     """EnviroAtlas dataset covering four cities with prior and weak input data layers.
 
     The `EnviroAtlas
-    <https://doi.org/10.5281/zenodo.5778192>`_ dataset contains NAIP aerial imagery,
+    <https://doi.org/10.5281/zenodo.5778192>`__ dataset contains NAIP aerial imagery,
     NLCD land cover labels, OpenStreetMap roads, water, waterways, and waterbodies,
     Microsoft building footprint labels, high-resolution land cover labels from the
     EPA EnviroAtlas dataset, and high-resolution land cover prior layers.

torchgeo/datasets/eudem.py

@@ -18,7 +18,7 @@ class EUDEM(RasterDataset):
     """European Digital Elevation Model (EU-DEM) Dataset.
 
     The `EU-DEM
-    <https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1?tab=mapview>`_
+    <https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1?tab=mapview>`__
     dataset is a Digital Elevation Model of reference for the entire European region.
     The dataset can be downloaded from this `website
     <https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1?tab=mapview>`_

torchgeo/datasets/eurosat.py

@@ -18,7 +18,7 @@ from .utils import check_integrity, download_url, extract_archive, rasterio_load
 class EuroSAT(VisionClassificationDataset):
     """EuroSAT dataset.
 
-    The `EuroSAT <https://github.com/phelber/EuroSAT>`_ dataset is based on Sentinel-2
+    The `EuroSAT <https://github.com/phelber/EuroSAT>`__ dataset is based on Sentinel-2
     satellite images covering 13 spectral bands and consists of 10 target classes with
     a total of 27,000 labeled and geo-referenced images.
 

torchgeo/datasets/fair1m.py

@@ -53,7 +53,7 @@ def parse_pascal_voc(path: str) -> Dict[str, Any]:
 class FAIR1M(VisionDataset):
     """FAIR1M dataset.
 
-    The `FAIR1M <http://gaofen-challenge.com/benchmark>`_
+    The `FAIR1M <http://gaofen-challenge.com/benchmark>`__
     dataset is a dataset for remote sensing fine-grained oriented object detection.
 
     Dataset features:

torchgeo/datasets/gbif.py

@@ -58,7 +58,7 @@ def _disambiguate_timestamps(
 class GBIF(GeoDataset):
     """Dataset for the Global Biodiversity Information Facility.
 
-    `GBIF <https://www.gbif.org/>`_, the Global Biodiversity Information Facility,
+    `GBIF <https://www.gbif.org/>`__, the Global Biodiversity Information Facility,
     is an international network and data infrastructure funded by the world's
     governments and aimed at providing anyone, anywhere, open access to data about
     all types of life on Earth.

torchgeo/datasets/gid15.py

@@ -20,7 +20,7 @@ from .utils import download_and_extract_archive
 class GID15(VisionDataset):
     """GID-15 dataset.
 
-    The `GID-15 <https://captain-whu.github.io/GID15/>`_
+    The `GID-15 <https://captain-whu.github.io/GID15/>`__
     dataset is a dataset for semantic segmentation.
 
     Dataset features:

torchgeo/datasets/idtrees.py

@@ -23,7 +23,7 @@ from .utils import download_url, extract_archive
 class IDTReeS(VisionDataset):
     """IDTReeS dataset.
 
-    The `IDTReeS <https://idtrees.org/competition/>`_
+    The `IDTReeS <https://idtrees.org/competition/>`__
     dataset is a dataset for tree crown detection.
 
     Dataset features:

torchgeo/datasets/inria.py

@@ -22,7 +22,7 @@ class InriaAerialImageLabeling(VisionDataset):
     r"""Inria Aerial Image Labeling Dataset.
 
     The `Inria Aerial Image Labeling
-    <https://project.inria.fr/aerialimagelabeling/>`_ dataset is a building
+    <https://project.inria.fr/aerialimagelabeling/>`__ dataset is a building
     detection dataset over dissimilar settlements ranging ranging from densely
     populated areas to alpine towns. Refer to the dataset homepage to download
     the dataset.

torchgeo/datasets/landcoverai.py

@@ -23,7 +23,7 @@ from .utils import download_url, extract_archive, working_dir
 class LandCoverAI(VisionDataset):
     r"""LandCover.ai dataset.
 
-    The `LandCover.ai <https://landcover.ai/>`_ (Land Cover from Aerial Imagery)
+    The `LandCover.ai <https://landcover.ai/>`__ (Land Cover from Aerial Imagery)
     dataset is a dataset for automatic mapping of buildings, woodlands, water and
     roads from aerial images. This implementation is specifically for Version 1 of
     Landcover.ai.

torchgeo/datasets/landsat.py

@@ -14,7 +14,7 @@ from .geo import RasterDataset
 class Landsat(RasterDataset, abc.ABC):
     """Abstract base class for all Landsat datasets.
 
-    `Landsat <https://landsat.gsfc.nasa.gov/>`_ is a joint NASA/USGS program,
+    `Landsat <https://landsat.gsfc.nasa.gov/>`__ is a joint NASA/USGS program,
     providing the longest continuous space-based record of Earth's land in existence.
 
     If you use this dataset in your research, please cite it using the following format:

torchgeo/datasets/levircd.py

@@ -20,7 +20,7 @@ from .utils import download_and_extract_archive
 class LEVIRCDPlus(VisionDataset):
     """LEVIR-CD+ dataset.
 
-    The `LEVIR-CD+ <https://github.com/S2Looking/Dataset>`_
+    The `LEVIR-CD+ <https://github.com/S2Looking/Dataset>`__
     dataset is a dataset for building change detection.
 
     Dataset features:

torchgeo/datasets/loveda.py

@@ -20,7 +20,7 @@ from .utils import download_and_extract_archive
 class LoveDA(VisionDataset):
     """LoveDA dataset.
 
-    The `LoveDA <https://github.com/Junjue-Wang/LoveDA>`_ datataset is a
+    The `LoveDA <https://github.com/Junjue-Wang/LoveDA>`__ datataset is a
     semantic segmentation dataset.
 
     Dataset features:

torchgeo/datasets/nasa_marine_debris.py

@@ -20,7 +20,7 @@ from .utils import download_radiant_mlhub_dataset, extract_archive
 class NASAMarineDebris(VisionDataset):
     """NASA Marine Debris dataset.
 
-    The `NASA Marine Debris <https://mlhub.earth/data/nasa_marine_debris>`_
+    The `NASA Marine Debris <https://mlhub.earth/data/nasa_marine_debris>`__
     dataset is a dataset for detection of floating marine debris in satellite imagery.
 
     Dataset features:

torchgeo/datasets/openbuildings.py

@@ -27,7 +27,7 @@ class OpenBuildings(VectorDataset):
     r"""Open Buildings dataset.
 
     The `Open Buildings
-    <https://sites.research.google/open-buildings/#download>`_ dataset
+    <https://sites.research.google/open-buildings/#download>`__ dataset
     consists of computer generated building detections across the African continent.
 
     Dataset features:

torchgeo/datasets/patternnet.py

@@ -16,7 +16,7 @@ from .utils import download_url, extract_archive
 class PatternNet(VisionClassificationDataset):
     """PatternNet dataset.
 
-    The `PatternNet <https://sites.google.com/view/zhouwx/dataset>`_
+    The `PatternNet <https://sites.google.com/view/zhouwx/dataset>`__
     dataset is a dataset for remote sensing scene classification and image retrieval.
 
     Dataset features:

torchgeo/datasets/potsdam.py

@@ -26,7 +26,7 @@ from .utils import (
 class Potsdam2D(VisionDataset):
     """Potsdam 2D Semantic Segmentation dataset.
 
-    The `Potsdam <https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-potsdam/>`_
+    The `Potsdam <https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-potsdam/>`__
     dataset is a dataset for urban semantic segmentation used in the 2D Semantic Labeling
     Contest - Potsdam. This dataset uses the "4_Ortho_RGBIR.zip" and "5_Labels_all.zip"
     files to create the train/test sets used in the challenge. The dataset can be

torchgeo/datasets/resisc45.py

@@ -17,7 +17,7 @@ from .utils import download_url, extract_archive
 class RESISC45(VisionClassificationDataset):
     """RESISC45 dataset.
 
-    The `RESISC45 <http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html>`_
+    The `RESISC45 <http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html>`__
     dataset is a dataset for remote sensing image scene classification.
 
     Dataset features:

torchgeo/datasets/sen12ms.py

@@ -19,7 +19,7 @@ from .utils import check_integrity, percentile_normalization
 class SEN12MS(VisionDataset):
     """SEN12MS dataset.
 
-    The `SEN12MS <https://doi.org/10.14459/2019mp1474000>`_ dataset contains
+    The `SEN12MS <https://doi.org/10.14459/2019mp1474000>`__ dataset contains
     180,662 patch triplets of corresponding Sentinel-1 dual-pol SAR data,
     Sentinel-2 multi-spectral images, and MODIS-derived land cover maps.
     The patches are distributed across the land masses of the Earth and

torchgeo/datasets/sentinel.py

@@ -16,7 +16,7 @@ from .geo import RasterDataset
 class Sentinel(RasterDataset):
     """Abstract base class for all Sentinel datasets.
 
-    `Sentinel <https://sentinel.esa.int/web/sentinel/home>`_ is a family of
+    `Sentinel <https://sentinel.esa.int/web/sentinel/home>`__ is a family of
     satellites launched by the `European Space Agency (ESA) <https://www.esa.int/>`_
     under the `Copernicus Programme <https://www.copernicus.eu/en>`_.
 

torchgeo/datasets/so2sat.py

@@ -18,7 +18,7 @@ from .utils import check_integrity, percentile_normalization
 class So2Sat(VisionDataset):
     """So2Sat dataset.
 
-    The `So2Sat <https://doi.org/10.1109/MGRS.2020.2964708>`_ dataset consists of
+    The `So2Sat <https://doi.org/10.1109/MGRS.2020.2964708>`__ dataset consists of
     corresponding synthetic aperture radar and multispectral optical image data
     acquired by the Sentinel-1 and Sentinel-2 remote sensing satellites, and a
     corresponding local climate zones (LCZ) label. The dataset is distributed over

torchgeo/datasets/spacenet.py

@@ -36,7 +36,7 @@ from .utils import (
 class SpaceNet(VisionDataset, abc.ABC):
     """Abstract base class for the SpaceNet datasets.
 
-    The `SpaceNet <https://spacenet.ai/datasets/>`_ datasets are a set of
+    The `SpaceNet <https://spacenet.ai/datasets/>`__ datasets are a set of
     datasets that all together contain >11M building footprints and ~20,000 km
     of road labels mapped over high-resolution satellite imagery obtained from
     a variety of sensors such as Worldview-2, Worldview-3 and Dove.

torchgeo/datasets/ucmerced.py

@@ -16,7 +16,7 @@ from .utils import check_integrity, download_url, extract_archive
 class UCMerced(VisionClassificationDataset):
     """UC Merced dataset.
 
-    The `UC Merced <http://weegee.vision.ucmerced.edu/datasets/landuse.html>`_
+    The `UC Merced <http://weegee.vision.ucmerced.edu/datasets/landuse.html>`__
     dataset is a land use classification dataset of 2.1k 256x256 1ft resolution RGB
     images of urban locations around the U.S. extracted from the USGS National Map Urban
     Area Imagery collection with 21 land use classes (100 images per class).

torchgeo/datasets/vaihingen.py

@@ -25,7 +25,7 @@ from .utils import (
 class Vaihingen2D(VisionDataset):
     """Vaihingen 2D Semantic Segmentation dataset.
 
-    The `Vaihingen <https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-vaihingen/>`_
+    The `Vaihingen <https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-vaihingen/>`__
     dataset is a dataset for urban semantic segmentation used in the 2D Semantic Labeling
     Contest - Vaihingen. This dataset uses the "ISPRS_semantic_labeling_Vaihingen.zip"
     and "ISPRS_semantic_labeling_Vaihingen_ground_truth_COMPLETE.zip" files to create the

torchgeo/datasets/xview.py

@@ -20,7 +20,7 @@ from .utils import check_integrity, draw_semantic_segmentation_masks, extract_ar
 class XView2(VisionDataset):
     """xView2 dataset.
 
-    The `xView2 <https://xview2.org/>`_
+    The `xView2 <https://xview2.org/>`__
     dataset is a dataset for building disaster change detection. This dataset object
     uses the "Challenge training set (~7.8 GB)" and "Challenge test set (~2.6 GB)" data
     from the xView2 website as the train and test splits. Note, the xView2 website

torchgeo/datasets/zuericrop.py

@@ -17,7 +17,7 @@ from .utils import download_url, percentile_normalization
 class ZueriCrop(VisionDataset):
     """ZueriCrop dataset.
 
-    The `ZueriCrop <https://github.com/0zgur0/ms-convSTAR>`_
+    The `ZueriCrop <https://github.com/0zgur0/ms-convSTAR>`__
     dataset is a dataset for time-series instance segmentation of crops.
 
     Dataset features: