Fix broken URLs

docs/index.rst

@@ -45,4 +45,4 @@ torchgeo
    torchvision <https://pytorch.org/vision>
    TorchElastic <https://pytorch.org/elastic/>
    TorchServe <https://pytorch.org/serve>
-   PyTorch on XLA Devices <http://pytorch.org/xla/>
+   PyTorch on XLA Devices <https://pytorch.org/xla/>

docs/tutorials/benchmarking.ipynb

@@ -85,7 +85,7 @@
    "source": [
     "## Datasets\n",
     "\n",
-    "For this tutorial, we'll be using imagery from the [National Agriculture Imagery Program (NAIP)](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) and labels from the [Chesapeake Bay High-Resolution Land Cover Project](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/). First, we manually download a few NAIP tiles."
+    "For this tutorial, we'll be using imagery from the [National Agriculture Imagery Program (NAIP)](https://catalog.data.gov/dataset/national-agriculture-imagery-program-naip) and labels from the [Chesapeake Bay High-Resolution Land Cover Project](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/). First, we manually download a few NAIP tiles."
    ]
   },
   {

docs/tutorials/getting_started.ipynb

@@ -86,7 +86,7 @@
    "source": [
     "## Datasets\n",
     "\n",
-    "For this tutorial, we'll be using imagery from the [National Agriculture Imagery Program (NAIP)](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) and labels from the [Chesapeake Bay High-Resolution Land Cover Project](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/). First, we manually download a few NAIP tiles and create a PyTorch Dataset."
+    "For this tutorial, we'll be using imagery from the [National Agriculture Imagery Program (NAIP)](https://catalog.data.gov/dataset/national-agriculture-imagery-program-naip) and labels from the [Chesapeake Bay High-Resolution Land Cover Project](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/). First, we manually download a few NAIP tiles and create a PyTorch Dataset."
    ]
   },
   {

docs/tutorials/indices.ipynb

@@ -29,7 +29,7 @@
     "In this tutorial, we demonstrate how to use TorchGeo's functions and transforms for computing popular indices used in remote sensing and provide examples of how to utilize them for analyzing raw imagery or simply for visualization purposes. Some common indices and their formulas can be found at the following links:\n",
     "\n",
     "- [Index Database](https://www.indexdatabase.de/db/i.php)\n",
-    "- [Awesome Spectral Indices](https://github.com/davemlz/awesome-spectral-indices)\n",
+    "- [Awesome Spectral Indices](https://github.com/awesome-spectral-indices/awesome-spectral-indices)\n",
     "\n",
     "It's recommended to run this notebook on Google Colab if you don't have your own GPU. Click the \"Open in Colab\" button above to get started."
    ]

docs/tutorials/trainers.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "# PyTorch Lightning Trainers\n",
     "\n",
-    "In this tutorial, we demonstrate TorchGeo trainers to train and test a model. Specifically, we use the [Tropical Cyclone dataset](https://torchgeo.readthedocs.io/en/latest/api/datasets.html#tropical-cyclone-wind-estimation-competition) and train models to predict cyclone wind speed given imagery of the cyclone. \n",
+    "In this tutorial, we demonstrate TorchGeo trainers to train and test a model. Specifically, we use the [Tropical Cyclone dataset](https://torchgeo.readthedocs.io/en/latest/api/datasets.html#tropical-cyclone) and train models to predict cyclone wind speed given imagery of the cyclone.\n",
     "\n",
     "It's recommended to run this notebook on Google Colab if you don't have your own GPU. Click the \"Open in Colab\" button above to get started."
    ]

docs/user/contributing.rst

@@ -33,7 +33,7 @@ For changes to Python code, you'll need to ensure that your code is :ref:`well-t
 Licensing
 ---------
 
-TorchGeo is licensed under the MIT License. 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.
+TorchGeo is licensed under the MIT License. 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://opensource.microsoft.com/cla/.
 
 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.
 
@@ -88,7 +88,7 @@ These tests require `pytest <https://docs.pytest.org/>`_ and `pytest-cov <https:
 Linters
 -------
 
-In order to remain `PEP-8 <https://www.python.org/dev/peps/pep-0008/>`_ compliant and maintain a high-quality codebase, we use several linting tools:
+In order to remain `PEP-8 <https://peps.python.org/pep-0008/>`_ compliant and maintain a high-quality codebase, we use several linting tools:
 
 * `black <https://black.readthedocs.io/>`_ for code formatting
 * `isort <https://pycqa.github.io/isort/>`_ for import ordering

torchgeo/datasets/chesapeake.py

@@ -36,15 +36,6 @@ class Chesapeake(RasterDataset, abc.ABC):
     Center (CIC) in partnership with the University of Vermont and WorldView Solutions,
     Inc. It consists of one-meter resolution land cover information for the Chesapeake
     Bay watershed (~100,000 square miles of land).
-
-    For more information, see:
-
-    * `User Guide
-      <https://chesapeakeconservancy.org/wp-content/uploads/2017/01/LandCover101Guide.pdf>`_
-    * `Class Descriptions
-      <https://chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf>`_
-    * `Accuracy Assessment
-      <https://chesapeakeconservancy.org/wp-content/uploads/2017/01/Chesapeake_Conservancy_Accuracy_Assessment_Methodology.pdf>`_
     """
 
     is_image = False
@@ -415,7 +406,7 @@ class ChesapeakeCVPR(GeoDataset):
     additional layer of data to this dataset containing a prior over the Chesapeake Bay
     land cover classes generated from the NLCD land cover labels. For more information
     about this layer see `the dataset documentation
-    <https://zenodo.org/record/5652512#.YcuAIZLMIQ8>`_.
+    <https://zenodo.org/record/5866525>`_.
 
     If you use this dataset in your research, please cite the following paper:
 

torchgeo/datasets/cv4a_kenya_crop_type.py

@@ -23,7 +23,7 @@ class CV4AKenyaCropType(NonGeoDataset):
     """CV4A Kenya Crop Type dataset.
 
     Used in a competition in the Computer NonGeo for Agriculture (CV4A) workshop in
-    ICLR 2020. See `this website <https://registry.mlhub.earth/10.34911/rdnt.dw605x/>`__
+    ICLR 2020. See `this website <https://mlhub.earth/10.34911/rdnt.dw605x>`__
     for dataset details.
 
     Consists of 4 tiles of Sentinel 2 imagery from 13 different points in time.

torchgeo/datasets/enviroatlas.py

@@ -35,7 +35,7 @@ class EnviroAtlas(GeoDataset):
     This dataset was organized to accompany the 2022 paper, `"Resolving label
     uncertainty with implicit generative models"
     <https://openreview.net/forum?id=AEa_UepnMDX>`_. More details can be found at
-    https://github.com/estherrolf/qr_for_landcover.
+    https://github.com/estherrolf/implicit-posterior.
 
     If you use this dataset in your research, please cite the following paper:
 

torchgeo/datasets/eudem.py

@@ -31,7 +31,7 @@ class EUDEM(RasterDataset):
     * vertical accuracy of +/- 7 m RMSE
     * data fused from `ASTER GDEM
       <https://lpdaac.usgs.gov/news/nasa-and-meti-release-aster-global-dem-version-3/>`_,
-      `SRTM <https://www2.jpl.nasa.gov/srtm/>`_ and Russian topomaps
+      `SRTM <https://science.jpl.nasa.gov/projects/srtm/>`_ and Russian topomaps
 
     Dataset format:
 

torchgeo/datasets/forestdamage.py

@@ -71,7 +71,7 @@ class ForestDamage(NonGeoDataset):
 
     * images are three-channel jpgs
     * annotations are in `Pascal VOC XML format
-      <https://roboflow.com/formats/pascal-voc-xml#w-tabs-0-data-w-pane-3>`_
+      <https://roboflow.com/formats/pascal-voc-xml>`_
 
     Dataset Classes:
 

torchgeo/datasets/naip.py

@@ -14,7 +14,7 @@ class NAIP(RasterDataset):
     """National Agriculture Imagery Program (NAIP) dataset.
 
     The `National Agriculture Imagery Program (NAIP)
-    <https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/>`_
+    <https://catalog.data.gov/dataset/national-agriculture-imagery-program-naip>`_
     acquires aerial imagery during the agricultural growing seasons in the continental
     U.S. A primary goal of the NAIP program is to make digital ortho photography
     available to governmental agencies and the public within a year of acquisition.

torchgeo/datasets/potsdam.py

@@ -26,7 +26,7 @@
 class Potsdam2D(NonGeoDataset):
     """Potsdam 2D Semantic Segmentation dataset.
 
-    The `Potsdam <https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-potsdam/>`__
+    The `Potsdam <https://www.isprs.org/education/benchmarks/UrbanSemLab/2d-sem-label-potsdam.aspx>`__
     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 @@
 class RESISC45(NonGeoClassificationDataset):
     """NWPU-RESISC45 dataset.
 
-    The `RESISC45 <http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html>`__
+    The `RESISC45 <https://doi.org/10.1109/jproc.2017.2675998>`__
     dataset is a dataset for remote sensing image scene classification.
 
     Dataset features:

torchgeo/datasets/seco.py

@@ -21,7 +21,7 @@
 class SeasonalContrastS2(NonGeoDataset):
     """Sentinel 2 imagery from the Seasonal Contrast paper.
 
-    The `Seasonal Contrast imagery <https://github.com/ElementAI/seasonal-contrast/>`_
+    The `Seasonal Contrast imagery <https://github.com/ServiceNow/seasonal-contrast>`_
     dataset contains Sentinel 2 imagery patches sampled from different points in time
     around the 10k most populated cities on Earth.
 

torchgeo/datasets/sen12ms.py

@@ -55,7 +55,7 @@ class SEN12MS(NonGeoDataset):
 
           for split in train test
           do
-              wget "https://raw.githubusercontent.com/schmitt-muc/SEN12MS/master/splits/${split}_list.txt"
+              wget "https://raw.githubusercontent.com/schmitt-muc/SEN12MS/3a41236a28d08d253ebe2fa1a081e5e32aa7eab4/splits/${split}_list.txt"
           done
 
        or manually downloaded from https://dataserv.ub.tum.de/s/m1474000

torchgeo/datasets/vhr10.py

@@ -159,10 +159,7 @@ class VHR10(NonGeoDataset):
         "md5": "d30a7ff99d92123ebb0b3a14d9102081",
     }
     target_meta = {
-        "url": (
-            "https://raw.githubusercontent.com/chaozhong2010/VHR-10_dataset_coco/"
-            "master/NWPU%20VHR-10_dataset_coco/annotations.json"
-        ),
+        "url": "https://raw.githubusercontent.com/chaozhong2010/VHR-10_dataset_coco/ce0ba0f5f6a0737031f1cbe05e785ddd5ef05bd7/NWPU%20VHR-10_dataset_coco/annotations.json",  # noqa: E501
         "filename": "annotations.json",
         "md5": "7c76ec50c17a61bb0514050d20f22c08",
     }

torchgeo/datasets/zuericrop.py

@@ -17,7 +17,7 @@
 class ZueriCrop(NonGeoDataset):
     """ZueriCrop dataset.
 
-    The `ZueriCrop <https://github.com/0zgur0/ms-convSTAR>`__
+    The `ZueriCrop <https://github.com/0zgur0/multi-stage-convSTAR-network>`__
     dataset is a dataset for time-series instance segmentation of crops.
 
     Dataset features:
@@ -36,8 +36,8 @@ class ZueriCrop(NonGeoDataset):
 
     Dataset classes:
 
-    * 48 fine-grained hierarchical crop
-      `categories <https://github.com/0zgur0/ms-convSTAR/blob/master/labels.csv>`_
+    * 48 fine-grained hierarchical crop `categories
+      <https://github.com/0zgur0/multi-stage-convSTAR-network/blob/fa92b5b3cb77f5171c5c3be740cd6e6395cc29b6/labels.csv>`_
 
     If you use this dataset in your research, please cite the following paper:
 
@@ -52,7 +52,7 @@ class ZueriCrop(NonGeoDataset):
 
     urls = [
         "https://polybox.ethz.ch/index.php/s/uXfdr2AcXE3QNB6/download",
-        "https://raw.githubusercontent.com/0zgur0/ms-convSTAR/master/labels.csv",
+        "https://raw.githubusercontent.com/0zgur0/multi-stage-convSTAR-network/fa92b5b3cb77f5171c5c3be740cd6e6395cc29b6/labels.csv",  # noqa: E501
     ]
     md5s = ["1635231df67f3d25f4f1e62c98e221a4", "5118398c7a5bbc246f5f6bb35d8d529b"]
     filenames = ["ZueriCrop.hdf5", "labels.csv"]

torchgeo/trainers/byol.py

@@ -282,7 +282,7 @@ class BYOLTask(pl.LightningModule):
     """Class for pre-training any PyTorch model using BYOL.
 
     Supports any available `Timm model
-    <https://rwightman.github.io/pytorch-image-models/>`_
+    <https://huggingface.co/docs/timm/index>`_
     as an architecture choice. To see a list of available pretrained
     models, you can do:
 

torchgeo/trainers/classification.py

@@ -33,7 +33,7 @@ class ClassificationTask(pl.LightningModule):
     """LightningModule for image classification.
 
     Supports any available `Timm model
-    <https://rwightman.github.io/pytorch-image-models/>`_
+    <https://huggingface.co/docs/timm/index>`_
     as an architecture choice. To see a list of available
     models, you can do:
 

torchgeo/trainers/regression.py

@@ -25,7 +25,7 @@ class RegressionTask(pl.LightningModule):
     """LightningModule for training models on regression datasets.
 
     Supports any available `Timm model
-    <https://rwightman.github.io/pytorch-image-models/>`_
+    <https://huggingface.co/docs/timm/index>`_
     as an architecture choice. To see a list of available
     models, you can do:
 

torchgeo/transforms/indices.py

@@ -5,7 +5,7 @@
 
 For more information about indices see the following references:
 - https://www.indexdatabase.de/db/i.php
-- https://github.com/davemlz/awesome-spectral-indices
+- https://github.com/awesome-spectral-indices/awesome-spectral-indices
 """
 
 from typing import Dict, Optional