diff --git a/docs/index.rst b/docs/index.rst index 44f63707012..d30b0c0eb73 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -45,4 +45,4 @@ torchgeo torchvision TorchElastic TorchServe - PyTorch on XLA Devices + PyTorch on XLA Devices diff --git a/docs/tutorials/benchmarking.ipynb b/docs/tutorials/benchmarking.ipynb index 774d88b240b..ba9f7376090 100644 --- a/docs/tutorials/benchmarking.ipynb +++ b/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." ] }, { diff --git a/docs/tutorials/getting_started.ipynb b/docs/tutorials/getting_started.ipynb index 5d7d89a16f2..aeffecad3ad 100644 --- a/docs/tutorials/getting_started.ipynb +++ b/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." ] }, { diff --git a/docs/tutorials/indices.ipynb b/docs/tutorials/indices.ipynb index eba05a81b8d..feeacf94525 100644 --- a/docs/tutorials/indices.ipynb +++ b/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." ] diff --git a/docs/tutorials/trainers.ipynb b/docs/tutorials/trainers.ipynb index 2bec9fc1d28..b129a36e26e 100644 --- a/docs/tutorials/trainers.ipynb +++ b/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." ] diff --git a/docs/user/contributing.rst b/docs/user/contributing.rst index fca3f016553..4dbe51c8a24 100644 --- a/docs/user/contributing.rst +++ b/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 `_ and `pytest-cov `_ compliant and maintain a high-quality codebase, we use several linting tools: +In order to remain `PEP-8 `_ compliant and maintain a high-quality codebase, we use several linting tools: * `black `_ for code formatting * `isort `_ for import ordering diff --git a/torchgeo/datasets/chesapeake.py b/torchgeo/datasets/chesapeake.py index f5f3139667f..43c829d6671 100644 --- a/torchgeo/datasets/chesapeake.py +++ b/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 - `_ - * `Class Descriptions - `_ - * `Accuracy Assessment - `_ """ 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 - `_. + `_. If you use this dataset in your research, please cite the following paper: diff --git a/torchgeo/datasets/cv4a_kenya_crop_type.py b/torchgeo/datasets/cv4a_kenya_crop_type.py index b6db80ea247..adf00b99609 100644 --- a/torchgeo/datasets/cv4a_kenya_crop_type.py +++ b/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 `__ + ICLR 2020. See `this website `__ for dataset details. Consists of 4 tiles of Sentinel 2 imagery from 13 different points in time. diff --git a/torchgeo/datasets/enviroatlas.py b/torchgeo/datasets/enviroatlas.py index cdf71804af1..5cc7e416ca0 100644 --- a/torchgeo/datasets/enviroatlas.py +++ b/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" `_. 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: diff --git a/torchgeo/datasets/eudem.py b/torchgeo/datasets/eudem.py index e52ec9f8a65..13be06458af 100644 --- a/torchgeo/datasets/eudem.py +++ b/torchgeo/datasets/eudem.py @@ -31,7 +31,7 @@ class EUDEM(RasterDataset): * vertical accuracy of +/- 7 m RMSE * data fused from `ASTER GDEM `_, - `SRTM `_ and Russian topomaps + `SRTM `_ and Russian topomaps Dataset format: diff --git a/torchgeo/datasets/forestdamage.py b/torchgeo/datasets/forestdamage.py index 03d2c718ac4..6c00604c793 100644 --- a/torchgeo/datasets/forestdamage.py +++ b/torchgeo/datasets/forestdamage.py @@ -71,7 +71,7 @@ class ForestDamage(NonGeoDataset): * images are three-channel jpgs * annotations are in `Pascal VOC XML format - `_ + `_ Dataset Classes: diff --git a/torchgeo/datasets/naip.py b/torchgeo/datasets/naip.py index 9274b830129..558df96dea4 100644 --- a/torchgeo/datasets/naip.py +++ b/torchgeo/datasets/naip.py @@ -14,7 +14,7 @@ class NAIP(RasterDataset): """National Agriculture Imagery Program (NAIP) dataset. The `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. diff --git a/torchgeo/datasets/potsdam.py b/torchgeo/datasets/potsdam.py index 288bc88f205..8ecfb1c6891 100644 --- a/torchgeo/datasets/potsdam.py +++ b/torchgeo/datasets/potsdam.py @@ -26,7 +26,7 @@ class Potsdam2D(NonGeoDataset): """Potsdam 2D Semantic Segmentation dataset. - The `Potsdam `__ + The `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 diff --git a/torchgeo/datasets/resisc45.py b/torchgeo/datasets/resisc45.py index 85b782d7bf1..a8cc4ca1ef1 100644 --- a/torchgeo/datasets/resisc45.py +++ b/torchgeo/datasets/resisc45.py @@ -17,7 +17,7 @@ class RESISC45(NonGeoClassificationDataset): """NWPU-RESISC45 dataset. - The `RESISC45 `__ + The `RESISC45 `__ dataset is a dataset for remote sensing image scene classification. Dataset features: diff --git a/torchgeo/datasets/seco.py b/torchgeo/datasets/seco.py index e2ca2818f75..01e2e444bd1 100644 --- a/torchgeo/datasets/seco.py +++ b/torchgeo/datasets/seco.py @@ -21,7 +21,7 @@ class SeasonalContrastS2(NonGeoDataset): """Sentinel 2 imagery from the Seasonal Contrast paper. - The `Seasonal Contrast imagery `_ + The `Seasonal Contrast imagery `_ dataset contains Sentinel 2 imagery patches sampled from different points in time around the 10k most populated cities on Earth. diff --git a/torchgeo/datasets/sen12ms.py b/torchgeo/datasets/sen12ms.py index 0e4db7adf73..2c50ebb2c29 100644 --- a/torchgeo/datasets/sen12ms.py +++ b/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 diff --git a/torchgeo/datasets/vhr10.py b/torchgeo/datasets/vhr10.py index 056449ba81a..214cbbe24a6 100644 --- a/torchgeo/datasets/vhr10.py +++ b/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", } diff --git a/torchgeo/datasets/zuericrop.py b/torchgeo/datasets/zuericrop.py index c752703dcd1..a03a29209ee 100644 --- a/torchgeo/datasets/zuericrop.py +++ b/torchgeo/datasets/zuericrop.py @@ -17,7 +17,7 @@ class ZueriCrop(NonGeoDataset): """ZueriCrop dataset. - The `ZueriCrop `__ + The `ZueriCrop `__ 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 `_ + * 48 fine-grained hierarchical crop `categories + `_ 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"] diff --git a/torchgeo/trainers/byol.py b/torchgeo/trainers/byol.py index bc35eedf16d..aae30f3060c 100644 --- a/torchgeo/trainers/byol.py +++ b/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 - `_ + `_ as an architecture choice. To see a list of available pretrained models, you can do: diff --git a/torchgeo/trainers/classification.py b/torchgeo/trainers/classification.py index 57b1a7fbf9d..ed5f3d0c9f3 100644 --- a/torchgeo/trainers/classification.py +++ b/torchgeo/trainers/classification.py @@ -33,7 +33,7 @@ class ClassificationTask(pl.LightningModule): """LightningModule for image classification. Supports any available `Timm model - `_ + `_ as an architecture choice. To see a list of available models, you can do: diff --git a/torchgeo/trainers/regression.py b/torchgeo/trainers/regression.py index 0d32e3436c7..b6bcad83678 100644 --- a/torchgeo/trainers/regression.py +++ b/torchgeo/trainers/regression.py @@ -25,7 +25,7 @@ class RegressionTask(pl.LightningModule): """LightningModule for training models on regression datasets. Supports any available `Timm model - `_ + `_ as an architecture choice. To see a list of available models, you can do: diff --git a/torchgeo/transforms/indices.py b/torchgeo/transforms/indices.py index 1af6bcccc30..6899959a4c6 100644 --- a/torchgeo/transforms/indices.py +++ b/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