Don't change Chesapeake URL

microsoft · Feb 26, 2023 · e844e15 · e844e15
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diff --git 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://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/lulc-data-project-2022/). 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
@@ -86,7 +86,7 @@
    "source": [
     "## Datasets\n",
     "\n",
-    "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/lulc-data-project-2022/). 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/torchgeo/datasets/chesapeake.py b/torchgeo/datasets/chesapeake.py
@@ -28,8 +28,8 @@
 class Chesapeake(RasterDataset, abc.ABC):
     """Abstract base class for all Chesapeake datasets.
 
-    `Chesapeake Bay Program Land Use/Land Cover Data Project
-    <https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/lulc-data-project-2022/>`_
+    `Chesapeake Bay High-Resolution Land Cover Project
+    <https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/>`_
     dataset.
 
     This dataset was collected by the Chesapeake Conservancy's Conservation Innovation