diff --git a/.github/workflows/coverage.yaml b/.github/workflows/coverage.yaml
deleted file mode 100644
index 179e13377ad..00000000000
--- a/.github/workflows/coverage.yaml
+++ /dev/null
@@ -1,22 +0,0 @@
-name: coverage
-on:
-  workflow_run:
-    workflows: [tests]
-    types: [completed]
-jobs:
-  codecov:
-    name: codecov
-    runs-on: ubuntu-latest
-    steps:
-    - name: Clone repo
-      uses: actions/checkout@v4.1.1
-    - name: Download coverage artifacts
-      uses: actions/download-artifact@v4.1.1
-      with:
-        github-token: ${{ github.token }}
-        run-id: ${{ github.event.workflow_run.id }}
-    - name: Report coverage
-      uses: codecov/codecov-action@v4.0.1
-      with:
-        token: ${{ secrets.CODECOV_TOKEN }}
-        fail_ci_if_error: true
diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml
index 6c6c680ae63..dce17ec3f94 100644
--- a/.github/workflows/release.yaml
+++ b/.github/workflows/release.yaml
@@ -16,7 +16,7 @@ jobs:
     - name: Set up python
       uses: actions/setup-python@v5.0.0
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Cache dependencies
       uses: actions/cache@v4.0.0
       id: cache
@@ -44,7 +44,7 @@ jobs:
     - name: Set up python
       uses: actions/setup-python@v5.0.0
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Cache dependencies
       uses: actions/cache@v4.0.0
       id: cache
@@ -72,7 +72,7 @@ jobs:
     - name: Set up python
       uses: actions/setup-python@v5.0.0
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Cache dependencies
       uses: actions/cache@v4.0.0
       id: cache
diff --git a/.github/workflows/style.yaml b/.github/workflows/style.yaml
index 7d6236c9408..8bd60000d5f 100644
--- a/.github/workflows/style.yaml
+++ b/.github/workflows/style.yaml
@@ -18,7 +18,7 @@ jobs:
     - name: Set up python
       uses: actions/setup-python@v5.0.0
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Cache dependencies
       uses: actions/cache@v4.0.0
       id: cache
@@ -43,7 +43,7 @@ jobs:
     - name: Set up python
       uses: actions/setup-python@v5.0.0
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Cache dependencies
       uses: actions/cache@v4.0.0
       id: cache
@@ -68,7 +68,7 @@ jobs:
     - name: Set up python
       uses: actions/setup-python@v5.0.0
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Cache dependencies
       uses: actions/cache@v4.0.0
       id: cache
@@ -93,7 +93,7 @@ jobs:
     - name: Set up python
       uses: actions/setup-python@v5.0.0
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Cache dependencies
       uses: actions/cache@v4.0.0
       id: cache
@@ -118,7 +118,7 @@ jobs:
     - name: Set up python
       uses: actions/setup-python@v5.0.0
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Cache dependencies
       uses: actions/cache@v4.0.0
       id: cache
@@ -143,7 +143,7 @@ jobs:
     - name: Set up python
       uses: actions/setup-python@v5.0.0
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Cache dependencies
       uses: actions/cache@v4.0.0
       id: cache
diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml
index ec5a68feb25..d72a114f092 100644
--- a/.github/workflows/tests.yaml
+++ b/.github/workflows/tests.yaml
@@ -17,7 +17,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ['3.9', '3.10', '3.11']
+        python-version: ['3.9', '3.10', '3.11', '3.12']
     steps:
     - name: Clone repo
       uses: actions/checkout@v4.1.1
@@ -31,7 +31,7 @@ jobs:
       with:
         path: ${{ env.pythonLocation }}
         key: ${{ env.pythonLocation }}-${{ hashFiles('requirements/required.txt') }}-${{ hashFiles('requirements/datasets.txt') }}-${{ hashFiles('requirements/tests.txt') }}
-      if: ${{ ! (runner.os == 'macOS' && matrix.python-version == '3.11') }}
+      if: ${{ ! (runner.os == 'macOS' && (matrix.python-version == '3.11' || matrix.python-version == '3.12')) }}
     - name: Setup headless display for pyvista
       uses: pyvista/setup-headless-display-action@v2
     - name: Install apt dependencies (Linux)
@@ -56,11 +56,10 @@ jobs:
       run: |
         pytest --cov=torchgeo --cov-report=xml --durations=10
         python3 -m torchgeo --help
-    - name: Upload coverage artifact
-      uses: actions/upload-artifact@v4.3.0
+    - name: Report coverage
+      uses: codecov/codecov-action@v3.1.6
       with:
-        name: coverage_${{ matrix.os }}_py-${{ matrix.python-version }}
-        path: coverage.xml
+        token: ${{ secrets.CODECOV_TOKEN }}
   minimum:
     name: minimum
     runs-on: ubuntu-latest
@@ -96,25 +95,10 @@ jobs:
       run: |
         pytest --cov=torchgeo --cov-report=xml --durations=10
         python3 -m torchgeo --help
-    - name: Upload coverage artifact
-      uses: actions/upload-artifact@v4.3.0
-      with:
-        name: coverage_minimum
-        path: coverage.xml
-  codecov:
-    name: codecov
-    runs-on: ubuntu-latest
-    needs: [latest, minimum]
-    steps:
-    - name: Clone repo
-      uses: actions/checkout@v4.1.1
-    - name: Download coverage artifacts
-      uses: actions/download-artifact@v4.1.1
     - name: Report coverage
       uses: codecov/codecov-action@v3.1.6
       with:
         token: ${{ secrets.CODECOV_TOKEN }}
-        fail_ci_if_error: true
 concurrency:
   group: ${{ github.workflow }}-${{ github.event.pull_request.head.label || github.head_ref || github.ref }}
   cancel-in-progress: true
diff --git a/.github/workflows/tutorials.yaml b/.github/workflows/tutorials.yaml
index 5833bccd4f6..800a3ca46a7 100644
--- a/.github/workflows/tutorials.yaml
+++ b/.github/workflows/tutorials.yaml
@@ -20,17 +20,17 @@ jobs:
     - name: Set up python
       uses: actions/setup-python@v5.0.0
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Cache dependencies
       uses: actions/cache@v4.0.0
       id: cache
       with:
         path: ${{ env.pythonLocation }}
-        key: ${{ env.pythonLocation }}-${{ hashFiles('pyproject.toml') }}-tutorials
+        key: ${{ env.pythonLocation }}-${{ hashFiles('requirements/required.txt') }}-${{ hashFiles('requirements/docs.txt') }}-${{ hashFiles('requirements/tests.txt') }}-tutorials
     - name: Install pip dependencies
       if: steps.cache.outputs.cache-hit != 'true'
       run: |
-        pip install .[docs,tests] planetary_computer pystac
+        pip install -r requirements/required.txt -r requirements/docs.txt -r requirements/tests.txt planetary_computer pystac
         pip cache purge
     - name: List pip dependencies
       run: pip list
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 534c7e24017..9cfd9895a23 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -8,7 +8,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.11"
+    python: "3.12"
 
 # Configuration of the Python environment to be used
 python:
diff --git a/README.md b/README.md
index 36ae3aa37eb..ec31fcb7753 100644
--- a/README.md
+++ b/README.md
@@ -132,7 +132,7 @@ from torchgeo.models import ResNet18_Weights
 
 weights = ResNet18_Weights.SENTINEL2_ALL_MOCO
 model = timm.create_model("resnet18", in_chans=weights.meta["in_chans"], num_classes=10)
-model = model.load_state_dict(weights.get_state_dict(progress=True), strict=False)
+model.load_state_dict(weights.get_state_dict(progress=True), strict=False)
 ```
 
 These weights can also directly be used in TorchGeo Lightning modules that are shown in the following section via the `weights` argument. For a notebook example, see this [tutorial](https://torchgeo.readthedocs.io/en/stable/tutorials/pretrained_weights.html).
diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst
index 645acbacc47..3f9539db034 100644
--- a/docs/api/datasets.rst
+++ b/docs/api/datasets.rst
@@ -23,6 +23,11 @@ Aboveground Woody Biomass
 
 .. autoclass:: AbovegroundLiveWoodyBiomassDensity
 
+AgriFieldNet
+^^^^^^^^^^^^
+
+.. autoclass:: AgriFieldNet
+
 Airphen
 ^^^^^^^
 
diff --git a/docs/api/geo_datasets.csv b/docs/api/geo_datasets.csv
index ed7655e843d..ac611c00e45 100644
--- a/docs/api/geo_datasets.csv
+++ b/docs/api/geo_datasets.csv
@@ -1,5 +1,6 @@
 Dataset,Type,Source,License,Size (px),Resolution (m)
 `Aboveground Woody Biomass`_,Masks,"Landsat, LiDAR","CC-BY-4.0","40,000x40,000",30
+`AgriFieldNet`_,"Imagery, Masks",Sentinel-2,"CC-BY-4.0","256x256",10
 `Airphen`_,Imagery,Airphen,-,"1,280x960",0.047--0.09
 `Aster Global DEM`_,Masks,Aster,"public domain","3,601x3,601",30
 `Canadian Building Footprints`_,Geometries,Bing Imagery,"ODbL-1.0",-,-
diff --git a/docs/tutorials/pretrained_weights.ipynb b/docs/tutorials/pretrained_weights.ipynb
index 26d97fcbc6c..e15dd1ebc16 100644
--- a/docs/tutorials/pretrained_weights.ipynb
+++ b/docs/tutorials/pretrained_weights.ipynb
@@ -228,7 +228,7 @@
    "source": [
     "in_chans = weights.meta[\"in_chans\"]\n",
     "model = timm.create_model(\"resnet18\", in_chans=in_chans, num_classes=10)\n",
-    "model = model.load_state_dict(weights.get_state_dict(progress=True), strict=False)"
+    "model.load_state_dict(weights.get_state_dict(progress=True), strict=False)"
    ]
   },
   {
diff --git a/docs/tutorials/transforms.ipynb b/docs/tutorials/transforms.ipynb
index 80a53f52643..ca76d9dc5a9 100644
--- a/docs/tutorials/transforms.ipynb
+++ b/docs/tutorials/transforms.ipynb
@@ -501,7 +501,7 @@
     "id": "w4ZbjxPyHoiB"
    },
    "source": [
-    "It's even possible to chain indices along with augmentations from kornia for a single callable during training."
+    "It's even possible to chain indices along with augmentations from Kornia for a single callable during training."
    ]
   },
   {
diff --git a/experiments/torchgeo/run_resisc45_experiments.py b/experiments/torchgeo/run_resisc45_experiments.py
index e4915bc0c64..1049edf749b 100755
--- a/experiments/torchgeo/run_resisc45_experiments.py
+++ b/experiments/torchgeo/run_resisc45_experiments.py
@@ -37,7 +37,7 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
     for model, lr, loss, weights in itertools.product(
         model_options, lr_options, loss_options, weight_options
     ):
-        experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_','-')}"
+        experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_', '-')}"
 
         output_dir = os.path.join("output", "resisc45_experiments")
         log_dir = os.path.join(output_dir, "logs")
diff --git a/experiments/torchgeo/run_so2sat_experiments.py b/experiments/torchgeo/run_so2sat_experiments.py
index d574d0987c0..fd8562ca345 100755
--- a/experiments/torchgeo/run_so2sat_experiments.py
+++ b/experiments/torchgeo/run_so2sat_experiments.py
@@ -37,7 +37,7 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
     for model, lr, loss, weights in itertools.product(
         model_options, lr_options, loss_options, weight_options
     ):
-        experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_','-')}"
+        experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_', '-')}"
 
         output_dir = os.path.join("output", "so2sat_experiments")
         log_dir = os.path.join(output_dir, "logs")
diff --git a/experiments/torchgeo/run_so2sat_seed_experiments.py b/experiments/torchgeo/run_so2sat_seed_experiments.py
index 90e6d274910..2f88eba3c75 100755
--- a/experiments/torchgeo/run_so2sat_seed_experiments.py
+++ b/experiments/torchgeo/run_so2sat_seed_experiments.py
@@ -38,7 +38,7 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
     for model, lr, loss, weights, seed in itertools.product(
         model_options, lr_options, loss_options, weight_options, seeds
     ):
-        experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_','-')}_{seed}"
+        experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_', '-')}_{seed}"
 
         output_dir = os.path.join("output", "so2sat_seed_experiments")
         log_dir = os.path.join(output_dir, "logs")
diff --git a/pyproject.toml b/pyproject.toml
index d4a8c7ae955..5b0a12c4830 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,6 +32,7 @@ classifiers = [
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
     "Topic :: Scientific/Engineering :: GIS",
 ]
diff --git a/requirements/datasets.txt b/requirements/datasets.txt
index a52aa575d9e..d2caa09ad30 100644
--- a/requirements/datasets.txt
+++ b/requirements/datasets.txt
@@ -3,7 +3,7 @@ h5py==3.10.0
 laspy==2.5.3
 opencv-python==4.9.0.80
 pycocotools==2.0.7
-pyvista==0.43.2
+pyvista==0.43.3
 radiant-mlhub==0.4.1
 rarfile==4.1
 scikit-image==0.22.0
diff --git a/requirements/docs.txt b/requirements/docs.txt
index 898ddd75ed4..9cfb14b7a3d 100644
--- a/requirements/docs.txt
+++ b/requirements/docs.txt
@@ -1,4 +1,4 @@
 # docs
-ipywidgets==8.1.1
+ipywidgets==8.1.2
 nbsphinx==0.9.3
 sphinx==5.3.0
diff --git a/requirements/required.txt b/requirements/required.txt
index 7631a543e30..e7cb2af6ac1 100644
--- a/requirements/required.txt
+++ b/requirements/required.txt
@@ -6,9 +6,9 @@ einops==0.7.0
 fiona==1.9.5
 kornia==0.7.1
 lightly==1.4.25
-lightning[pytorch-extra]==2.1.4
+lightning[pytorch-extra]==2.2.0
 matplotlib==3.8.2
-numpy==1.26.3
+numpy==1.26.4
 pandas==2.2.0
 pillow==10.2.0
 pyproj==3.6.1
diff --git a/tests/data/agrifieldnet/data.py b/tests/data/agrifieldnet/data.py
new file mode 100644
index 00000000000..e0b4d0e256c
--- /dev/null
+++ b/tests/data/agrifieldnet/data.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+
+import numpy as np
+import rasterio
+from rasterio.crs import CRS
+from rasterio.transform import Affine
+
+
+def generate_test_data(paths: str) -> str:
+    """Create test data archive for AgriFieldNet dataset.
+
+    Args:
+        paths: path to store test data
+        n_samples: number of samples.
+
+    Returns:
+        md5 hash of created archive
+    """
+    dtype = np.uint8
+    dtype_max = np.iinfo(dtype).max
+
+    SIZE = 32
+
+    np.random.seed(0)
+
+    bands = (
+        "B01",
+        "B02",
+        "B03",
+        "B04",
+        "B05",
+        "B06",
+        "B07",
+        "B08",
+        "B8A",
+        "B09",
+        "B11",
+        "B12",
+    )
+
+    profile = {
+        "dtype": dtype,
+        "width": SIZE,
+        "height": SIZE,
+        "count": 1,
+        "crs": CRS.from_epsg(32644),
+        "transform": Affine(10.0, 0.0, 535840.0, 0.0, -10.0, 3079680.0),
+    }
+
+    source_dir = os.path.join(paths, "source")
+    train_mask_dir = os.path.join(paths, "train_labels")
+    test_field_dir = os.path.join(paths, "test_labels")
+
+    os.makedirs(source_dir, exist_ok=True)
+    os.makedirs(train_mask_dir, exist_ok=True)
+    os.makedirs(test_field_dir, exist_ok=True)
+
+    source_unique_folder_ids = ["32407", "8641e", "a419f", "eac11", "ff450"]
+    train_folder_ids = source_unique_folder_ids[0:5]
+    test_folder_ids = source_unique_folder_ids[3:5]
+
+    for id in source_unique_folder_ids:
+        directory = os.path.join(
+            source_dir, "ref_agrifieldnet_competition_v1_source_" + id
+        )
+        os.makedirs(directory, exist_ok=True)
+
+        for band in bands:
+            train_arr = np.random.randint(dtype_max, size=(SIZE, SIZE), dtype=dtype)
+            path = os.path.join(
+                directory, f"ref_agrifieldnet_competition_v1_source_{id}_{band}_10m.tif"
+            )
+            with rasterio.open(path, "w", **profile) as src:
+                src.write(train_arr, 1)
+
+    for id in train_folder_ids:
+        train_mask_arr = np.random.randint(size=(SIZE, SIZE), low=0, high=6)
+        path = os.path.join(
+            train_mask_dir, f"ref_agrifieldnet_competition_v1_labels_train_{id}.tif"
+        )
+        with rasterio.open(path, "w", **profile) as src:
+            src.write(train_mask_arr, 1)
+
+        train_field_arr = np.random.randint(20, size=(SIZE, SIZE), dtype=np.uint16)
+        path = os.path.join(
+            train_mask_dir,
+            f"ref_agrifieldnet_competition_v1_labels_train_{id}_field_ids.tif",
+        )
+        with rasterio.open(path, "w", **profile) as src:
+            src.write(train_field_arr, 1)
+
+    for id in test_folder_ids:
+        test_field_arr = np.random.randint(10, 30, size=(SIZE, SIZE), dtype=np.uint16)
+        path = os.path.join(
+            test_field_dir,
+            f"ref_agrifieldnet_competition_v1_labels_test_{id}_field_ids.tif",
+        )
+        with rasterio.open(path, "w", **profile) as src:
+            src.write(test_field_arr, 1)
+
+
+if __name__ == "__main__":
+    generate_test_data(os.getcwd())
diff --git a/tests/data/agrifieldnet/source/ref_agrifieldnet_competition_v1_source_32407/ref_agrifieldnet_competition_v1_source_32407_B01_10m.tif b/tests/data/agrifieldnet/source/ref_agrifieldnet_competition_v1_source_32407/ref_agrifieldnet_competition_v1_source_32407_B01_10m.tif
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index 00000000000..0742b602456
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index 8dce2bb82e9..00000000000
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deleted file mode 100644
index 67be3087ed7..00000000000
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diff --git a/tests/data/nccm/CDL2017_clip.tif b/tests/data/nccm/CDL2017_clip.tif
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index 00000000000..1040f7936c6
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diff --git a/tests/data/nccm/CDL2018_clip1.tif b/tests/data/nccm/CDL2018_clip1.tif
new file mode 100644
index 00000000000..3313fef10d1
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diff --git a/tests/data/nccm/CDL2019_clip.tif b/tests/data/nccm/CDL2019_clip.tif
new file mode 100644
index 00000000000..9c4d1dcae44
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diff --git a/tests/data/nccm/data.py b/tests/data/nccm/data.py
index 6a98ca3a2d0..2956f147033 100644
--- a/tests/data/nccm/data.py
+++ b/tests/data/nccm/data.py
@@ -5,7 +5,6 @@
 
 import hashlib
 import os
-import shutil
 
 import numpy as np
 import rasterio
@@ -48,20 +47,14 @@ def create_file(path: str, dtype: str):
 
 
 if __name__ == "__main__":
-    dir = os.path.join(os.getcwd(), "13090442")
-
-    if os.path.exists(dir) and os.path.isdir(dir):
-        shutil.rmtree(dir)
-
+    dir = os.path.join(os.getcwd())
     os.makedirs(dir, exist_ok=True)
 
     for file in files:
         create_file(os.path.join(dir, file), dtype="int8")
 
-    # Compress data
-    shutil.make_archive("13090442", "zip", ".", dir)
-
     # Compute checksums
-    with open("13090442.zip", "rb") as f:
-        md5 = hashlib.md5(f.read()).hexdigest()
-        print(f"13090442.zip: {md5}")
+    for file in files:
+        with open(file, "rb") as f:
+            md5 = hashlib.md5(f.read()).hexdigest()
+            print(f"{file}: {md5}")
diff --git a/tests/datasets/test_agrifieldnet.py b/tests/datasets/test_agrifieldnet.py
new file mode 100644
index 00000000000..cdb539671f9
--- /dev/null
+++ b/tests/datasets/test_agrifieldnet.py
@@ -0,0 +1,77 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pytest
+import torch
+import torch.nn as nn
+from rasterio.crs import CRS
+
+from torchgeo.datasets import (
+    AgriFieldNet,
+    BoundingBox,
+    DatasetNotFoundError,
+    IntersectionDataset,
+    RGBBandsMissingError,
+    UnionDataset,
+)
+
+
+class TestAgriFieldNet:
+    @pytest.fixture
+    def dataset(self) -> AgriFieldNet:
+        path = os.path.join("tests", "data", "agrifieldnet")
+        transforms = nn.Identity()
+        return AgriFieldNet(paths=path, transforms=transforms)
+
+    def test_getitem(self, dataset: AgriFieldNet) -> None:
+        x = dataset[dataset.bounds]
+        assert isinstance(x, dict)
+        assert isinstance(x["crs"], CRS)
+        assert isinstance(x["image"], torch.Tensor)
+        assert isinstance(x["mask"], torch.Tensor)
+
+    def test_and(self, dataset: AgriFieldNet) -> None:
+        ds = dataset & dataset
+        assert isinstance(ds, IntersectionDataset)
+
+    def test_or(self, dataset: AgriFieldNet) -> None:
+        ds = dataset | dataset
+        assert isinstance(ds, UnionDataset)
+
+    def test_already_downloaded(self, dataset: AgriFieldNet) -> None:
+        AgriFieldNet(paths=dataset.paths)
+
+    def test_not_downloaded(self, tmp_path: Path) -> None:
+        with pytest.raises(DatasetNotFoundError, match="Dataset not found"):
+            AgriFieldNet(str(tmp_path))
+
+    def test_plot(self, dataset: AgriFieldNet) -> None:
+        x = dataset[dataset.bounds]
+        dataset.plot(x, suptitle="Test")
+        plt.close()
+
+    def test_plot_prediction(self, dataset: AgriFieldNet) -> None:
+        x = dataset[dataset.bounds]
+        x["prediction"] = x["mask"].clone()
+        dataset.plot(x, suptitle="Prediction")
+        plt.close()
+
+    def test_invalid_query(self, dataset: AgriFieldNet) -> None:
+        query = BoundingBox(0, 0, 0, 0, 0, 0)
+        with pytest.raises(
+            IndexError, match="query: .* not found in index with bounds:"
+        ):
+            dataset[query]
+
+    def test_rgb_bands_absent_plot(self, dataset: AgriFieldNet) -> None:
+        with pytest.raises(
+            RGBBandsMissingError, match="Dataset does not contain some of the RGB bands"
+        ):
+            ds = AgriFieldNet(dataset.paths, bands=["B01", "B02", "B05"])
+            x = ds[ds.bounds]
+            ds.plot(x, suptitle="Test")
+            plt.close()
diff --git a/tests/datasets/test_nccm.py b/tests/datasets/test_nccm.py
index 6637da3e840..0d922d9d3d5 100644
--- a/tests/datasets/test_nccm.py
+++ b/tests/datasets/test_nccm.py
@@ -25,9 +25,19 @@ class TestNCCM:
     @pytest.fixture
     def dataset(self, monkeypatch: MonkeyPatch, tmp_path: Path) -> NCCM:
         monkeypatch.setattr(torchgeo.datasets.nccm, "download_url", download_url)
-        url = os.path.join("tests", "data", "nccm", "13090442.zip")
+        md5s = {
+            2017: "ae5c390d0ffb8970d544b8a09142759f",
+            2018: "0d453bdb8ea5b7318c33e62513760580",
+            2019: "d4ab7ab00bb57623eafb6b27747e5639",
+        }
+        monkeypatch.setattr(NCCM, "md5s", md5s)
+        urls = {
+            2017: os.path.join("tests", "data", "nccm", "CDL2017_clip.tif"),
+            2018: os.path.join("tests", "data", "nccm", "CDL2018_clip1.tif"),
+            2019: os.path.join("tests", "data", "nccm", "CDL2019_clip.tif"),
+        }
+        monkeypatch.setattr(NCCM, "urls", urls)
         transforms = nn.Identity()
-        monkeypatch.setattr(NCCM, "url", url)
         root = str(tmp_path)
         return NCCM(root, transforms=transforms, download=True, checksum=True)
 
@@ -48,11 +58,8 @@ def test_or(self, dataset: NCCM) -> None:
     def test_already_extracted(self, dataset: NCCM) -> None:
         NCCM(dataset.paths, download=True)
 
-    def test_already_downloaded(self, tmp_path: Path) -> None:
-        pathname = os.path.join("tests", "data", "nccm", "13090442.zip")
-        root = str(tmp_path)
-        shutil.copy(pathname, root)
-        NCCM(root)
+    def test_already_downloaded(self, dataset: NCCM) -> None:
+        NCCM(dataset.paths, download=True)
 
     def test_plot(self, dataset: NCCM) -> None:
         query = dataset.bounds
diff --git a/tests/trainers/test_utils.py b/tests/trainers/test_utils.py
index 52d7a9be25d..06da0a359eb 100644
--- a/tests/trainers/test_utils.py
+++ b/tests/trainers/test_utils.py
@@ -41,7 +41,7 @@ def test_get_input_layer_name_and_module() -> None:
 
 def test_load_state_dict(checkpoint: str, model: Module) -> None:
     _, state_dict = extract_backbone(checkpoint)
-    model = load_state_dict(model, state_dict)
+    load_state_dict(model, state_dict)
 
 
 def test_load_state_dict_unequal_input_channels(checkpoint: str, model: Module) -> None:
@@ -58,7 +58,7 @@ def test_load_state_dict_unequal_input_channels(checkpoint: str, model: Module)
         f" model {expected_in_channels}. Overriding with new input channels"
     )
     with pytest.warns(UserWarning, match=warning):
-        model = load_state_dict(model, state_dict)
+        load_state_dict(model, state_dict)
 
 
 def test_load_state_dict_unequal_classes(checkpoint: str, model: Module) -> None:
@@ -74,7 +74,7 @@ def test_load_state_dict_unequal_classes(checkpoint: str, model: Module) -> None
         f" {expected_num_classes}. Overriding with new num classes"
     )
     with pytest.warns(UserWarning, match=warning):
-        model = load_state_dict(model, state_dict)
+        load_state_dict(model, state_dict)
 
 
 def test_reinit_initial_conv_layer() -> None:
diff --git a/torchgeo/datamodules/chesapeake.py b/torchgeo/datamodules/chesapeake.py
index 912ccb58db5..604c0d7e3ec 100644
--- a/torchgeo/datamodules/chesapeake.py
+++ b/torchgeo/datamodules/chesapeake.py
@@ -6,7 +6,6 @@
 from typing import Any, Optional
 
 import kornia.augmentation as K
-import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
@@ -113,7 +112,7 @@ def __init__(
         self.test_splits = test_splits
         self.class_set = class_set
         self.use_prior_labels = use_prior_labels
-        self.prior_smoothing_constant = torch.tensor(prior_smoothing_constant)
+        self.prior_smoothing_constant = prior_smoothing_constant
 
         if self.use_prior_labels:
             self.layers = [
diff --git a/torchgeo/datamodules/spacenet.py b/torchgeo/datamodules/spacenet.py
index ed4fbe15795..3a3b7531cba 100644
--- a/torchgeo/datamodules/spacenet.py
+++ b/torchgeo/datamodules/spacenet.py
@@ -6,7 +6,6 @@
 from typing import Any
 
 import kornia.augmentation as K
-import torch
 from torch import Tensor
 
 from ..datasets import SpaceNet1
@@ -88,6 +87,6 @@ def on_after_batch_transfer(
         # We add 1 to the mask to map the current {background, building} labels to
         # the values {1, 2}. This is necessary because we add 0 padding to the
         # mask that we want to ignore in the loss function.
-        batch["mask"] += torch.tensor(1)
+        batch["mask"] += 1
 
         return super().on_after_batch_transfer(batch, dataloader_idx)
diff --git a/torchgeo/datasets/__init__.py b/torchgeo/datasets/__init__.py
index 235ab83c8eb..71eb3f168dc 100644
--- a/torchgeo/datasets/__init__.py
+++ b/torchgeo/datasets/__init__.py
@@ -5,6 +5,7 @@
 
 from .advance import ADVANCE
 from .agb_live_woody_density import AbovegroundLiveWoodyBiomassDensity
+from .agrifieldnet import AgriFieldNet
 from .airphen import Airphen
 from .astergdem import AsterGDEM
 from .benin_cashews import BeninSmallHolderCashews
@@ -139,6 +140,7 @@
 __all__ = (
     # GeoDataset
     "AbovegroundLiveWoodyBiomassDensity",
+    "AgriFieldNet",
     "Airphen",
     "AsterGDEM",
     "CanadianBuildingFootprints",
diff --git a/torchgeo/datasets/agrifieldnet.py b/torchgeo/datasets/agrifieldnet.py
new file mode 100644
index 00000000000..67a0c994f44
--- /dev/null
+++ b/torchgeo/datasets/agrifieldnet.py
@@ -0,0 +1,272 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""AgriFieldNet India Challenge dataset."""
+
+import os
+import re
+from collections.abc import Iterable, Sequence
+from typing import Any, Callable, Optional, Union, cast
+
+import matplotlib.pyplot as plt
+import torch
+from matplotlib.figure import Figure
+from rasterio.crs import CRS
+from torch import Tensor
+
+from .geo import RasterDataset
+from .utils import BoundingBox, RGBBandsMissingError
+
+
+class AgriFieldNet(RasterDataset):
+    """AgriFieldNet India Challenge dataset.
+
+    The `AgriFieldNet India Challenge
+    <https://zindi.africa/competitions/agrifieldnet-india-challenge>`__ dataset
+    includes satellite imagery from Sentinel-2 cloud free composites
+    (single snapshot) and labels for crop type that were collected by ground survey.
+    The Sentinel-2 data are then matched with corresponding labels.
+    The dataset contains 7081 fields, which have been split into training and
+    test sets (5551 fields in the train and 1530 fields in the test).
+    Satellite imagery and labels are tiled into 256x256 chips adding up to 1217 tiles.
+    The fields are distributed across all chips, some chips may only have train or
+    test fields and some may have both. Since the labels are derived from data
+    collected on the ground, not all the pixels are labeled in each chip.
+    If the field ID for a pixel is set to 0 it means that pixel is not included in
+    either of the train or test set (and correspondingly the crop label
+    will be 0 as well). For this challenge train and test sets have slightly
+    different crop type distributions. The train set follows the distribution
+    of ground reference data which is a skewed distribution with a few dominant
+    crops being over represented. The test set was drawn randomly from an area
+    weighted field list that ensured that fields with less common crop types
+    were better represented in the test set. The original dataset can be
+    downloaded from `Source Cooperative <https://beta.source.coop/
+    radiantearth/agrifieldnet-competition/>`__.
+
+    Dataset format:
+
+    * images are 12-band Sentinel-2 data
+    * masks are tiff images with unique values representing the class and field id
+
+    Dataset classes:
+
+    0 - No-Data
+    1 - Wheat
+    2 - Mustard
+    3 - Lentil
+    4 - No Crop/Fallow
+    5 - Green pea
+    6 - Sugarcane
+    8 - Garlic
+    9 - Maize
+    13 - Gram
+    14 - Coriander
+    15 - Potato
+    16 - Berseem
+    36 - Rice
+
+    If you use this dataset in your research, please cite the following dataset:
+
+    * https://doi.org/10.34911/rdnt.wu92p1
+
+    .. versionadded:: 0.6
+    """
+
+    filename_regex = r"""
+        ^ref_agrifieldnet_competition_v1_source_
+        (?P<unique_folder_id>[a-z0-9]{5})
+        _(?P<band>B[0-9A-Z]{2})_10m
+    """
+
+    rgb_bands = ["B04", "B03", "B02"]
+    all_bands = [
+        "B01",
+        "B02",
+        "B03",
+        "B04",
+        "B05",
+        "B06",
+        "B07",
+        "B08",
+        "B8A",
+        "B09",
+        "B11",
+        "B12",
+    ]
+
+    cmap = {
+        0: (0, 0, 0, 255),
+        1: (255, 211, 0, 255),
+        2: (255, 37, 37, 255),
+        3: (0, 168, 226, 255),
+        4: (255, 158, 9, 255),
+        5: (37, 111, 0, 255),
+        6: (255, 255, 0, 255),
+        8: (111, 166, 0, 255),
+        9: (0, 175, 73, 255),
+        13: (222, 166, 9, 255),
+        14: (222, 166, 9, 255),
+        15: (124, 211, 255, 255),
+        16: (226, 0, 124, 255),
+        36: (137, 96, 83, 255),
+    }
+
+    def __init__(
+        self,
+        paths: Union[str, Iterable[str]] = "data",
+        crs: Optional[CRS] = None,
+        classes: list[int] = list(cmap.keys()),
+        bands: Sequence[str] = all_bands,
+        transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None,
+        cache: bool = True,
+    ) -> None:
+        """Initialize a new AgriFieldNet dataset instance.
+
+        Args:
+            paths: one or more root directories to search for files to load
+            crs: :term:`coordinate reference system (CRS)` to warp to
+                (defaults to the CRS of the first file found)
+            classes: list of classes to include, the rest will be mapped to 0
+                (defaults to all classes)
+            bands: the subset of bands to load
+            transforms: a function/transform that takes input sample and its target as
+                entry and returns a transformed version
+            cache: if True, cache the dataset in memory
+
+        Raises:
+            DatasetNotFoundError: If dataset is not found.
+        """
+        assert (
+            set(classes) <= self.cmap.keys()
+        ), f"Only the following classes are valid: {list(self.cmap.keys())}."
+        assert 0 in classes, "Classes must include the background class: 0"
+
+        self.paths = paths
+        self.classes = classes
+        self.ordinal_map = torch.zeros(max(self.cmap.keys()) + 1, dtype=self.dtype)
+        self.ordinal_cmap = torch.zeros((len(self.classes), 4), dtype=torch.uint8)
+
+        super().__init__(
+            paths=paths, crs=crs, bands=bands, transforms=transforms, cache=cache
+        )
+
+        # Map chosen classes to ordinal numbers, all others mapped to background class
+        for v, k in enumerate(self.classes):
+            self.ordinal_map[k] = v
+            self.ordinal_cmap[v] = torch.tensor(self.cmap[k])
+
+    def __getitem__(self, query: BoundingBox) -> dict[str, Any]:
+        """Return an index within the dataset.
+
+        Args:
+            index: index to return
+
+        Returns:
+            data, label, and field ids at that index
+        """
+        assert isinstance(self.paths, str)
+
+        hits = self.index.intersection(tuple(query), objects=True)
+        filepaths = cast(list[str], [hit.object for hit in hits])
+
+        if not filepaths:
+            raise IndexError(
+                f"query: {query} not found in index with bounds: {self.bounds}"
+            )
+
+        data_list: list[Tensor] = []
+        filename_regex = re.compile(self.filename_regex, re.VERBOSE)
+        for band in self.bands:
+            band_filepaths = []
+            for filepath in filepaths:
+                filename = os.path.basename(filepath)
+                directory = os.path.dirname(filepath)
+                match = re.match(filename_regex, filename)
+                if match:
+                    if "band" in match.groupdict():
+                        start = match.start("band")
+                        end = match.end("band")
+                        filename = filename[:start] + band + filename[end:]
+                filepath = os.path.join(directory, filename)
+                band_filepaths.append(filepath)
+            data_list.append(self._merge_files(band_filepaths, query))
+        image = torch.cat(data_list)
+
+        mask_filepaths = []
+        for root, dirs, files in os.walk(os.path.join(self.paths, "train_labels")):
+            for file in files:
+                if not file.endswith("_field_ids.tif") and file.endswith(".tif"):
+                    file_path = os.path.join(root, file)
+                    mask_filepaths.append(file_path)
+
+        mask = self._merge_files(mask_filepaths, query)
+        mask = self.ordinal_map[mask.squeeze().long()]
+
+        sample = {
+            "crs": self.crs,
+            "bbox": query,
+            "image": image.float(),
+            "mask": mask.long(),
+        }
+
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+
+        return sample
+
+    def plot(
+        self,
+        sample: dict[str, Tensor],
+        show_titles: bool = True,
+        suptitle: Optional[str] = None,
+    ) -> Figure:
+        """Plot a sample from the dataset.
+
+        Args:
+            sample: a sample returned by :meth:`__getitem__`
+            show_titles: flag indicating whether to show titles above each panel
+            suptitle: optional string to use as a suptitle
+
+        Returns:
+            a matplotlib Figure with the rendered sample
+
+        Raises:
+            RGBBandsMissingError: If *bands* does not include all RGB bands.
+        """
+        rgb_indices = []
+        for band in self.rgb_bands:
+            if band in self.bands:
+                rgb_indices.append(self.bands.index(band))
+            else:
+                raise RGBBandsMissingError()
+
+        image = sample["image"][rgb_indices].permute(1, 2, 0)
+        image = (image - image.min()) / (image.max() - image.min())
+
+        mask = sample["mask"].squeeze()
+        ncols = 2
+
+        showing_prediction = "prediction" in sample
+        if showing_prediction:
+            pred = sample["prediction"].squeeze()
+            ncols += 1
+
+        fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(ncols * 4, 4))
+        axs[0].imshow(image)
+        axs[0].axis("off")
+        axs[1].imshow(self.ordinal_cmap[mask], interpolation="none")
+        axs[1].axis("off")
+        if show_titles:
+            axs[0].set_title("Image")
+            axs[1].set_title("Mask")
+
+        if showing_prediction:
+            axs[2].imshow(self.ordinal_cmap[pred], interpolation="none")
+            axs[2].axis("off")
+            if show_titles:
+                axs[2].set_title("Prediction")
+
+        if suptitle is not None:
+            plt.suptitle(suptitle)
+
+        return fig
diff --git a/torchgeo/datasets/benin_cashews.py b/torchgeo/datasets/benin_cashews.py
index 9edda8d26cb..6b9f95d34bb 100644
--- a/torchgeo/datasets/benin_cashews.py
+++ b/torchgeo/datasets/benin_cashews.py
@@ -387,8 +387,7 @@ def _load_mask(self, transform: rasterio.Affine) -> Tensor:
             dtype=np.uint8,
         )
 
-        mask = torch.from_numpy(mask_data)
-        mask = mask.long()
+        mask = torch.from_numpy(mask_data).long()
         return mask
 
     def _check_integrity(self) -> bool:
diff --git a/torchgeo/datasets/bigearthnet.py b/torchgeo/datasets/bigearthnet.py
index 3a821811f54..9a127248a8b 100644
--- a/torchgeo/datasets/bigearthnet.py
+++ b/torchgeo/datasets/bigearthnet.py
@@ -408,8 +408,7 @@ def _load_image(self, index: int) -> Tensor:
                 )
                 images.append(array)
         arrays: "np.typing.NDArray[np.int_]" = np.stack(images, axis=0)
-        tensor = torch.from_numpy(arrays)
-        tensor = tensor.float()
+        tensor = torch.from_numpy(arrays).float()
         return tensor
 
     def _load_target(self, index: int) -> Tensor:
diff --git a/torchgeo/datasets/biomassters.py b/torchgeo/datasets/biomassters.py
index eff773308ae..970c5594950 100644
--- a/torchgeo/datasets/biomassters.py
+++ b/torchgeo/datasets/biomassters.py
@@ -198,8 +198,7 @@ def _load_target(self, filename: str) -> Tensor:
         with rasterio.open(os.path.join(self.root, "train_agbm", filename), "r") as src:
             arr: "np.typing.NDArray[np.float_]" = src.read()
 
-        target = torch.from_numpy(arr)
-        target = target.float()
+        target = torch.from_numpy(arr).float()
         return target
 
     def _verify(self) -> None:
diff --git a/torchgeo/datasets/cloud_cover.py b/torchgeo/datasets/cloud_cover.py
index 5f19a935beb..60bbe78e21d 100644
--- a/torchgeo/datasets/cloud_cover.py
+++ b/torchgeo/datasets/cloud_cover.py
@@ -384,7 +384,7 @@ def plot(
         else:
             n_cols = 2
 
-        image, mask = sample["image"] / torch.tensor(3000), sample["mask"]
+        image, mask = sample["image"] / 3000, sample["mask"]
 
         fig, axs = plt.subplots(nrows=1, ncols=n_cols, figsize=(10, n_cols * 5))
 
diff --git a/torchgeo/datasets/cowc.py b/torchgeo/datasets/cowc.py
index 47cd3766af6..0e0518502ad 100644
--- a/torchgeo/datasets/cowc.py
+++ b/torchgeo/datasets/cowc.py
@@ -144,8 +144,7 @@ def _load_image(self, index: int) -> Tensor:
         filename = os.path.join(self.root, self.images[index])
         with Image.open(filename) as img:
             array: "np.typing.NDArray[np.int_]" = np.array(img)
-            tensor = torch.from_numpy(array)
-            tensor = tensor.float()
+            tensor = torch.from_numpy(array).float()
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))
             return tensor
@@ -160,8 +159,7 @@ def _load_target(self, index: int) -> Tensor:
             the target
         """
         target = int(self.targets[index])
-        tensor = torch.tensor(target)
-        tensor = tensor.float()
+        tensor = torch.tensor(target).float()
         return tensor
 
     def _check_integrity(self) -> bool:
diff --git a/torchgeo/datasets/cyclone.py b/torchgeo/datasets/cyclone.py
index 49cf247e409..c9a1243e970 100644
--- a/torchgeo/datasets/cyclone.py
+++ b/torchgeo/datasets/cyclone.py
@@ -164,8 +164,7 @@ def _load_image(self, directory: str) -> Tensor:
                 img = img.resize(size=(self.size, self.size), resample=resample)
             array: "np.typing.NDArray[np.int_]" = np.array(img.convert("RGB"))
             tensor = torch.from_numpy(array)
-            tensor = tensor.permute((2, 0, 1))
-            tensor = tensor.float()
+            tensor = tensor.permute((2, 0, 1)).float()
             return tensor
 
     def _load_features(self, directory: str) -> dict[str, Any]:
diff --git a/torchgeo/datasets/etci2021.py b/torchgeo/datasets/etci2021.py
index e93a35fa9af..7dfa50fb2ab 100644
--- a/torchgeo/datasets/etci2021.py
+++ b/torchgeo/datasets/etci2021.py
@@ -204,8 +204,7 @@ def _load_image(self, path: str) -> Tensor:
         filename = os.path.join(path)
         with Image.open(filename) as img:
             array: "np.typing.NDArray[np.int_]" = np.array(img.convert("RGB"))
-            tensor = torch.from_numpy(array)
-            tensor = tensor.float()
+            tensor = torch.from_numpy(array).float()
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))
             return tensor
diff --git a/torchgeo/datasets/idtrees.py b/torchgeo/datasets/idtrees.py
index 9253cadbafd..715c3bfedab 100644
--- a/torchgeo/datasets/idtrees.py
+++ b/torchgeo/datasets/idtrees.py
@@ -494,9 +494,7 @@ def plot(
         assert len(hsi_indices) == 3
 
         def normalize(x: Tensor) -> Tensor:
-            # https://github.com/pytorch/pytorch/issues/116327
-            out: Tensor = (x - x.min()) / (x.max() - x.min())
-            return out
+            return (x - x.min()) / (x.max() - x.min())
 
         ncols = 3
 
diff --git a/torchgeo/datasets/inria.py b/torchgeo/datasets/inria.py
index b3b8c79d57a..b3ab0a6fd9c 100644
--- a/torchgeo/datasets/inria.py
+++ b/torchgeo/datasets/inria.py
@@ -135,8 +135,7 @@ def _load_image(self, path: str) -> Tensor:
         """
         with rio.open(path) as img:
             array = img.read().astype(np.int32)
-            tensor = torch.from_numpy(array)
-            tensor = tensor.float()
+            tensor = torch.from_numpy(array).float()
             return tensor
 
     def _load_target(self, path: str) -> Tensor:
@@ -151,8 +150,7 @@ def _load_target(self, path: str) -> Tensor:
         with rio.open(path) as img:
             array = img.read().astype(np.int32)
             array = np.clip(array, 0, 1)
-            mask = torch.from_numpy(array[0])
-            mask = mask.long()
+            mask = torch.from_numpy(array[0]).long()
             return mask
 
     def __len__(self) -> int:
diff --git a/torchgeo/datasets/landcoverai.py b/torchgeo/datasets/landcoverai.py
index 1962bdcbca5..8dec50b562e 100644
--- a/torchgeo/datasets/landcoverai.py
+++ b/torchgeo/datasets/landcoverai.py
@@ -364,8 +364,7 @@ def _load_image(self, id_: str) -> Tensor:
         filename = os.path.join(self.root, "output", id_ + ".jpg")
         with Image.open(filename) as img:
             array: "np.typing.NDArray[np.int_]" = np.array(img)
-            tensor = torch.from_numpy(array)
-            tensor = tensor.float()
+            tensor = torch.from_numpy(array).float()
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))
             return tensor
@@ -383,8 +382,7 @@ def _load_target(self, id_: str) -> Tensor:
         filename = os.path.join(self.root, "output", id_ + "_m.png")
         with Image.open(filename) as img:
             array: "np.typing.NDArray[np.int_]" = np.array(img.convert("L"))
-            tensor = torch.from_numpy(array)
-            tensor = tensor.long()
+            tensor = torch.from_numpy(array).long()
             return tensor
 
     def _verify_data(self) -> bool:
diff --git a/torchgeo/datasets/levircd.py b/torchgeo/datasets/levircd.py
index 0642557d187..76481fed1dd 100644
--- a/torchgeo/datasets/levircd.py
+++ b/torchgeo/datasets/levircd.py
@@ -109,8 +109,7 @@ def _load_image(self, path: str) -> Tensor:
         filename = os.path.join(path)
         with Image.open(filename) as img:
             array: "np.typing.NDArray[np.int_]" = np.array(img.convert("RGB"))
-            tensor = torch.from_numpy(array)
-            tensor = tensor.float()
+            tensor = torch.from_numpy(array).float()
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))
             return tensor
diff --git a/torchgeo/datasets/loveda.py b/torchgeo/datasets/loveda.py
index fc5b15af20a..9c7e2aaff4e 100644
--- a/torchgeo/datasets/loveda.py
+++ b/torchgeo/datasets/loveda.py
@@ -210,8 +210,7 @@ def _load_image(self, path: str) -> Tensor:
         filename = os.path.join(path)
         with Image.open(filename) as img:
             array: "np.typing.NDArray[np.int_]" = np.array(img.convert("RGB"))
-            tensor = torch.from_numpy(array)
-            tensor = tensor.float()
+            tensor = torch.from_numpy(array).float()
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))
             return tensor
diff --git a/torchgeo/datasets/mapinwild.py b/torchgeo/datasets/mapinwild.py
index f26c2564842..5eaa426d230 100644
--- a/torchgeo/datasets/mapinwild.py
+++ b/torchgeo/datasets/mapinwild.py
@@ -220,8 +220,7 @@ def _load_raster(self, filename: int, source: str) -> Tensor:
             array: "np.typing.NDArray[np.int_]" = np.stack(raw_array, axis=0)
             if array.dtype == np.uint16:
                 array = array.astype(np.int32)
-            tensor = torch.from_numpy(array)
-            tensor = tensor.float()
+            tensor = torch.from_numpy(array).float()
             return tensor
 
     def _verify(self, url: str, md5: Optional[str] = None) -> None:
diff --git a/torchgeo/datasets/nasa_marine_debris.py b/torchgeo/datasets/nasa_marine_debris.py
index b43b1881f1e..a1637f46e7d 100644
--- a/torchgeo/datasets/nasa_marine_debris.py
+++ b/torchgeo/datasets/nasa_marine_debris.py
@@ -138,8 +138,7 @@ def _load_image(self, path: str) -> Tensor:
         """
         with rasterio.open(path) as f:
             array = f.read()
-        tensor = torch.from_numpy(array)
-        tensor = tensor.float()
+        tensor = torch.from_numpy(array).float()
         return tensor
 
     def _load_target(self, path: str) -> Tensor:
diff --git a/torchgeo/datasets/nccm.py b/torchgeo/datasets/nccm.py
index 3a43ddddcc5..38a0d3eee91 100644
--- a/torchgeo/datasets/nccm.py
+++ b/torchgeo/datasets/nccm.py
@@ -3,8 +3,6 @@
 
 """Northeastern China Crop Map Dataset."""
 
-import glob
-import os
 from collections.abc import Iterable
 from typing import Any, Callable, Optional, Union
 
@@ -14,7 +12,7 @@
 from rasterio.crs import CRS
 
 from .geo import RasterDataset
-from .utils import BoundingBox, DatasetNotFoundError, download_url, extract_archive
+from .utils import BoundingBox, DatasetNotFoundError, download_url
 
 
 class NCCM(RasterDataset):
@@ -55,12 +53,24 @@ class NCCM(RasterDataset):
 
     filename_regex = r"CDL(?P<year>\d{4})_clip"
     filename_glob = "CDL*.*"
-    zipfile_glob = "13090442.zip"
 
     date_format = "%Y"
     is_image = False
-    url = "https://figshare.com/ndownloader/articles/13090442/versions/1"
-    md5 = "eae952f1b346d7e649d027e8139a76f5"
+    urls = {
+        2019: "https://figshare.com/ndownloader/files/25070540",
+        2018: "https://figshare.com/ndownloader/files/25070624",
+        2017: "https://figshare.com/ndownloader/files/25070582",
+    }
+    md5s = {
+        2019: "0d062bbd42e483fdc8239d22dba7020f",
+        2018: "b3bb4894478d10786aa798fb11693ec1",
+        2017: "d047fbe4a85341fa6248fd7e0badab6c",
+    }
+    fnames = {
+        2019: "CDL2019_clip.tif",
+        2018: "CDL2018_clip1.tif",
+        2017: "CDL2017_clip.tif",
+    }
 
     cmap = {
         0: (0, 255, 0, 255),
@@ -75,6 +85,7 @@ def __init__(
         paths: Union[str, Iterable[str]] = "data",
         crs: Optional[CRS] = None,
         res: Optional[float] = None,
+        years: list[int] = [2019],
         transforms: Optional[Callable[[dict[str, Any]], dict[str, Any]]] = None,
         cache: bool = True,
         download: bool = False,
@@ -88,6 +99,7 @@ def __init__(
                 (defaults to the CRS of the first file found)
             res: resolution of the dataset in units of CRS
                 (defaults to the resolution of the first file found)
+            years: list of years for which to use nccm layers
             transforms: a function/transform that takes an input sample
                 and returns a transformed version
             cache: if True, cache file handle to speed up repeated sampling
@@ -97,7 +109,12 @@ def __init__(
         Raises:
             DatasetNotFoundError: If dataset is not found and *download* is False.
         """
+        assert set(years) <= self.md5s.keys(), (
+            "NCCM data product only exists for the following years: "
+            f"{list(self.md5s.keys())}."
+        )
         self.paths = paths
+        self.years = years
         self.download = download
         self.checksum = checksum
         self.ordinal_map = torch.full((max(self.cmap.keys()) + 1,), 4, dtype=self.dtype)
@@ -128,37 +145,26 @@ def __getitem__(self, query: BoundingBox) -> dict[str, Any]:
 
     def _verify(self) -> None:
         """Verify the integrity of the dataset."""
-        # Check if the extracted files already exist
+        # Check if the files already exist
         if self.files:
             return
 
-        # Check if the zip file has already been downloaded
-        assert isinstance(self.paths, str)
-        pathname = os.path.join(self.paths, "**", self.zipfile_glob)
-        if glob.glob(pathname, recursive=True):
-            self._extract()
-            return
-
         # Check if the user requested to download the dataset
         if not self.download:
             raise DatasetNotFoundError(self)
 
         # Download the dataset
         self._download()
-        self._extract()
 
     def _download(self) -> None:
         """Download the dataset."""
-        filename = "13090442.zip"
-        download_url(
-            self.url, self.paths, filename, md5=self.md5 if self.checksum else None
-        )
-
-    def _extract(self) -> None:
-        """Extract the dataset."""
-        assert isinstance(self.paths, str)
-        pathname = os.path.join(self.paths, "**", self.zipfile_glob)
-        extract_archive(glob.glob(pathname, recursive=True)[0], self.paths)
+        for year in self.years:
+            download_url(
+                self.urls[year],
+                self.paths,
+                filename=self.fnames[year],
+                md5=self.md5s[year] if self.checksum else None,
+            )
 
     def plot(
         self,
diff --git a/torchgeo/datasets/oscd.py b/torchgeo/datasets/oscd.py
index db98e6ff2b4..eebe7348d0a 100644
--- a/torchgeo/datasets/oscd.py
+++ b/torchgeo/datasets/oscd.py
@@ -222,8 +222,7 @@ def _load_image(self, paths: Sequence[str]) -> Tensor:
             with Image.open(path) as img:
                 images.append(np.array(img))
         array: "np.typing.NDArray[np.int_]" = np.stack(images, axis=0).astype(np.int_)
-        tensor = torch.from_numpy(array)
-        tensor = tensor.float()
+        tensor = torch.from_numpy(array).float()
         return tensor
 
     def _load_target(self, path: str) -> Tensor:
diff --git a/torchgeo/datasets/pastis.py b/torchgeo/datasets/pastis.py
index 4e38eb4af30..84925a85022 100644
--- a/torchgeo/datasets/pastis.py
+++ b/torchgeo/datasets/pastis.py
@@ -235,8 +235,7 @@ def _load_semantic_targets(self, index: int) -> Tensor:
         # See https://github.com/VSainteuf/pastis-benchmark/blob/main/code/dataloader.py#L201 # noqa: E501
         # even though the mask file is 3 bands, we just select the first band
         array = np.load(self.files[index]["semantic"])[0].astype(np.uint8)
-        tensor = torch.from_numpy(array)
-        tensor = tensor.long()
+        tensor = torch.from_numpy(array).long()
         return tensor
 
     def _load_instance_targets(self, index: int) -> tuple[Tensor, Tensor, Tensor]:
diff --git a/torchgeo/datasets/potsdam.py b/torchgeo/datasets/potsdam.py
index a510a3f49fb..782fd7ce87d 100644
--- a/torchgeo/datasets/potsdam.py
+++ b/torchgeo/datasets/potsdam.py
@@ -192,8 +192,7 @@ def _load_image(self, index: int) -> Tensor:
         path = self.files[index]["image"]
         with rasterio.open(path) as f:
             array = f.read()
-            tensor = torch.from_numpy(array)
-            tensor = tensor.float()
+            tensor = torch.from_numpy(array).float()
             return tensor
 
     def _load_target(self, index: int) -> Tensor:
diff --git a/torchgeo/datasets/seasonet.py b/torchgeo/datasets/seasonet.py
index 82e0438f2d0..b01ad3cae68 100644
--- a/torchgeo/datasets/seasonet.py
+++ b/torchgeo/datasets/seasonet.py
@@ -359,9 +359,7 @@ def _load_target(self, index: int) -> Tensor:
         path = self.files.iloc[index][0]
         with rasterio.open(f"{path}_labels.tif") as f:
             array = f.read() - 1
-        tensor = torch.from_numpy(array)
-        tensor = tensor.squeeze()
-        tensor = tensor.long()
+        tensor = torch.from_numpy(array).squeeze().long()
         return tensor
 
     def _verify(self) -> None:
diff --git a/torchgeo/datasets/skippd.py b/torchgeo/datasets/skippd.py
index baa21c4495c..156b3f1568d 100644
--- a/torchgeo/datasets/skippd.py
+++ b/torchgeo/datasets/skippd.py
@@ -173,8 +173,7 @@ def _load_image(self, index: int) -> Tensor:
         else:
             arr = rearrange(arr, "h w c -> c h w")
 
-        tensor = torch.from_numpy(arr)
-        tensor = tensor.to(torch.float32)
+        tensor = torch.from_numpy(arr).to(torch.float32)
         return tensor
 
     def _load_features(self, index: int) -> dict[str, Union[str, Tensor]]:
diff --git a/torchgeo/datasets/spacenet.py b/torchgeo/datasets/spacenet.py
index 5cb5d1e2356..c6780e1971c 100644
--- a/torchgeo/datasets/spacenet.py
+++ b/torchgeo/datasets/spacenet.py
@@ -200,8 +200,7 @@ def _load_mask(
                 dtype=np.uint8,
             )
 
-        mask = torch.from_numpy(mask_data)
-        mask = mask.long()
+        mask = torch.from_numpy(mask_data).long()
 
         return mask
 
@@ -727,8 +726,7 @@ def _load_mask(
                 dtype=np.uint8,
             )
 
-        mask = torch.from_numpy(mask_data)
-        mask = mask.long()
+        mask = torch.from_numpy(mask_data).long()
         return mask
 
     def plot(
diff --git a/torchgeo/datasets/ssl4eo_benchmark.py b/torchgeo/datasets/ssl4eo_benchmark.py
index a3f769aa789..003cccf2fcf 100644
--- a/torchgeo/datasets/ssl4eo_benchmark.py
+++ b/torchgeo/datasets/ssl4eo_benchmark.py
@@ -306,8 +306,7 @@ def _load_image(self, path: str) -> Tensor:
             image
         """
         with rasterio.open(path) as src:
-            image = torch.from_numpy(src.read())
-            image = image.float()
+            image = torch.from_numpy(src.read()).float()
         return image
 
     def _load_mask(self, path: str) -> Tensor:
@@ -320,8 +319,7 @@ def _load_mask(self, path: str) -> Tensor:
             mask
         """
         with rasterio.open(path) as src:
-            mask = torch.from_numpy(src.read())
-            mask = mask.long()
+            mask = torch.from_numpy(src.read()).long()
         mask = self.ordinal_map[mask]
         return mask
 
diff --git a/torchgeo/datasets/usavars.py b/torchgeo/datasets/usavars.py
index 81823cd848d..f33d268d4ce 100644
--- a/torchgeo/datasets/usavars.py
+++ b/torchgeo/datasets/usavars.py
@@ -181,8 +181,7 @@ def _load_image(self, path: str) -> Tensor:
         """
         with rasterio.open(path) as f:
             array: "np.typing.NDArray[np.int_]" = f.read()
-            tensor = torch.from_numpy(array)
-            tensor = tensor.float()
+            tensor = torch.from_numpy(array).float()
             return tensor
 
     def _verify(self) -> None:
diff --git a/torchgeo/losses/qr.py b/torchgeo/losses/qr.py
index 9bffc4a915f..ecffa33e9fa 100644
--- a/torchgeo/losses/qr.py
+++ b/torchgeo/losses/qr.py
@@ -5,7 +5,6 @@
 
 import torch
 import torch.nn.functional as F
-from torch import Tensor
 from torch.nn.modules import Module
 
 
@@ -29,16 +28,12 @@ def forward(self, probs: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
             qr loss
         """
         q = probs
-        # https://github.com/pytorch/pytorch/issues/116327
-        q_bar: Tensor = q.mean(dim=(0, 2, 3))
-        log_q_bar = torch.log(q_bar)
-        qbar_log_S: Tensor = q_bar * log_q_bar
-        qbar_log_S = qbar_log_S.sum()
+        q_bar = q.mean(dim=(0, 2, 3))
+        qbar_log_S = (q_bar * torch.log(q_bar)).sum()
 
-        q_log_p = torch.einsum("bcxy,bcxy->bxy", q, torch.log(target))
-        q_log_p = q_log_p.mean()
+        q_log_p = torch.einsum("bcxy,bcxy->bxy", q, torch.log(target)).mean()
 
-        loss: Tensor = qbar_log_S - q_log_p
+        loss = qbar_log_S - q_log_p
         return loss
 
 
@@ -67,7 +62,6 @@ def forward(self, probs: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         z = q / q.norm(p=1, dim=(0, 2, 3), keepdim=True).clamp_min(1e-12).expand_as(q)
         r = F.normalize(z * target, p=1, dim=1)
 
-        loss = torch.einsum("bcxy,bcxy->bxy", r, torch.log(r) - torch.log(q))
-        loss = loss.mean()
+        loss = torch.einsum("bcxy,bcxy->bxy", r, torch.log(r) - torch.log(q)).mean()
 
         return loss
diff --git a/torchgeo/models/rcf.py b/torchgeo/models/rcf.py
index ebf46bcc610..59f42223cf1 100644
--- a/torchgeo/models/rcf.py
+++ b/torchgeo/models/rcf.py
@@ -94,7 +94,7 @@ def __init__(
             ),
         )
         self.register_buffer(
-            "biases", torch.zeros(num_patches, requires_grad=False) + torch.tensor(bias)
+            "biases", torch.zeros(num_patches, requires_grad=False) + bias
         )
 
         if mode == "empirical":
diff --git a/torchgeo/samplers/batch.py b/torchgeo/samplers/batch.py
index a60a787444a..ef9bb05f10f 100644
--- a/torchgeo/samplers/batch.py
+++ b/torchgeo/samplers/batch.py
@@ -128,7 +128,7 @@ def __init__(
         # torch.multinomial requires float probabilities > 0
         self.areas = torch.tensor(areas, dtype=torch.float)
         if torch.sum(self.areas) == 0:
-            self.areas += torch.tensor(1)
+            self.areas += 1
 
     def __iter__(self) -> Iterator[list[BoundingBox]]:
         """Return the indices of a dataset.
diff --git a/torchgeo/samplers/single.py b/torchgeo/samplers/single.py
index 1180044c6e4..7251f4b274f 100644
--- a/torchgeo/samplers/single.py
+++ b/torchgeo/samplers/single.py
@@ -128,7 +128,7 @@ def __init__(
         # torch.multinomial requires float probabilities > 0
         self.areas = torch.tensor(areas, dtype=torch.float)
         if torch.sum(self.areas) == 0:
-            self.areas += torch.tensor(1)
+            self.areas += 1
 
     def __iter__(self) -> Iterator[BoundingBox]:
         """Return the index of a dataset.
diff --git a/torchgeo/trainers/byol.py b/torchgeo/trainers/byol.py
index 68bdb6c9c43..d6c0b62765e 100644
--- a/torchgeo/trainers/byol.py
+++ b/torchgeo/trainers/byol.py
@@ -343,7 +343,7 @@ def configure_models(self) -> None:
                 _, state_dict = utils.extract_backbone(weights)
             else:
                 state_dict = get_weight(weights).get_state_dict(progress=True)
-            backbone = utils.load_state_dict(backbone, state_dict)
+            utils.load_state_dict(backbone, state_dict)
 
         self.model = BYOL(backbone, in_channels=in_channels, image_size=(224, 224))
 
diff --git a/torchgeo/trainers/classification.py b/torchgeo/trainers/classification.py
index 9ac312051c7..5d8d10c9dce 100644
--- a/torchgeo/trainers/classification.py
+++ b/torchgeo/trainers/classification.py
@@ -137,7 +137,7 @@ def configure_models(self) -> None:
                 _, state_dict = utils.extract_backbone(weights)
             else:
                 state_dict = get_weight(weights).get_state_dict(progress=True)
-            self.model = utils.load_state_dict(self.model, state_dict)
+            utils.load_state_dict(self.model, state_dict)
 
         # Freeze backbone and unfreeze classifier head
         if self.hparams["freeze_backbone"]:
diff --git a/torchgeo/trainers/moco.py b/torchgeo/trainers/moco.py
index d2621a8da74..4dbc1e453c9 100644
--- a/torchgeo/trainers/moco.py
+++ b/torchgeo/trainers/moco.py
@@ -261,7 +261,7 @@ def configure_models(self) -> None:
                 _, state_dict = utils.extract_backbone(weights)
             else:
                 state_dict = get_weight(weights).get_state_dict(progress=True)
-            self.backbone = utils.load_state_dict(self.backbone, state_dict)
+            utils.load_state_dict(self.backbone, state_dict)
 
         # Create projection (and prediction) head
         batch_norm = version == 3
diff --git a/torchgeo/trainers/regression.py b/torchgeo/trainers/regression.py
index c58f033b5bc..9cc2ea56441 100644
--- a/torchgeo/trainers/regression.py
+++ b/torchgeo/trainers/regression.py
@@ -128,7 +128,7 @@ def configure_models(self) -> None:
                 _, state_dict = utils.extract_backbone(weights)
             else:
                 state_dict = get_weight(weights).get_state_dict(progress=True)
-            self.model = utils.load_state_dict(self.model, state_dict)
+            utils.load_state_dict(self.model, state_dict)
 
         # Freeze backbone and unfreeze classifier head
         if self.hparams["freeze_backbone"]:
diff --git a/torchgeo/trainers/simclr.py b/torchgeo/trainers/simclr.py
index a889be1c96f..27719eda224 100644
--- a/torchgeo/trainers/simclr.py
+++ b/torchgeo/trainers/simclr.py
@@ -172,7 +172,7 @@ def configure_models(self) -> None:
                 _, state_dict = utils.extract_backbone(weights)
             else:
                 state_dict = get_weight(weights).get_state_dict(progress=True)
-            self.backbone = utils.load_state_dict(self.backbone, state_dict)
+            utils.load_state_dict(self.backbone, state_dict)
 
         # Create projection head
         input_dim = self.backbone.num_features
diff --git a/torchgeo/trainers/utils.py b/torchgeo/trainers/utils.py
index e1b6678ed73..b5cd8f1e923 100644
--- a/torchgeo/trainers/utils.py
+++ b/torchgeo/trainers/utils.py
@@ -71,7 +71,9 @@ def _get_input_layer_name_and_module(model: Module) -> tuple[str, Module]:
     return key, module
 
 
-def load_state_dict(model: Module, state_dict: "OrderedDict[str, Tensor]") -> Module:
+def load_state_dict(
+    model: Module, state_dict: "OrderedDict[str, Tensor]"
+) -> tuple[list[str], list[str]]:
     """Load pretrained resnet weights to a model.
 
     Args:
@@ -79,7 +81,7 @@ def load_state_dict(model: Module, state_dict: "OrderedDict[str, Tensor]") -> Mo
         state_dict: dict containing tensor parameters
 
     Returns:
-        the model with pretrained weights
+        The missing and unexpected keys
 
     Warns:
         If input channels in model != pretrained model input channels
@@ -115,8 +117,10 @@ def load_state_dict(model: Module, state_dict: "OrderedDict[str, Tensor]") -> Mo
             state_dict[output_module_key + ".bias"],
         )
 
-    model.load_state_dict(state_dict, strict=False)
-    return model
+    missing_keys: list[str]
+    unexpected_keys: list[str]
+    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
+    return missing_keys, unexpected_keys
 
 
 def reinit_initial_conv_layer(
diff --git a/torchgeo/transforms/color.py b/torchgeo/transforms/color.py
index e89ed471a4a..5459fc2f854 100644
--- a/torchgeo/transforms/color.py
+++ b/torchgeo/transforms/color.py
@@ -70,11 +70,8 @@ def apply_transform(
         Returns:
             The augmented input.
         """
-        weights = flags["weights"]
-        weights = weights[..., :, None, None]
-        weights = weights.to(input.device)
-        out: Tensor = input * weights
+        weights = flags["weights"][..., :, None, None].to(input.device)
+        out = input * weights
         out = out.sum(dim=-3)
-        out = out.unsqueeze(-3)
-        out = out.expand(input.shape)
+        out = out.unsqueeze(-3).expand(input.shape)
         return out