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diff --git a/tests/data/chesapeake/BAYWIDE/Baywide_13Class_20132014.zip b/tests/data/chesapeake/BAYWIDE/Baywide_13Class_20132014.zip
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diff --git a/tests/datasets/test_chesapeake.py b/tests/datasets/test_chesapeake.py
new file mode 100644
index 00000000000..c89c4440807
--- /dev/null
+++ b/tests/datasets/test_chesapeake.py
@@ -0,0 +1,78 @@
+import os
+import shutil
+from pathlib import Path
+from typing import Generator
+
+import matplotlib.pyplot as plt
+import pytest
+import torch
+from _pytest.fixtures import SubRequest
+from pytest import MonkeyPatch
+from rasterio.crs import CRS
+
+import torchgeo.datasets.utils
+from torchgeo.datasets import BoundingBox, Chesapeake13, ZipDataset
+from torchgeo.transforms import Identity
+
+
+def download_url(url: str, root: str, *args: str) -> None:
+    shutil.copy(url, root)
+
+
+class TestChesapeake13:
+    @pytest.fixture
+    def dataset(
+        self,
+        monkeypatch: Generator[MonkeyPatch, None, None],
+        tmp_path: Path,
+        request: SubRequest,
+    ) -> Chesapeake13:
+        monkeypatch.setattr(  # type: ignore[attr-defined]
+            torchgeo.datasets.utils, "download_url", download_url
+        )
+        md5 = "8363639b51651cc1de2bdbeb2be4f9b1"
+        monkeypatch.setattr(Chesapeake13, "md5", md5)  # type: ignore[attr-defined]
+        url = os.path.join(
+            "tests", "data", "chesapeake", "BAYWIDE", "Baywide_13Class_20132014.zip"
+        )
+        monkeypatch.setattr(Chesapeake13, "url", url)  # type: ignore[attr-defined]
+        monkeypatch.setattr(  # type: ignore[attr-defined]
+            plt, "show", lambda *args: None
+        )
+        (tmp_path / "chesapeake" / "BAYWIDE").mkdir(parents=True)
+        root = str(tmp_path)
+        transforms = Identity()
+        return Chesapeake13(root, transforms=transforms, download=True, checksum=True)
+
+    def test_getitem(self, dataset: Chesapeake13) -> None:
+        x = dataset[dataset.bounds]
+        assert isinstance(x, dict)
+        assert isinstance(x["crs"], CRS)
+        assert isinstance(x["masks"], torch.Tensor)
+
+    def test_add(self, dataset: Chesapeake13) -> None:
+        ds = dataset + dataset
+        assert isinstance(ds, ZipDataset)
+
+    def test_already_downloaded(self, dataset: Chesapeake13) -> None:
+        Chesapeake13(root=os.path.dirname(os.path.dirname(dataset.root)), download=True)
+
+    def test_plot(self, dataset: Chesapeake13) -> None:
+        query = dataset.bounds
+        x = dataset[query]
+        dataset.plot(x["masks"])
+
+    def test_url(self) -> None:
+        ds = Chesapeake13(os.path.join("tests", "data"))
+        assert "cicwebresources.blob.core.windows.net" in ds.url
+
+    def test_not_downloaded(self, tmp_path: Path) -> None:
+        with pytest.raises(RuntimeError, match="Dataset not found or corrupted."):
+            Chesapeake13(str(tmp_path))
+
+    def test_invalid_query(self, dataset: Chesapeake13) -> None:
+        query = BoundingBox(0, 0, 0, 0, 0, 0)
+        with pytest.raises(
+            IndexError, match="query: .* is not within bounds of the index:"
+        ):
+            dataset[query]
diff --git a/torchgeo/datasets/__init__.py b/torchgeo/datasets/__init__.py
index 69ad6e86ab3..fbc350480eb 100644
--- a/torchgeo/datasets/__init__.py
+++ b/torchgeo/datasets/__init__.py
@@ -2,6 +2,18 @@
 
 from .benin_cashews import BeninSmallHolderCashews
 from .cdl import CDL
+from .chesapeake import (
+    Chesapeake,
+    Chesapeake7,
+    Chesapeake13,
+    ChesapeakeDC,
+    ChesapeakeDE,
+    ChesapeakeMD,
+    ChesapeakeNY,
+    ChesapeakePA,
+    ChesapeakeVA,
+    ChesapeakeWV,
+)
 from .cowc import COWC, COWCCounting, COWCDetection
 from .cv4a_kenya_crop_type import CV4AKenyaCropType
 from .cyclone import TropicalCycloneWindEstimation
@@ -31,6 +43,16 @@
     "BoundingBox",
     "CDL",
     "collate_dict",
+    "Chesapeake",
+    "Chesapeake7",
+    "Chesapeake13",
+    "ChesapeakeDC",
+    "ChesapeakeDE",
+    "ChesapeakeMD",
+    "ChesapeakeNY",
+    "ChesapeakePA",
+    "ChesapeakeVA",
+    "ChesapeakeWV",
     "COWC",
     "COWCCounting",
     "COWCDetection",
diff --git a/torchgeo/datasets/chesapeake.py b/torchgeo/datasets/chesapeake.py
new file mode 100644
index 00000000000..12e0ef5b11e
--- /dev/null
+++ b/torchgeo/datasets/chesapeake.py
@@ -0,0 +1,338 @@
+"""Chesapeake Bay High-Resolution Land Cover Project dataset."""
+
+import abc
+import os
+import sys
+from typing import Any, Callable, Dict, Optional
+
+import matplotlib.pyplot as plt
+import numpy as np
+import rasterio
+import torch
+from rasterio.crs import CRS
+from rasterio.vrt import WarpedVRT
+from rtree.index import Index, Property
+from torch import Tensor
+
+from .geo import GeoDataset
+from .utils import BoundingBox, check_integrity, download_and_extract_archive
+
+_crs = CRS.from_wkt(
+    """
+PROJCS["USA_Contiguous_Albers_Equal_Area_Conic_USGS_version",
+    GEOGCS["NAD83",
+        DATUM["North_American_Datum_1983",
+            SPHEROID["GRS 1980",6378137,298.257222101004,
+                AUTHORITY["EPSG","7019"]],
+            AUTHORITY["EPSG","6269"]],
+        PRIMEM["Greenwich",0],
+        UNIT["degree",0.0174532925199433,
+            AUTHORITY["EPSG","9122"]],
+        AUTHORITY["EPSG","4269"]],
+    PROJECTION["Albers_Conic_Equal_Area"],
+    PARAMETER["latitude_of_center",23],
+    PARAMETER["longitude_of_center",-96],
+    PARAMETER["standard_parallel_1",29.5],
+    PARAMETER["standard_parallel_2",45.5],
+    PARAMETER["false_easting",0],
+    PARAMETER["false_northing",0],
+    UNIT["metre",1,
+        AUTHORITY["EPSG","9001"]],
+    AXIS["Easting",EAST],
+    AXIS["Northing",NORTH]]
+"""
+)
+
+
+class Chesapeake(GeoDataset, abc.ABC):
+    """Abstract base class for all Chesapeake datasets.
+
+    `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
+    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>`_
+
+    If you use this dataset in your research, please cite the following paper:
+
+    * https://doi.org/10.1109/cvpr.2019.01301
+    """
+
+    @property
+    @abc.abstractmethod
+    def base_folder(self) -> str:
+        """Subdirectory to find/store dataset in."""
+
+    @property
+    @abc.abstractmethod
+    def filename(self) -> str:
+        """Filename to find/store dataset in."""
+
+    @property
+    @abc.abstractmethod
+    def zipfile(self) -> str:
+        """Name of zipfile in download URL."""
+
+    @property
+    @abc.abstractmethod
+    def md5(self) -> str:
+        """MD5 checksum to verify integrity of dataset."""
+
+    @property
+    def url(self) -> str:
+        """URL to download dataset from."""
+        url = "https://cicwebresources.blob.core.windows.net/chesapeakebaylandcover"
+        url += f"/{self.base_folder}/{self.zipfile}"
+        return url
+
+    def __init__(
+        self,
+        root: str,
+        crs: CRS = _crs,
+        transforms: Optional[Callable[[Any], Any]] = None,
+        download: bool = False,
+        checksum: bool = False,
+    ) -> None:
+        """Initialize a new Chesapeake dataset instance.
+
+        Args:
+            root: root directory where dataset can be found
+            crs: :term:`coordinate reference system (CRS)` to project to
+            transforms: a function/transform that takes input sample and its target as
+                entry and returns a transformed version
+            download: if True, download dataset and store it in the root directory
+            checksum: if True, check the MD5 of the downloaded files (may be slow)
+        """
+        self.root = os.path.join(root, "chesapeake", self.base_folder)
+        self.crs = crs
+        self.transforms = transforms
+        self.checksum = checksum
+
+        if download:
+            self._download()
+
+        if not self._check_integrity():
+            raise RuntimeError(
+                "Dataset not found or corrupted. "
+                + "You can use download=True to download it"
+            )
+
+        # Create an R-tree to index the dataset
+        self.index = Index(interleaved=False, properties=Property(dimension=3))
+        filename = os.path.join(self.root, self.filename)
+        with rasterio.open(filename) as src:
+            with rasterio.open(filename) as src:
+                cmap = src.colormap(1)
+                with WarpedVRT(src, crs=self.crs) as vrt:
+                    minx, miny, maxx, maxy = vrt.bounds
+        mint = 0
+        maxt = sys.maxsize
+        coords = (minx, maxx, miny, maxy, mint, maxt)
+        self.index.insert(0, coords, filename)
+        self.cmap = np.array([cmap[i] for i in range(len(cmap))])
+
+    def __getitem__(self, query: BoundingBox) -> Dict[str, Any]:
+        """Retrieve labels and metadata indexed by query.
+
+        Args:
+            query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index
+
+        Returns:
+            sample of labels and metadata at that index
+
+        Raises:
+            IndexError: if query is not within bounds of the index
+        """
+        if not query.intersects(self.bounds):
+            raise IndexError(
+                f"query: {query} is not within bounds of the index: {self.bounds}"
+            )
+
+        hits = self.index.intersection(query, objects=True)
+        filename = next(hits).object
+        with rasterio.open(filename) as src:
+            with WarpedVRT(src, crs=self.crs) as vrt:
+                window = rasterio.windows.from_bounds(
+                    query.minx,
+                    query.miny,
+                    query.maxx,
+                    query.maxy,
+                    transform=vrt.transform,
+                )
+                masks = vrt.read(window=window)
+        sample = {
+            "masks": torch.tensor(masks),  # type: ignore[attr-defined]
+            "crs": self.crs,
+            "bbox": query,
+        }
+
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+
+        return sample
+
+    def _check_integrity(self) -> bool:
+        """Check integrity of dataset.
+
+        Returns:
+            True if dataset MD5s match, else False
+        """
+        integrity: bool = check_integrity(
+            os.path.join(self.root, self.zipfile),
+            self.md5 if self.checksum else None,
+        )
+        return integrity
+
+    def _download(self) -> None:
+        """Download the dataset and extract it."""
+        if self._check_integrity():
+            print("Files already downloaded and verified")
+            return
+
+        download_and_extract_archive(
+            self.url,
+            self.root,
+            filename=self.zipfile,
+            md5=self.md5,
+        )
+
+    def plot(self, image: Tensor) -> None:
+        """Plot an image on a map.
+
+        Args:
+            image: the image to plot
+        """
+        # Convert from class labels to RGBA values
+        array = image.squeeze().numpy()
+        array = self.cmap[array]
+
+        # Plot the image
+        ax = plt.axes()
+        ax.imshow(array, origin="lower")
+        ax.axis("off")
+        plt.show()
+
+
+class Chesapeake7(Chesapeake):
+    """Complete 7-class dataset.
+
+    This version of the dataset is composed of 7 classes:
+
+    0. No Data: Background values
+    1. Barren: Areas devoid of vegetation consisting of natural earthen material
+    2. Impervious Roads: Impervious surfaces that are used for transportation
+    3. Impervious Surfaces: Human-constructed surfaces less than 2 meters in height
+    4. Low Vegetation: Plant material less than 2 meters in height including lawns
+    5. Tree Canopy: Deciduous and evergreen woody vegetation over 3-5 meters in height
+    6. Tree Canopy over Impervious Surfaces: Tree cover overlapping impervious surfaces
+    7. Water: All areas of open water including ponds, rivers, and lakes
+    """
+
+    # TODO: make sure these class numbers are correct
+
+    base_folder = "BAYWIDE"
+    filename = "Baywide_7Class_20132014.tif"
+    zipfile = "Baywide_7Class_20132014.zip"
+    md5 = ""
+
+
+class Chesapeake13(Chesapeake):
+    """Complete 13-class dataset.
+
+    This version of the dataset is composed of 13 classes:
+
+    0. No Data: Background values
+    1. Water: All areas of open water including ponds, rivers, and lakes
+    2. Wetlands: Low vegetation areas located along marine or estuarine regions
+    3. Tree Canopy: Deciduous and evergreen woody vegetation over 3-5 meters in height
+    4. Shrubland: Heterogeneous woody vegetation including shrubs and young trees
+    5. Low Vegetation: Plant material less than 2 meters in height including lawns
+    6. Barren: Areas devoid of vegetation consisting of natural earthen material
+    7. Structures: Human-constructed objects made of impervious materials
+    8. Impervious Surfaces: Human-constructed surfaces less than 2 meters in height
+    9. Impervious Roads: Impervious surfaces that are used for transportation
+    10. Tree Canopy over Structures: Tree cover overlapping impervious structures
+    11. Tree Canopy over Impervious Surfaces: Tree cover overlapping impervious surfaces
+    12. Tree Canopy over Impervious Roads: Tree cover overlapping impervious roads
+    13. Aberdeen Proving Ground: U.S. Army facility with no labels
+    """
+
+    base_folder = "BAYWIDE"
+    filename = "Baywide_13Class_20132014.tif"
+    zipfile = "Baywide_13Class_20132014.zip"
+    md5 = "7e51118923c91e80e6e268156d25a4b9"
+
+
+class ChesapeakeDC(Chesapeake):
+    """This subset of the dataset contains data only for Washington, D.C."""
+
+    base_folder = "DC"
+    filename = os.path.join("DC_11001", "DC_11001.img")
+    zipfile = "DC_11001.zip"
+    md5 = "ed06ba7570d2955e8857d7d846c53b06"
+
+
+class ChesapeakeDE(Chesapeake):
+    """This subset of the dataset contains data only for Delaware."""
+
+    base_folder = "DE"
+    filename = "DE_STATEWIDE.tif"
+    zipfile = "_DE_STATEWIDE.zip"
+    md5 = "5e12eff3b6950c01092c7e480b38e544"
+
+
+class ChesapeakeMD(Chesapeake):
+    """This subset of the dataset contains data only for Maryland."""
+
+    base_folder = "MD"
+    filename = "MD_STATEWIDE.tif"
+    zipfile = "_MD_STATEWIDE.zip"
+    md5 = "40c7cd697a887f2ffdb601b5c114e567"
+
+
+class ChesapeakeNY(Chesapeake):
+    """This subset of the dataset contains data only for New York."""
+
+    base_folder = "NY"
+    filename = "NY_STATEWIDE.tif"
+    zipfile = "_NY_STATEWIDE.zip"
+    md5 = "1100078c526616454ef2e508affda915"
+
+
+class ChesapeakePA(Chesapeake):
+    """This subset of the dataset contains data only for Pennsylvania."""
+
+    base_folder = "PA"
+    filename = "PA_STATEWIDE.tif"
+    zipfile = "_PA_STATEWIDE.zip"
+    md5 = ""
+
+
+class ChesapeakeVA(Chesapeake):
+    """This subset of the dataset contains data only for Virginia."""
+
+    base_folder = "VA"
+    filename = "CIC2014_VA_STATEWIDE.tif"
+    zipfile = "_VA_STATEWIDE.zip"
+    md5 = "6f2c97deaf73bb3e1ea9b21bd7a3fc8e"
+
+
+class ChesapeakeWV(Chesapeake):
+    """This subset of the dataset contains data only for West Virginia."""
+
+    base_folder = "WV"
+    filename = "WV_STATEWIDE.tif"
+    zipfile = "_WV_STATEWIDE.zip"
+    md5 = "350621ea293651fbc557a1c3e3c64cc3"