diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst
index be91f41d433..8ce91fafc68 100644
--- a/docs/api/datasets.rst
+++ b/docs/api/datasets.rst
@@ -294,6 +294,10 @@ DL4GAM
 ^^^^^^
 .. autoclass:: DL4GAMAlps
 
+DOTA
+^^^^
+.. autoclass:: DOTA
+
 ETCI2021 Flood Detection
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
diff --git a/docs/api/datasets/non_geo_datasets.csv b/docs/api/datasets/non_geo_datasets.csv
index e7b63a774fa..5d93b2af150 100644
--- a/docs/api/datasets/non_geo_datasets.csv
+++ b/docs/api/datasets/non_geo_datasets.csv
@@ -16,6 +16,7 @@ Dataset,Task,Source,License,# Samples,# Classes,Size (px),Resolution (m),Bands
 `DIOR`_,OD,Aerial,"CC-BY-NC-4.0","23,463",20,"800x800",0.5,RGB
 `Digital Typhoon`_,"C, R",Himawari,"CC-BY-4.0","189,364",8,512,5000,Infrared
 `DL4GAM`_,S,"Sentinel-2","CC-BY-4.0","2,251 or 11,440","2","256x256","10","MSI"
+`DOTA`_,OD,"Google Earth, Gaofen-2, Jilin-1, CycloMedia B.V.","non-commercial","5,229",15,"1000--5000",RGB
 `ETCI2021 Flood Detection`_,S,Sentinel-1,-,"66,810",2,256x256,5--20,SAR
 `EuroSAT`_,C,Sentinel-2,"MIT","27,000",10,64x64,10,MSI
 `EverWatch`_,OD,Aerial,"CC0-1.0","5,325",7,"1,500x1500p",0.01,RGB
diff --git a/tests/data/dota/data.py b/tests/data/dota/data.py
new file mode 100644
index 00000000000..b2b1e46f7ea
--- /dev/null
+++ b/tests/data/dota/data.py
@@ -0,0 +1,159 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import hashlib
+import os
+import shutil
+import tarfile
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from PIL import Image
+
+
+def create_dummy_image(path: Path, size: tuple[int, int] = (64, 64)) -> None:
+    """Create small dummy image."""
+    img = np.random.randint(0, 255, (*size, 3), dtype=np.uint8)
+    Image.fromarray(img).save(path)
+
+
+def create_annotation_file(
+    path: Path, is_hbb: bool = False, no_boxes: bool = False
+) -> None:
+    """Create dummy annotation file with scaled coordinates."""
+    if is_hbb:
+        # Horizontal boxes scaled for 64x64
+        boxes = [
+            '10.0 10.0 20.0 10.0 20.0 20.0 10.0 20.0 plane 0\n',
+            '30.0 30.0 40.0 30.0 40.0 40.0 30.0 40.0 ship 0\n',
+        ]
+    else:
+        # Oriented boxes scaled for 64x64
+        boxes = [
+            '10.0 10.0 20.0 12.0 18.0 20.0 8.0 18.0 plane 0\n',
+            '30.0 30.0 42.0 32.0 40.0 40.0 28.0 38.0 ship 0\n',
+        ]
+
+    if no_boxes:
+        boxes = []
+
+    with open(path, 'w') as f:
+        f.write('imagesource:dummy\n')
+        f.write('gsd:1.0\n')
+        f.writelines(boxes)
+
+
+def create_test_data(root: Path) -> None:
+    """Create DOTA test dataset."""
+    splits = ['train', 'val']
+    versions = ['1.0', '1.5', '2.0']
+
+    # Create directory structure
+    for split in splits:
+        num_samples = 3 if split == 'train' else 2
+
+        if os.path.exists(root / split):
+            shutil.rmtree(root / split)
+        for version in versions:
+            # Create images and annotations
+            for i in range(num_samples):
+                img_name = f'P{i:04d}.png'
+                ann_name = f'P{i:04d}.txt'
+
+                # Create directories
+                (root / split / 'images').mkdir(parents=True, exist_ok=True)
+                (root / split / 'annotations' / f'version{version}').mkdir(
+                    parents=True, exist_ok=True
+                )
+
+                # Create files
+                if i == 0:
+                    no_boxes = True
+                else:
+                    no_boxes = False
+                create_dummy_image(root / split / 'images' / img_name)
+                create_annotation_file(
+                    root / split / 'annotations' / f'version{version}' / ann_name,
+                    False,
+                    no_boxes,
+                )
+
+            # Create tar archives
+            for type_ in ['images', 'annotations']:
+                src_dir = root / split / type_
+                if src_dir.exists():
+                    tar_name = f'dotav{version}_{type_}_{split}.tar.gz'
+                    with tarfile.open(root / tar_name, 'w:gz') as tar:
+                        tar.add(src_dir, arcname=f'{split}/{type_}')
+
+            # print md5sums
+            def md5(fname: str) -> str:
+                hash_md5 = hashlib.md5()
+                with open(fname, 'rb') as f:
+                    for chunk in iter(lambda: f.read(4096), b''):
+                        hash_md5.update(chunk)
+                return hash_md5.hexdigest()
+
+    print('file_info = {')
+    for split in splits:
+        print(f"    '{split}': {{")
+
+        for type_ in ['images', 'annotations']:
+            print(f"        '{type_}': {{")
+
+            for version in versions:
+                tar_name = f'dotav{version}_{type_}_{split}.tar.gz'
+                checksum = md5(tar_name)
+
+                # version 1.0 and 1.5 have the same images
+                if version == '1.5' and type_ == 'images':
+                    version_filename = '1.0'
+                else:
+                    version_filename = version
+
+                print(f"            '{version}': {{")
+                print(
+                    f"                'filename': 'dotav{version_filename}_{type_}_{split}.tar.gz',"
+                )
+                print(f"                'md5': '{checksum}',")
+                print('            },')
+
+            print('        },')
+
+        print('    },')
+    print('}')
+
+
+def create_sample_df(root: Path) -> pd.DataFrame:
+    """Create sample DataFrame for test data."""
+    rows = []
+    splits = ['train', 'val']
+    versions = ['1.0', '1.5', '2.0']
+
+    for split in splits:
+        num_samples = 3 if split == 'train' else 2
+        for version in versions:
+            for i in range(num_samples):
+                img_name = f'P{i:04d}.png'
+                ann_name = f'P{i:04d}.txt'
+
+                row = {
+                    'image_path': str(Path(split) / 'images' / img_name),
+                    'annotation_path': str(
+                        Path(split) / 'annotations' / f'version{version}' / ann_name
+                    ),
+                    'split': split,
+                    'version': version,
+                }
+                rows.append(row)
+
+    df = pd.DataFrame(rows)
+    df.to_csv(root / 'samples.csv')
+    return df
+
+
+if __name__ == '__main__':
+    root = Path('.')
+    create_test_data(root)
+    df = create_sample_df(root)
diff --git a/tests/data/dota/dotav1.0_annotations_train.tar.gz b/tests/data/dota/dotav1.0_annotations_train.tar.gz
new file mode 100644
index 00000000000..0d23ac13202
Binary files /dev/null and b/tests/data/dota/dotav1.0_annotations_train.tar.gz differ
diff --git a/tests/data/dota/dotav1.0_annotations_val.tar.gz b/tests/data/dota/dotav1.0_annotations_val.tar.gz
new file mode 100644
index 00000000000..d432b12872c
Binary files /dev/null and b/tests/data/dota/dotav1.0_annotations_val.tar.gz differ
diff --git a/tests/data/dota/dotav1.0_images_train.tar.gz b/tests/data/dota/dotav1.0_images_train.tar.gz
new file mode 100644
index 00000000000..82c9ccf1c04
Binary files /dev/null and b/tests/data/dota/dotav1.0_images_train.tar.gz differ
diff --git a/tests/data/dota/dotav1.0_images_val.tar.gz b/tests/data/dota/dotav1.0_images_val.tar.gz
new file mode 100644
index 00000000000..81c52c64200
Binary files /dev/null and b/tests/data/dota/dotav1.0_images_val.tar.gz differ
diff --git a/tests/data/dota/dotav1.5_annotations_train.tar.gz b/tests/data/dota/dotav1.5_annotations_train.tar.gz
new file mode 100644
index 00000000000..a63f3d683fe
Binary files /dev/null and b/tests/data/dota/dotav1.5_annotations_train.tar.gz differ
diff --git a/tests/data/dota/dotav1.5_annotations_val.tar.gz b/tests/data/dota/dotav1.5_annotations_val.tar.gz
new file mode 100644
index 00000000000..0acce6ea66e
Binary files /dev/null and b/tests/data/dota/dotav1.5_annotations_val.tar.gz differ
diff --git a/tests/data/dota/dotav1.5_images_train.tar.gz b/tests/data/dota/dotav1.5_images_train.tar.gz
new file mode 100644
index 00000000000..21765e3c65b
Binary files /dev/null and b/tests/data/dota/dotav1.5_images_train.tar.gz differ
diff --git a/tests/data/dota/dotav1.5_images_val.tar.gz b/tests/data/dota/dotav1.5_images_val.tar.gz
new file mode 100644
index 00000000000..ac7d73f0614
Binary files /dev/null and b/tests/data/dota/dotav1.5_images_val.tar.gz differ
diff --git a/tests/data/dota/dotav2.0_annotations_train.tar.gz b/tests/data/dota/dotav2.0_annotations_train.tar.gz
new file mode 100644
index 00000000000..945a7ced291
Binary files /dev/null and b/tests/data/dota/dotav2.0_annotations_train.tar.gz differ
diff --git a/tests/data/dota/dotav2.0_annotations_val.tar.gz b/tests/data/dota/dotav2.0_annotations_val.tar.gz
new file mode 100644
index 00000000000..d3f95593b0d
Binary files /dev/null and b/tests/data/dota/dotav2.0_annotations_val.tar.gz differ
diff --git a/tests/data/dota/dotav2.0_images_train.tar.gz b/tests/data/dota/dotav2.0_images_train.tar.gz
new file mode 100644
index 00000000000..d3280139fd1
Binary files /dev/null and b/tests/data/dota/dotav2.0_images_train.tar.gz differ
diff --git a/tests/data/dota/dotav2.0_images_val.tar.gz b/tests/data/dota/dotav2.0_images_val.tar.gz
new file mode 100644
index 00000000000..0e0f875749a
Binary files /dev/null and b/tests/data/dota/dotav2.0_images_val.tar.gz differ
diff --git a/tests/data/dota/samples.csv b/tests/data/dota/samples.csv
new file mode 100644
index 00000000000..5733c21796d
--- /dev/null
+++ b/tests/data/dota/samples.csv
@@ -0,0 +1,16 @@
+,image_path,annotation_path,split,version
+0,train/images/P0000.png,train/annotations/version1.0/P0000.txt,train,1.0
+1,train/images/P0001.png,train/annotations/version1.0/P0001.txt,train,1.0
+2,train/images/P0002.png,train/annotations/version1.0/P0002.txt,train,1.0
+3,train/images/P0000.png,train/annotations/version1.5/P0000.txt,train,1.5
+4,train/images/P0001.png,train/annotations/version1.5/P0001.txt,train,1.5
+5,train/images/P0002.png,train/annotations/version1.5/P0002.txt,train,1.5
+6,train/images/P0000.png,train/annotations/version2.0/P0000.txt,train,2.0
+7,train/images/P0001.png,train/annotations/version2.0/P0001.txt,train,2.0
+8,train/images/P0002.png,train/annotations/version2.0/P0002.txt,train,2.0
+9,val/images/P0000.png,val/annotations/version1.0/P0000.txt,val,1.0
+10,val/images/P0001.png,val/annotations/version1.0/P0001.txt,val,1.0
+11,val/images/P0000.png,val/annotations/version1.5/P0000.txt,val,1.5
+12,val/images/P0001.png,val/annotations/version1.5/P0001.txt,val,1.5
+13,val/images/P0000.png,val/annotations/version2.0/P0000.txt,val,2.0
+14,val/images/P0001.png,val/annotations/version2.0/P0001.txt,val,2.0
diff --git a/tests/data/dota/samples.parquet b/tests/data/dota/samples.parquet
new file mode 100644
index 00000000000..b97ce0de9d6
Binary files /dev/null and b/tests/data/dota/samples.parquet differ
diff --git a/tests/data/dota/train/annotations/version1.0/P0000.txt b/tests/data/dota/train/annotations/version1.0/P0000.txt
new file mode 100644
index 00000000000..0e9123426ea
--- /dev/null
+++ b/tests/data/dota/train/annotations/version1.0/P0000.txt
@@ -0,0 +1,2 @@
+imagesource:dummy
+gsd:1.0
diff --git a/tests/data/dota/train/annotations/version1.0/P0001.txt b/tests/data/dota/train/annotations/version1.0/P0001.txt
new file mode 100644
index 00000000000..20b1dc50fc6
--- /dev/null
+++ b/tests/data/dota/train/annotations/version1.0/P0001.txt
@@ -0,0 +1,4 @@
+imagesource:dummy
+gsd:1.0
+10.0 10.0 20.0 12.0 18.0 20.0 8.0 18.0 plane 0
+30.0 30.0 42.0 32.0 40.0 40.0 28.0 38.0 ship 0
diff --git a/tests/data/dota/train/annotations/version1.0/P0002.txt b/tests/data/dota/train/annotations/version1.0/P0002.txt
new file mode 100644
index 00000000000..20b1dc50fc6
--- /dev/null
+++ b/tests/data/dota/train/annotations/version1.0/P0002.txt
@@ -0,0 +1,4 @@
+imagesource:dummy
+gsd:1.0
+10.0 10.0 20.0 12.0 18.0 20.0 8.0 18.0 plane 0
+30.0 30.0 42.0 32.0 40.0 40.0 28.0 38.0 ship 0
diff --git a/tests/data/dota/train/annotations/version1.5/P0000.txt b/tests/data/dota/train/annotations/version1.5/P0000.txt
new file mode 100644
index 00000000000..0e9123426ea
--- /dev/null
+++ b/tests/data/dota/train/annotations/version1.5/P0000.txt
@@ -0,0 +1,2 @@
+imagesource:dummy
+gsd:1.0
diff --git a/tests/data/dota/train/annotations/version1.5/P0001.txt b/tests/data/dota/train/annotations/version1.5/P0001.txt
new file mode 100644
index 00000000000..20b1dc50fc6
--- /dev/null
+++ b/tests/data/dota/train/annotations/version1.5/P0001.txt
@@ -0,0 +1,4 @@
+imagesource:dummy
+gsd:1.0
+10.0 10.0 20.0 12.0 18.0 20.0 8.0 18.0 plane 0
+30.0 30.0 42.0 32.0 40.0 40.0 28.0 38.0 ship 0
diff --git a/tests/data/dota/train/annotations/version1.5/P0002.txt b/tests/data/dota/train/annotations/version1.5/P0002.txt
new file mode 100644
index 00000000000..20b1dc50fc6
--- /dev/null
+++ b/tests/data/dota/train/annotations/version1.5/P0002.txt
@@ -0,0 +1,4 @@
+imagesource:dummy
+gsd:1.0
+10.0 10.0 20.0 12.0 18.0 20.0 8.0 18.0 plane 0
+30.0 30.0 42.0 32.0 40.0 40.0 28.0 38.0 ship 0
diff --git a/tests/data/dota/train/annotations/version2.0/P0000.txt b/tests/data/dota/train/annotations/version2.0/P0000.txt
new file mode 100644
index 00000000000..0e9123426ea
--- /dev/null
+++ b/tests/data/dota/train/annotations/version2.0/P0000.txt
@@ -0,0 +1,2 @@
+imagesource:dummy
+gsd:1.0
diff --git a/tests/data/dota/train/annotations/version2.0/P0001.txt b/tests/data/dota/train/annotations/version2.0/P0001.txt
new file mode 100644
index 00000000000..20b1dc50fc6
--- /dev/null
+++ b/tests/data/dota/train/annotations/version2.0/P0001.txt
@@ -0,0 +1,4 @@
+imagesource:dummy
+gsd:1.0
+10.0 10.0 20.0 12.0 18.0 20.0 8.0 18.0 plane 0
+30.0 30.0 42.0 32.0 40.0 40.0 28.0 38.0 ship 0
diff --git a/tests/data/dota/train/annotations/version2.0/P0002.txt b/tests/data/dota/train/annotations/version2.0/P0002.txt
new file mode 100644
index 00000000000..20b1dc50fc6
--- /dev/null
+++ b/tests/data/dota/train/annotations/version2.0/P0002.txt
@@ -0,0 +1,4 @@
+imagesource:dummy
+gsd:1.0
+10.0 10.0 20.0 12.0 18.0 20.0 8.0 18.0 plane 0
+30.0 30.0 42.0 32.0 40.0 40.0 28.0 38.0 ship 0
diff --git a/tests/data/dota/train/images/P0000.png b/tests/data/dota/train/images/P0000.png
new file mode 100644
index 00000000000..a613e42d70a
Binary files /dev/null and b/tests/data/dota/train/images/P0000.png differ
diff --git a/tests/data/dota/train/images/P0001.png b/tests/data/dota/train/images/P0001.png
new file mode 100644
index 00000000000..db50a37c4c1
Binary files /dev/null and b/tests/data/dota/train/images/P0001.png differ
diff --git a/tests/data/dota/train/images/P0002.png b/tests/data/dota/train/images/P0002.png
new file mode 100644
index 00000000000..b51d3a2dc1b
Binary files /dev/null and b/tests/data/dota/train/images/P0002.png differ
diff --git a/tests/data/dota/val/annotations/version1.0/P0000.txt b/tests/data/dota/val/annotations/version1.0/P0000.txt
new file mode 100644
index 00000000000..0e9123426ea
--- /dev/null
+++ b/tests/data/dota/val/annotations/version1.0/P0000.txt
@@ -0,0 +1,2 @@
+imagesource:dummy
+gsd:1.0
diff --git a/tests/data/dota/val/annotations/version1.0/P0001.txt b/tests/data/dota/val/annotations/version1.0/P0001.txt
new file mode 100644
index 00000000000..20b1dc50fc6
--- /dev/null
+++ b/tests/data/dota/val/annotations/version1.0/P0001.txt
@@ -0,0 +1,4 @@
+imagesource:dummy
+gsd:1.0
+10.0 10.0 20.0 12.0 18.0 20.0 8.0 18.0 plane 0
+30.0 30.0 42.0 32.0 40.0 40.0 28.0 38.0 ship 0
diff --git a/tests/data/dota/val/annotations/version1.5/P0000.txt b/tests/data/dota/val/annotations/version1.5/P0000.txt
new file mode 100644
index 00000000000..0e9123426ea
--- /dev/null
+++ b/tests/data/dota/val/annotations/version1.5/P0000.txt
@@ -0,0 +1,2 @@
+imagesource:dummy
+gsd:1.0
diff --git a/tests/data/dota/val/annotations/version1.5/P0001.txt b/tests/data/dota/val/annotations/version1.5/P0001.txt
new file mode 100644
index 00000000000..20b1dc50fc6
--- /dev/null
+++ b/tests/data/dota/val/annotations/version1.5/P0001.txt
@@ -0,0 +1,4 @@
+imagesource:dummy
+gsd:1.0
+10.0 10.0 20.0 12.0 18.0 20.0 8.0 18.0 plane 0
+30.0 30.0 42.0 32.0 40.0 40.0 28.0 38.0 ship 0
diff --git a/tests/data/dota/val/annotations/version2.0/P0000.txt b/tests/data/dota/val/annotations/version2.0/P0000.txt
new file mode 100644
index 00000000000..0e9123426ea
--- /dev/null
+++ b/tests/data/dota/val/annotations/version2.0/P0000.txt
@@ -0,0 +1,2 @@
+imagesource:dummy
+gsd:1.0
diff --git a/tests/data/dota/val/annotations/version2.0/P0001.txt b/tests/data/dota/val/annotations/version2.0/P0001.txt
new file mode 100644
index 00000000000..20b1dc50fc6
--- /dev/null
+++ b/tests/data/dota/val/annotations/version2.0/P0001.txt
@@ -0,0 +1,4 @@
+imagesource:dummy
+gsd:1.0
+10.0 10.0 20.0 12.0 18.0 20.0 8.0 18.0 plane 0
+30.0 30.0 42.0 32.0 40.0 40.0 28.0 38.0 ship 0
diff --git a/tests/data/dota/val/images/P0000.png b/tests/data/dota/val/images/P0000.png
new file mode 100644
index 00000000000..72023b4a34c
Binary files /dev/null and b/tests/data/dota/val/images/P0000.png differ
diff --git a/tests/data/dota/val/images/P0001.png b/tests/data/dota/val/images/P0001.png
new file mode 100644
index 00000000000..2258575293f
Binary files /dev/null and b/tests/data/dota/val/images/P0001.png differ
diff --git a/tests/datasets/test_dota.py b/tests/datasets/test_dota.py
new file mode 100644
index 00000000000..23a34188d34
--- /dev/null
+++ b/tests/datasets/test_dota.py
@@ -0,0 +1,173 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+import shutil
+from itertools import product
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pytest
+import torch
+import torch.nn as nn
+from _pytest.fixtures import SubRequest
+from pytest import MonkeyPatch
+
+from torchgeo.datasets import DOTA, DatasetNotFoundError
+
+
+class TestDOTA:
+    @pytest.fixture(
+        params=product(
+            ['train', 'val'], ['1.0', '1.5', '2.0'], ['horizontal', 'oriented']
+        )
+    )
+    def dataset(
+        self, monkeypatch: MonkeyPatch, tmp_path: Path, request: SubRequest
+    ) -> DOTA:
+        url = os.path.join('tests', 'data', 'dota', '{}')
+        monkeypatch.setattr(DOTA, 'url', url)
+
+        file_info = {
+            'train': {
+                'images': {
+                    '1.0': {
+                        'filename': 'dotav1.0_images_train.tar.gz',
+                        'md5': '126d42cc8b2c093e7914528ac01ea8fc',
+                    },
+                    '1.5': {
+                        'filename': 'dotav1.0_images_train.tar.gz',
+                        'md5': 'fd187ea8acc3d429f0ba9e5ef96def75',
+                    },
+                    '2.0': {
+                        'filename': 'dotav2.0_images_train.tar.gz',
+                        'md5': '613d192b70dc53fe7e10f95eed0e1a9d',
+                    },
+                },
+                'annotations': {
+                    '1.0': {
+                        'filename': 'dotav1.0_annotations_train.tar.gz',
+                        'md5': '1fbdb35e2d55cab2632a8c20ed54a6de',
+                    },
+                    '1.5': {
+                        'filename': 'dotav1.5_annotations_train.tar.gz',
+                        'md5': '7a7ed5a309acb45dd1885f088fa24783',
+                    },
+                    '2.0': {
+                        'filename': 'dotav2.0_annotations_train.tar.gz',
+                        'md5': 'f8cd1bf53362bd372ddc2fba97cff2b6',
+                    },
+                },
+            },
+            'val': {
+                'images': {
+                    '1.0': {
+                        'filename': 'dotav1.0_images_val.tar.gz',
+                        'md5': 'f73dbdc8aa4e580dda4ef6cb54cfbd68',
+                    },
+                    '1.5': {
+                        'filename': 'dotav1.0_images_val.tar.gz',
+                        'md5': 'b1c618180e0ca3e4426ecf53b82c8d74',
+                    },
+                    '2.0': {
+                        'filename': 'dotav2.0_images_val.tar.gz',
+                        'md5': '0950df7a4c700934572f3a9a85133520',
+                    },
+                },
+                'annotations': {
+                    '1.0': {
+                        'filename': 'dotav1.0_annotations_val.tar.gz',
+                        'md5': '700fd2e7cba8dd543ca5bcbe411c9db4',
+                    },
+                    '1.5': {
+                        'filename': 'dotav1.5_annotations_val.tar.gz',
+                        'md5': 'f0a32911fa3614a8de67f5fd8d04dd9e',
+                    },
+                    '2.0': {
+                        'filename': 'dotav2.0_annotations_val.tar.gz',
+                        'md5': '4823cdc2c35d5f74254ffab0d99ea876',
+                    },
+                },
+            },
+        }
+        monkeypatch.setattr(DOTA, 'file_info', file_info)
+
+        root = tmp_path
+        split, version, bbox_orientation = request.param
+
+        transforms = nn.Identity()
+
+        return DOTA(
+            root,
+            split,
+            version=version,
+            bbox_orientation=bbox_orientation,
+            transforms=transforms,
+            download=True,
+            checksum=True,
+        )
+
+    def test_getitem(self, dataset: DOTA) -> None:
+        for i in range(len(dataset)):
+            x = dataset[i]
+            assert isinstance(x, dict)
+            assert isinstance(x['image'], torch.Tensor)
+            assert isinstance(x['labels'], torch.Tensor)
+            if dataset.bbox_orientation == 'oriented':
+                bbox_key = 'bbox'
+            else:
+                bbox_key = 'bbox_xyxy'
+            assert isinstance(x[bbox_key], torch.Tensor)
+
+            if dataset.bbox_orientation == 'oriented':
+                assert x[bbox_key].shape[1] == 8
+            else:
+                assert x[bbox_key].shape[1] == 4
+
+            assert x['labels'].shape[0] == x[bbox_key].shape[0]
+
+    def test_len(self, dataset: DOTA) -> None:
+        if dataset.split == 'train':
+            assert len(dataset) == 3
+        else:
+            assert len(dataset) == 2
+
+    def test_already_downloaded(self, dataset: DOTA) -> None:
+        DOTA(root=dataset.root, download=True)
+
+    def test_not_yet_extracted(self, tmp_path: Path) -> None:
+        files = [
+            'dotav1.0_images_train.tar.gz',
+            'dotav1.0_annotations_train.tar.gz',
+            'dotav1.5_annotations_train.tar.gz',
+            'dotav1.5_annotations_val.tar.gz',
+            'dotav1.0_images_val.tar.gz',
+            'dotav1.0_annotations_val.tar.gz',
+            'dotav2.0_images_train.tar.gz',
+            'dotav2.0_annotations_train.tar.gz',
+            'dotav2.0_images_val.tar.gz',
+            'dotav2.0_annotations_val.tar.gz',
+            'samples.csv',
+        ]
+        for path in files:
+            shutil.copyfile(
+                os.path.join('tests', 'data', 'dota', path),
+                os.path.join(str(tmp_path), path),
+            )
+
+        DOTA(root=tmp_path)
+
+    def test_corrupted(self, tmp_path: Path) -> None:
+        with open(os.path.join(tmp_path, 'dotav1.0_images_train.tar.gz'), 'w') as f:
+            f.write('bad')
+        with pytest.raises(RuntimeError, match='Archive'):
+            DOTA(root=tmp_path, checksum=True)
+
+    def test_not_downloaded(self, tmp_path: Path) -> None:
+        with pytest.raises(DatasetNotFoundError, match='Dataset not found'):
+            DOTA(tmp_path)
+
+    def test_plot(self, dataset: DOTA) -> None:
+        x = dataset[1]
+        dataset.plot(x, suptitle='Test')
+        plt.close()
diff --git a/torchgeo/datasets/__init__.py b/torchgeo/datasets/__init__.py
index ebfd2e1d5c5..5a92ce04a36 100644
--- a/torchgeo/datasets/__init__.py
+++ b/torchgeo/datasets/__init__.py
@@ -39,6 +39,7 @@
 from .digital_typhoon import DigitalTyphoon
 from .dior import DIOR
 from .dl4gam import DL4GAMAlps
+from .dota import DOTA
 from .eddmaps import EDDMapS
 from .enmap import EnMAP
 from .enviroatlas import EnviroAtlas
@@ -162,6 +163,7 @@
     'COWC',
     'DFC2022',
     'DIOR',
+    'DOTA',
     'ETCI2021',
     'EUDEM',
     'FAIR1M',
diff --git a/torchgeo/datasets/dota.py b/torchgeo/datasets/dota.py
new file mode 100644
index 00000000000..0be663a88dd
--- /dev/null
+++ b/torchgeo/datasets/dota.py
@@ -0,0 +1,506 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""DOTA dataset."""
+
+import os
+from collections.abc import Callable
+from typing import Any, ClassVar, Literal
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+from matplotlib import patches
+from matplotlib.figure import Figure
+from PIL import Image
+from torch import Tensor
+
+from .errors import DatasetNotFoundError
+from .geo import NonGeoDataset
+from .utils import (
+    Path,
+    check_integrity,
+    download_url,
+    extract_archive,
+    percentile_normalization,
+)
+
+
+class DOTA(NonGeoDataset):
+    """DOTA dataset.
+
+    `DOTA <https://captain-whu.github.io/DOTA/index.html>`__ is a large-scale object
+    detection dataset for aerial imagery containing RGB and gray-scale imagery
+    from Google Earth, GF-2 and JL-1 satellites as well as additional aerial imagery
+    from CycloMedia. There are three versions of the dataset: v1.0, v1.5, and v2.0, where,
+    v1.0 and v1.5 have the same images but different annotations,
+    and v2.0 extends both the images and annotations with more samples
+
+    Dataset features:
+
+    * 1869 samples in v1.0 and v1.5 and 2423 samples in v2.0
+    * multi-class object detection (15 classes in v1.0 and v1.5 and 18 classes in v2.0)
+    * horizontal and oriented bounding boxes
+
+    Dataset format:
+
+    * images are three channel PNGs with various pixel sizes
+    * annotations are text files with one line per bounding box
+
+    Classes:
+
+    0. plane
+    1. ship
+    2. storage-tank
+    3. baseball-diamond
+    4. tennis-court
+    5. basketball-court
+    6. ground-track-field
+    7. harbor
+    8. bridge
+    9. large-vehicle
+    10. small-vehicle
+    11. helicopter
+    12. roundabout
+    13. soccer-ball-field
+    14. swimming-pool
+    15. container-crane (v1.5+)
+    16. airport (v2.0+)
+    17. helipad (v2.0+)
+
+    If you use this work in your research, please cite the following papers:
+
+    * https://arxiv.org/abs/2102.12219
+    * https://arxiv.org/abs/1711.10398
+
+    .. versionadded:: 0.7
+    """
+
+    url = 'https://huggingface.co/datasets/torchgeo/dota/resolve/672e63236622f7da6ee37fca44c50ac368b77cab/{}'
+
+    file_info: ClassVar[dict[str, dict[str, dict[str, dict[str, str]]]]] = {
+        'train': {
+            'images': {
+                '1.0': {
+                    'filename': 'dotav1.0_images_train.tar.gz',
+                    'md5': '363b472dc3c71e7fa2f4a60223b437ea',
+                },
+                '1.5': {
+                    'filename': 'dotav1.0_images_train.tar.gz',
+                    'md5': '363b472dc3c71e7fa2f4a60223b437ea',
+                },
+                '2.0': {
+                    'filename': 'dotav2.0_images_train.tar.gz',
+                    'md5': '91ae5212d170330ab9f65ccb6c675763',
+                },
+            },
+            'annotations': {
+                '1.0': {
+                    'filename': 'dotav1.0_annotations_train.tar.gz',
+                    'md5': 'f6788257bcc4d29018344a4128e3734a',
+                },
+                '1.5': {
+                    'filename': 'dotav1.5_annotations_train.tar.gz',
+                    'md5': '0da97e5623a87d7bec22e75f6978dbce',
+                },
+                '2.0': {
+                    'filename': 'dotav2.0_annotations_train.tar.gz',
+                    'md5': '04d3d626df2203053b7f06581b3b0667',
+                },
+            },
+        },
+        'val': {
+            'images': {
+                '1.0': {
+                    'filename': 'dotav1.0_images_val.tar.gz',
+                    'md5': '42293219ba61d61c417ae558bbe1f2ba',
+                },
+                '1.5': {
+                    'filename': 'dotav1.0_images_val.tar.gz',
+                    'md5': '42293219ba61d61c417ae558bbe1f2ba',
+                },
+                '2.0': {
+                    'filename': 'dotav2.0_images_val.tar.gz',
+                    'md5': '737f65edf54b5aa627b3d48b0e253095',
+                },
+            },
+            'annotations': {
+                '1.0': {
+                    'filename': 'dotav1.0_annotations_val.tar.gz',
+                    'md5': '28155c05b1dc3a0f5cb6b9bdfef85a13',
+                },
+                '1.5': {
+                    'filename': 'dotav1.5_annotations_val.tar.gz',
+                    'md5': '85bf945788784cf9b4f1c714453178fc',
+                },
+                '2.0': {
+                    'filename': 'dotav2.0_annotations_val.tar.gz',
+                    'md5': 'ec53c1dbcfc125d7532bd6a065c647ac',
+                },
+            },
+        },
+    }
+
+    sample_df_path = 'samples.csv'
+
+    classes = (
+        'plane',
+        'ship',
+        'storage-tank',
+        'baseball-diamond',
+        'tennis-court',
+        'basketball-court',
+        'ground-track-field',
+        'harbor',
+        'bridge',
+        'large-vehicle',
+        'small-vehicle',
+        'helicopter',
+        'roundabout',
+        'soccer-ball-field',
+        'swimming-pool',
+        'container-crane',
+        'airport',
+        'helipad',
+    )
+
+    valid_splits = ('train', 'val')
+    valid_versions = ('1.0', '1.5', '2.0')
+
+    valid_orientations = ('horizontal', 'oriented')
+
+    def __init__(
+        self,
+        root: Path = 'data',
+        split: Literal['train', 'val'] = 'train',
+        version: Literal['1.0', '1.5', '2.0'] = '2.0',
+        bbox_orientation: Literal['horizontal', 'oriented'] = 'oriented',
+        transforms: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
+        download: bool = False,
+        checksum: bool = False,
+    ) -> None:
+        """Initialize a new DOTA dataset instance.
+
+        Args:
+            root: root directory where dataset can be found
+            split: split of the dataset to use, one of ['train', 'val']
+            version: version of the dataset to use, one of ['1.0', '1.5', '2.0']
+            bbox_orientation: bounding box orientation, one of ['horizontal', 'oriented'], where horizontal
+                returnx xyxy format and oriented returns x1y1x2y2x3y3x4y4 format
+            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)
+
+        Raises:
+            AssertionError: if *split*, *version*, or *bbox_orientation* argument are not valid
+            DatasetNotFoundError: if dataset is not found or corrupted, and *download* is False
+        """
+        assert split in self.valid_splits, (
+            f"Split '{split}' not supported, use one of {self.valid_splits}"
+        )
+        assert version in self.valid_versions, (
+            f"Version '{version}' not supported, use one of {self.valid_versions}"
+        )
+
+        assert bbox_orientation in self.valid_orientations, (
+            f'Bounding box orientation must be one of {self.valid_orientations}'
+        )
+
+        self.root = root
+        self.split = split
+        self.version = version
+        self.transforms = transforms
+        self.download = download
+        self.checksum = checksum
+        self.bbox_orientation = bbox_orientation
+
+        self._verify()
+
+        self.sample_df = pd.read_csv(os.path.join(self.root, 'samples.csv'))
+        self.sample_df['version'] = self.sample_df['version'].astype(str)
+        self.sample_df = self.sample_df[self.sample_df['split'] == self.split]
+        self.sample_df = self.sample_df[
+            self.sample_df['version'] == self.version
+        ].reset_index(drop=True)
+
+    def __len__(self) -> int:
+        """Return the number of samples in the dataset.
+
+        Returns:
+            length of the dataset
+        """
+        return len(self.sample_df)
+
+    def __getitem__(self, index: int) -> dict[str, Any]:
+        """Return an index within the dataset.
+
+        Args:
+            index: index to return
+
+        Returns:
+            data and label at that index
+        """
+        sample_row = self.sample_df.iloc[index]
+
+        sample = {'image': self._load_image(sample_row['image_path'])}
+
+        boxes, labels = self._load_annotations(sample_row['annotation_path'])
+
+        if self.bbox_orientation == 'horizontal':
+            sample['bbox_xyxy'] = boxes
+        else:
+            sample['bbox'] = boxes
+        sample['labels'] = labels
+
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+
+        return sample
+
+    def _load_image(self, path: str) -> Tensor:
+        """Load image.
+
+        Args:
+            path: path to image file
+
+        Returns:
+            image: image tensor
+        """
+        image = Image.open(os.path.join(self.root, path)).convert('RGB')
+        return torch.from_numpy(np.array(image).transpose(2, 0, 1)).float()
+
+    def _load_annotations(self, path: str) -> tuple[Tensor, Tensor]:
+        """Load DOTA annotations from text file.
+
+        Format:
+            x1 y1 x2 y2 x3 y3 x4 y4 class difficult
+
+        Some files have 2 header lines that need to be skipped:
+            imagesource:GoogleEarth
+            gsd:0.146343590398
+
+        Args:
+            path: path to annotation file
+
+        Returns:
+            tuple of:
+                boxes: tensor of shape (N, 8) with coordinates for oriented
+                    and (N, 4) for horizontal
+                labels: tensor of shape (N,) with class indices
+        """
+        with open(os.path.join(self.root, path)) as f:
+            lines = f.readlines()
+
+        # Skip header if present
+        start_idx = 0
+        if lines and lines[0].startswith('imagesource'):
+            start_idx = 2
+        boxes = []
+        labels = []
+
+        for line in lines[start_idx:]:
+            parts = line.strip().split(' ')
+
+            # Always read 8 coordinates
+            coords = [float(p) for p in parts[:8]]
+            label = parts[8]
+
+            labels.append(self.classes.index(label))
+
+            if self.bbox_orientation == 'horizontal':
+                # Convert to [xmin, ymin, xmax, ymax] format
+                x_coords = coords[::2]  # even indices (0,2,4,6)
+                y_coords = coords[1::2]  # odd indices (1,3,5,7)
+                xmin, xmax = min(x_coords), max(x_coords)
+                ymin, ymax = min(y_coords), max(y_coords)
+                boxes.append([xmin, ymin, xmax, ymax])
+            else:
+                boxes.append(coords)
+
+        if not boxes:
+            return (
+                torch.zeros((0, 4 if self.bbox_orientation == 'horizontal' else 8)),
+                torch.zeros(0, dtype=torch.long),
+            )
+        else:
+            return torch.tensor(boxes), torch.tensor(labels)
+
+    def _verify(self) -> None:
+        """Verify dataset integrity and download/extract if needed."""
+        # check if directories and sample file are present
+        required_dirs = [
+            os.path.join(self.root, self.split, 'images'),
+            os.path.join(
+                self.root, self.split, 'annotations', f'version{self.version}'
+            ),
+            os.path.join(self.root, self.sample_df_path),
+        ]
+        if all(os.path.exists(d) for d in required_dirs):
+            return
+
+        # Check for compressed files, v1.0 and v1.5 have the same images but different annotations
+        files_needed = [
+            (
+                self.file_info[self.split]['images'][self.version]['filename'],
+                self.file_info[self.split]['images'][self.version]['md5'],
+            ),
+            (
+                self.file_info[self.split]['annotations'][self.version]['filename'],
+                self.file_info[self.split]['annotations'][self.version]['md5'],
+            ),
+        ]
+        # For v2.0, also need v1.0 image files, but only v2 annotations
+        if self.version == '2.0':
+            files_needed.append(
+                (
+                    self.file_info[self.split]['images']['1.0']['filename'],
+                    self.file_info[self.split]['images']['1.0']['md5'],
+                )
+            )
+
+        # Check if archives exist and verify checksums if requested
+        exists = []
+        for filename, md5 in files_needed:
+            filepath = os.path.join(self.root, filename)
+            if os.path.exists(filepath):
+                if self.checksum:
+                    if not check_integrity(filepath, md5):
+                        raise RuntimeError(f'Archive {filename} corrupted')
+                exists.append(True)
+                self._extract([(filename, md5)])
+            else:
+                exists.append(False)
+
+        if all(exists):
+            return
+
+        if not self.download:
+            raise DatasetNotFoundError(self)
+
+        # also download the metadata file
+        self._download(files_needed)
+        self._extract(files_needed)
+
+    def _download(self, files_needed: list[tuple[str, str]]) -> None:
+        """Download the dataset.
+
+        Args:
+            files_needed: list of files to download for the particular version
+        """
+        for filename, md5 in files_needed:
+            if not os.path.exists(os.path.join(self.root, filename)):
+                download_url(
+                    url=self.url.format(filename),
+                    root=self.root,
+                    filename=filename,
+                    md5=None if not self.checksum else md5,
+                )
+
+        if not os.path.exists(os.path.join(self.root, self.sample_df_path)):
+            download_url(
+                url=self.url.format(self.sample_df_path),
+                root=self.root,
+                filename=self.sample_df_path,
+            )
+
+    def _extract(self, files_needed: list[tuple[str, str]]) -> None:
+        """Extract the dataset.
+
+        Args:
+            files_needed: list of files to extract for the particular version
+        """
+        for filename, _ in files_needed:
+            filepath = os.path.join(self.root, filename)
+            extract_archive(filepath, self.root)
+
+    def plot(
+        self,
+        sample: dict[str, Tensor],
+        show_titles: bool = True,
+        suptitle: str | None = None,
+        box_alpha: float = 0.7,
+    ) -> Figure:
+        """Plot a sample from the dataset.
+
+        Args:
+            sample: a sample returned by __getitem__
+            show_titles: flag indicating whether to show titles
+            suptitle: optional string to use as a suptitle
+            box_alpha: alpha value for boxes
+
+        Returns:
+            a matplotlib Figure with the rendered sample
+        """
+        image = percentile_normalization(sample['image'].permute(1, 2, 0).numpy())
+        if self.bbox_orientation == 'horizontal':
+            boxes = sample['bbox_xyxy'].cpu().numpy()
+        else:
+            boxes = sample['bbox'].cpu().numpy()
+        labels = sample['labels'].cpu().numpy()
+
+        fig, ax = plt.subplots(figsize=(10, 10))
+        ax.imshow(image)
+        ax.axis('off')
+
+        # Create color map for classes
+        cm = plt.get_cmap('gist_rainbow')
+
+        for box, label_idx in zip(boxes, labels):
+            color = cm(label_idx / len(self.classes))
+            label = self.classes[label_idx]
+
+            if self.bbox_orientation == 'horizontal':
+                # Horizontal box: [xmin, ymin, xmax, ymax]
+                x1, y1, x2, y2 = box
+                rect = patches.Rectangle(
+                    (x1, y1),
+                    x2 - x1,
+                    y2 - y1,
+                    linewidth=2,
+                    alpha=box_alpha,
+                    linestyle='solid',
+                    edgecolor=color,
+                    facecolor='none',
+                )
+                ax.add_patch(rect)
+                # Add label above box
+                ax.text(
+                    x1,
+                    y1 - 5,
+                    label,
+                    color='white',
+                    fontsize=8,
+                    bbox=dict(facecolor=color, alpha=box_alpha),
+                )
+            else:
+                # Oriented box: [x1,y1,x2,y2,x3,y3,x4,y4]
+                vertices = box.reshape(4, 2)
+                polygon = patches.Polygon(
+                    vertices,
+                    linewidth=2,
+                    alpha=box_alpha,
+                    linestyle='solid',
+                    edgecolor=color,
+                    facecolor='none',
+                )
+                ax.add_patch(polygon)
+                # Add label at centroid
+                centroid_x = vertices[:, 0].mean()
+                centroid_y = vertices[:, 1].mean()
+                ax.text(
+                    centroid_x,
+                    centroid_y,
+                    label,
+                    color='white',
+                    fontsize=8,
+                    bbox=dict(facecolor=color, alpha=box_alpha),
+                    ha='center',
+                    va='center',
+                )
+
+        if suptitle is not None:
+            plt.suptitle(suptitle)
+
+        return fig