diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst
index 8147863f095..2b9abf0e11b 100644
--- a/docs/api/datasets.rst
+++ b/docs/api/datasets.rst
@@ -331,6 +331,11 @@ HySpecNet-11k
 
 .. autoclass:: HySpecNet11k
 
+iSAID
+^^^^^
+
+.. autoclass:: ISAID
+
 IDTReeS
 ^^^^^^^
 
diff --git a/docs/api/datasets/non_geo_datasets.csv b/docs/api/datasets/non_geo_datasets.csv
index 1defcb032bd..4d53ced59ff 100644
--- a/docs/api/datasets/non_geo_datasets.csv
+++ b/docs/api/datasets/non_geo_datasets.csv
@@ -25,6 +25,7 @@ Dataset,Task,Source,License,# Samples,# Classes,Size (px),Resolution (m),Bands
 `HySpecNet-11k`_,-,EnMAP,CC0-1.0,11k,-,128,30,HSI
 `IDTReeS`_,"OD,C",Aerial,"CC-BY-4.0",591,33,200x200,0.1--1,RGB
 `Inria Aerial Image Labeling`_,S,Aerial,-,360,2,"5,000x5,000",0.3,RGB
+`iSAID`_,"OD,I",Aerial,"CC-BY-NC-4.0","2,806",15,"varies","varies",RGB
 `LandCover.ai`_,S,Aerial,"CC-BY-NC-SA-4.0","10,674",5,512x512,0.25--0.5,RGB
 `LEVIR-CD`_,CD,Google Earth,-,637,2,"1,024x1,024",0.5,RGB
 `LEVIR-CD+`_,CD,Google Earth,-,985,2,"1,024x1,024",0.5,RGB
diff --git a/tests/data/isaid/data.py b/tests/data/isaid/data.py
new file mode 100644
index 00000000000..6563dfffd5c
--- /dev/null
+++ b/tests/data/isaid/data.py
@@ -0,0 +1,135 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import hashlib
+import json
+import os
+import shutil
+import tarfile
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+
+def create_dummy_image(path: Path, size: tuple[int, int] = (64, 64)) -> None:
+    """Create dummy RGB image."""
+    img = np.random.randint(0, 255, (*size, 3), dtype=np.uint8)
+    Image.fromarray(img).save(path)
+
+
+def create_coco_annotations(split: str, num_images: int) -> dict:
+    """Create COCO format annotations."""
+    return {
+        'info': {'year': 2023, 'version': '1.0'},
+        'images': [
+            {'id': i, 'file_name': f'P{i:04d}.png', 'height': 64, 'width': 64}
+            for i in range(num_images)
+        ],
+        'annotations': [
+            {
+                'id': i,
+                'image_id': i // 2,  # 2 annotations per image
+                'category_id': i % 15,
+                'segmentation': [[10, 10, 20, 10, 20, 20, 10, 20]],
+                'area': 100,
+                'bbox': [10, 10, 10, 10],
+                'iscrowd': 0,
+            }
+            for i in range(num_images * 2)
+        ],
+        'categories': [
+            {'id': i, 'name': name}
+            for i, name in enumerate(
+                [
+                    'plane',
+                    'ship',
+                    'storage tank',
+                    'baseball diamond',
+                    'tennis court',
+                    'basketball court',
+                    'ground track field',
+                    'harbor',
+                    'bridge',
+                    'vehicle',
+                    'helicopter',
+                    'roundabout',
+                    'swimming pool',
+                    'soccer ball field',
+                    'container crane',
+                ]
+            )
+        ],
+    }
+
+
+def create_test_data(root: Path) -> None:
+    """Create iSAID test dataset."""
+    splits = {'train': 3, 'val': 2}
+
+    for split, num_samples in splits.items():
+        if os.path.exists(root / split):
+            shutil.rmtree(root / split)
+
+        # Create directories
+        for subdir in ['images', 'Annotations', 'Instance_masks', 'Semantic_masks']:
+            (root / split / subdir).mkdir(parents=True, exist_ok=True)
+
+        # Create images and masks
+        for i in range(num_samples):
+            # RGB image
+            create_dummy_image(root / split / 'images' / f'P{i:04d}.png')
+
+            # Instance mask (R+G*256+B*256^2 encoding)
+            instance_mask = np.zeros((64, 64, 3), dtype=np.uint8)
+            instance_mask[10:20, 10:20, 0] = i + 1  # R channel for unique IDs
+            Image.fromarray(instance_mask).save(
+                root / split / 'Instance_masks' / f'P{i:04d}.png'
+            )
+
+            # Semantic mask (similar encoding for class IDs)
+            semantic_mask = np.zeros((64, 64, 3), dtype=np.uint8)
+            semantic_mask[10:20, 10:20, 0] = 1  # Class ID 1
+            Image.fromarray(semantic_mask).save(
+                root / split / 'Semantic_masks' / f'P{i:04d}.png'
+            )
+
+        # Create COCO annotations
+        annotations = create_coco_annotations(split, num_samples)
+        with open(root / split / 'Annotations' / f'iSAID_{split}.json', 'w') as f:
+            json.dump(annotations, f)
+
+        # Create image tar
+        img_tar = f'dotav1_images_{split}.tar.gz'
+        with tarfile.open(root / img_tar, 'w:gz') as tar:
+            tar.add(root / split / 'images', arcname=os.path.join(split, 'images'))
+
+        # Create annotations tar with all splits
+        ann_tar = f'isaid_annotations_{split}.tar.gz'
+        with tarfile.open(root / ann_tar, 'w:gz') as tar:
+            for split in splits:
+                for subdir in ['Annotations', 'Instance_masks', 'Semantic_masks']:
+                    tar.add(root / split / subdir, arcname=os.path.join(split, subdir))
+
+    # 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 MD5 checksums
+    for split in splits:
+        print(
+            f'MD5 for dotav1_images_{split}.tar.gz: '
+            f'{md5(root / f"dotav1_images_{split}.tar.gz")}'
+        )
+        print(
+            f'MD5 for isaid_annotations_{split}.tar.gz: {md5(root / f"isaid_annotations_{split}.tar.gz")}'
+        )
+
+
+if __name__ == '__main__':
+    root = Path('.')
+    create_test_data(root)
diff --git a/tests/data/isaid/dotav1_images_train.tar.gz b/tests/data/isaid/dotav1_images_train.tar.gz
new file mode 100644
index 00000000000..0991de0e84b
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diff --git a/tests/data/isaid/dotav1_images_val.tar.gz b/tests/data/isaid/dotav1_images_val.tar.gz
new file mode 100644
index 00000000000..4ac9f97bbaf
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diff --git a/tests/data/isaid/isaid_annotations_train.tar.gz b/tests/data/isaid/isaid_annotations_train.tar.gz
new file mode 100644
index 00000000000..93b2ae902e6
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diff --git a/tests/data/isaid/isaid_annotations_val.tar.gz b/tests/data/isaid/isaid_annotations_val.tar.gz
new file mode 100644
index 00000000000..422c6042d90
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diff --git a/tests/data/isaid/train/Annotations/iSAID_train.json b/tests/data/isaid/train/Annotations/iSAID_train.json
new file mode 100644
index 00000000000..bc9c49d7763
--- /dev/null
+++ b/tests/data/isaid/train/Annotations/iSAID_train.json
@@ -0,0 +1 @@
+{"info": {"year": 2023, "version": "1.0"}, "images": [{"id": 0, "file_name": "P0000.png", "height": 64, "width": 64}, {"id": 1, "file_name": "P0001.png", "height": 64, "width": 64}, {"id": 2, "file_name": "P0002.png", "height": 64, "width": 64}], "annotations": [{"id": 0, "image_id": 0, "category_id": 0, "segmentation": [[10, 10, 20, 10, 20, 20, 10, 20]], "area": 100, "bbox": [10, 10, 10, 10], "iscrowd": 0}, {"id": 1, "image_id": 0, "category_id": 1, "segmentation": [[10, 10, 20, 10, 20, 20, 10, 20]], "area": 100, "bbox": [10, 10, 10, 10], "iscrowd": 0}, {"id": 2, "image_id": 1, "category_id": 2, "segmentation": [[10, 10, 20, 10, 20, 20, 10, 20]], "area": 100, "bbox": [10, 10, 10, 10], "iscrowd": 0}, {"id": 3, "image_id": 1, "category_id": 3, "segmentation": [[10, 10, 20, 10, 20, 20, 10, 20]], "area": 100, "bbox": [10, 10, 10, 10], "iscrowd": 0}, {"id": 4, "image_id": 2, "category_id": 4, "segmentation": [[10, 10, 20, 10, 20, 20, 10, 20]], "area": 100, "bbox": [10, 10, 10, 10], "iscrowd": 0}, {"id": 5, "image_id": 2, "category_id": 5, "segmentation": [[10, 10, 20, 10, 20, 20, 10, 20]], "area": 100, "bbox": [10, 10, 10, 10], "iscrowd": 0}], "categories": [{"id": 0, "name": "plane"}, {"id": 1, "name": "ship"}, {"id": 2, "name": "storage tank"}, {"id": 3, "name": "baseball diamond"}, {"id": 4, "name": "tennis court"}, {"id": 5, "name": "basketball court"}, {"id": 6, "name": "ground track field"}, {"id": 7, "name": "harbor"}, {"id": 8, "name": "bridge"}, {"id": 9, "name": "vehicle"}, {"id": 10, "name": "helicopter"}, {"id": 11, "name": "roundabout"}, {"id": 12, "name": "swimming pool"}, {"id": 13, "name": "soccer ball field"}, {"id": 14, "name": "container crane"}]}
\ No newline at end of file
diff --git a/tests/data/isaid/train/Instance_masks/P0000.png b/tests/data/isaid/train/Instance_masks/P0000.png
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index 00000000000..89de3058e86
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diff --git a/tests/data/isaid/train/Instance_masks/P0002.png b/tests/data/isaid/train/Instance_masks/P0002.png
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diff --git a/tests/data/isaid/train/Semantic_masks/P0000.png b/tests/data/isaid/train/Semantic_masks/P0000.png
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diff --git a/tests/data/isaid/train/images/P0000.png b/tests/data/isaid/train/images/P0000.png
new file mode 100644
index 00000000000..f12b278a43a
Binary files /dev/null and b/tests/data/isaid/train/images/P0000.png differ
diff --git a/tests/data/isaid/train/images/P0001.png b/tests/data/isaid/train/images/P0001.png
new file mode 100644
index 00000000000..f3a910b9613
Binary files /dev/null and b/tests/data/isaid/train/images/P0001.png differ
diff --git a/tests/data/isaid/train/images/P0002.png b/tests/data/isaid/train/images/P0002.png
new file mode 100644
index 00000000000..8a67e1fc367
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diff --git a/tests/data/isaid/val/Annotations/iSAID_val.json b/tests/data/isaid/val/Annotations/iSAID_val.json
new file mode 100644
index 00000000000..9f77d848851
--- /dev/null
+++ b/tests/data/isaid/val/Annotations/iSAID_val.json
@@ -0,0 +1 @@
+{"info": {"year": 2023, "version": "1.0"}, "images": [{"id": 0, "file_name": "P0000.png", "height": 64, "width": 64}, {"id": 1, "file_name": "P0001.png", "height": 64, "width": 64}], "annotations": [{"id": 0, "image_id": 0, "category_id": 0, "segmentation": [[10, 10, 20, 10, 20, 20, 10, 20]], "area": 100, "bbox": [10, 10, 10, 10], "iscrowd": 0}, {"id": 1, "image_id": 0, "category_id": 1, "segmentation": [[10, 10, 20, 10, 20, 20, 10, 20]], "area": 100, "bbox": [10, 10, 10, 10], "iscrowd": 0}, {"id": 2, "image_id": 1, "category_id": 2, "segmentation": [[10, 10, 20, 10, 20, 20, 10, 20]], "area": 100, "bbox": [10, 10, 10, 10], "iscrowd": 0}, {"id": 3, "image_id": 1, "category_id": 3, "segmentation": [[10, 10, 20, 10, 20, 20, 10, 20]], "area": 100, "bbox": [10, 10, 10, 10], "iscrowd": 0}], "categories": [{"id": 0, "name": "plane"}, {"id": 1, "name": "ship"}, {"id": 2, "name": "storage tank"}, {"id": 3, "name": "baseball diamond"}, {"id": 4, "name": "tennis court"}, {"id": 5, "name": "basketball court"}, {"id": 6, "name": "ground track field"}, {"id": 7, "name": "harbor"}, {"id": 8, "name": "bridge"}, {"id": 9, "name": "vehicle"}, {"id": 10, "name": "helicopter"}, {"id": 11, "name": "roundabout"}, {"id": 12, "name": "swimming pool"}, {"id": 13, "name": "soccer ball field"}, {"id": 14, "name": "container crane"}]}
\ No newline at end of file
diff --git a/tests/data/isaid/val/Instance_masks/P0000.png b/tests/data/isaid/val/Instance_masks/P0000.png
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diff --git a/tests/data/isaid/val/Semantic_masks/P0000.png b/tests/data/isaid/val/Semantic_masks/P0000.png
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new file mode 100644
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diff --git a/tests/data/isaid/val/images/P0000.png b/tests/data/isaid/val/images/P0000.png
new file mode 100644
index 00000000000..83476529ac5
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diff --git a/tests/data/isaid/val/images/P0001.png b/tests/data/isaid/val/images/P0001.png
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index 00000000000..483b958c70b
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diff --git a/tests/datasets/test_isaid.py b/tests/datasets/test_isaid.py
new file mode 100644
index 00000000000..448cb3b0d38
--- /dev/null
+++ b/tests/datasets/test_isaid.py
@@ -0,0 +1,104 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+import shutil
+from pathlib import Path
+
+import pytest
+import torch
+import torch.nn as nn
+from _pytest.fixtures import SubRequest
+from pytest import MonkeyPatch
+
+from torchgeo.datasets import ISAID, DatasetNotFoundError
+
+pytest.importorskip('pycocotools')
+
+
+class TestISAID:
+    @pytest.fixture(params=['train', 'val'])
+    def dataset(
+        self, monkeypatch: MonkeyPatch, tmp_path: Path, request: SubRequest
+    ) -> ISAID:
+        url = os.path.join('tests', 'data', 'isaid', '{}')
+        monkeypatch.setattr(ISAID, 'img_url', url)
+        monkeypatch.setattr(ISAID, 'label_url', url)
+
+        img_files = {
+            'train': {
+                'filename': 'dotav1_images_train.tar.gz',
+                'md5': 'a38ad9832066e2ca6d30b8eec65f9ce8',
+            },
+            'val': {
+                'filename': 'dotav1_images_val.tar.gz',
+                'md5': '154babe8091484bd85c6340f43cea1ea',
+            },
+        }
+
+        monkeypatch.setattr(ISAID, 'img_files', img_files)
+
+        label_files = {
+            'train': {
+                'filename': 'isaid_annotations_train.tar.gz',
+                'md5': 'f4de0f6b38f1b11b121dc01c880aeb2a',
+            },
+            'val': {
+                'filename': 'isaid_annotations_val.tar.gz',
+                'md5': '88eccdf9744c201248266b9a784ffeab',
+            },
+        }
+        monkeypatch.setattr(ISAID, 'label_files', label_files)
+
+        root = tmp_path
+        split = request.param
+
+        transforms = nn.Identity()
+
+        return ISAID(root, split, transforms=transforms, download=True, checksum=True)
+
+    def test_getitem(self, dataset: ISAID) -> None:
+        for i in range(len(dataset)):
+            x = dataset[i]
+            assert isinstance(x, dict)
+            assert isinstance(x['image'], torch.Tensor)
+            assert isinstance(x['masks'], torch.Tensor)
+            assert isinstance(x['boxes'], torch.Tensor)
+
+    def test_len(self, dataset: ISAID) -> None:
+        if dataset.split == 'train':
+            assert len(dataset) == 3
+        else:
+            assert len(dataset) == 2
+
+    def test_already_downloaded(self, dataset: ISAID) -> None:
+        ISAID(root=dataset.root, download=True)
+
+    def test_not_yet_extracted(self, tmp_path: Path) -> None:
+        files = [
+            'dotav1_images_train.tar.gz',
+            'dotav1_images_val.tar.gz',
+            'isaid_annotations_train.tar.gz',
+            'isaid_annotations_val.tar.gz',
+        ]
+        for path in files:
+            shutil.copyfile(
+                os.path.join('tests', 'data', 'isaid', path),
+                os.path.join(str(tmp_path), path),
+            )
+
+        ISAID(root=tmp_path)
+
+    def test_invalid_split(self) -> None:
+        with pytest.raises(AssertionError):
+            ISAID(split='foo')
+
+    def test_corrupted(self, tmp_path: Path) -> None:
+        with open(os.path.join(tmp_path, 'dotav1_images_train.tar.gz'), 'w') as f:
+            f.write('bad')
+        with pytest.raises(RuntimeError, match='Archive'):
+            ISAID(root=tmp_path, checksum=True)
+
+    def test_not_downloaded(self, tmp_path: Path) -> None:
+        with pytest.raises(DatasetNotFoundError, match='Dataset not found'):
+            ISAID(tmp_path)
diff --git a/torchgeo/datasets/__init__.py b/torchgeo/datasets/__init__.py
index 1d644c6fc69..84e488df384 100644
--- a/torchgeo/datasets/__init__.py
+++ b/torchgeo/datasets/__init__.py
@@ -68,6 +68,7 @@
 from .inaturalist import INaturalist
 from .inria import InriaAerialImageLabeling
 from .iobench import IOBench
+from .isaid import ISAID
 from .l7irish import L7Irish
 from .l8biome import L8Biome
 from .landcoverai import LandCoverAI, LandCoverAI100, LandCoverAIBase, LandCoverAIGeo
@@ -163,6 +164,7 @@
     'FAIR1M',
     'GBIF',
     'GID15',
+    'ISAID',
     'LEVIRCD',
     'MDAS',
     'NAIP',
diff --git a/torchgeo/datasets/isaid.py b/torchgeo/datasets/isaid.py
new file mode 100644
index 00000000000..6183700e143
--- /dev/null
+++ b/torchgeo/datasets/isaid.py
@@ -0,0 +1,340 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""iSAID dataset."""
+
+import os
+from collections.abc import Callable
+from typing import Any, ClassVar
+
+import numpy as np
+import torch
+from PIL import Image
+from torch import Tensor
+
+from .errors import DatasetNotFoundError
+from .geo import NonGeoDataset
+from .utils import (
+    Path,
+    check_integrity,
+    download_and_extract_archive,
+    extract_archive,
+    lazy_import,
+)
+
+
+def convert_coco_poly_to_mask(
+    segmentations: list[int], height: int, width: int
+) -> Tensor:
+    """Convert coco polygons to mask tensor.
+
+    Args:
+        segmentations (List[int]): polygon coordinates
+        height (int): image height
+        width (int): image width
+
+    Returns:
+        Tensor: Mask tensor
+
+    Raises:
+        DependencyNotFoundError: If pycocotools is not installed.
+    """
+    pycocotools = lazy_import('pycocotools')
+    masks = []
+    for polygons in segmentations:
+        rles = pycocotools.mask.frPyObjects(polygons, height, width)
+        mask = pycocotools.mask.decode(rles)
+        mask = torch.as_tensor(mask, dtype=torch.uint8)
+        mask = mask.any(dim=2)
+        masks.append(mask)
+    masks_tensor = torch.stack(masks, dim=0)
+    return masks_tensor
+
+
+class ConvertCocoAnnotations:
+    """Callable for converting the boxes, masks and labels into tensors.
+
+    This is a modified version of ConvertCocoPolysToMask() from torchvision found in
+    https://github.com/pytorch/vision/blob/v0.14.0/references/detection/coco_utils.py
+    """
+
+    def __call__(self, sample: dict[str, Any]) -> dict[str, Any]:
+        """Converts MS COCO fields (boxes, masks & labels) from list of ints to tensors.
+
+        Args:
+            sample: Sample
+
+        Returns:
+            Processed sample
+        """
+        image = sample['image']
+        _, h, w = image.size()
+        target = sample['label']
+
+        image_id = target['image_id']
+        image_id = torch.tensor([image_id])
+
+        anno = target['annotations']
+
+        anno = [obj for obj in anno if obj['iscrowd'] == 0]
+
+        bboxes = [obj['bbox'] for obj in anno]
+        # guard against no boxes via resizing
+        boxes = torch.as_tensor(bboxes, dtype=torch.float32).reshape(-1, 4)
+        boxes[:, 2:] += boxes[:, :2]
+        boxes[:, 0::2].clamp_(min=0, max=w)
+        boxes[:, 1::2].clamp_(min=0, max=h)
+
+        categories = [obj['category_id'] for obj in anno]
+        classes = torch.tensor(categories, dtype=torch.int64)
+
+        segmentations = [obj['segmentation'] for obj in anno]
+
+        masks = convert_coco_poly_to_mask(segmentations, h, w)
+
+        keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
+        boxes = boxes[keep]
+        classes = classes[keep]
+
+        target = {'boxes': boxes, 'labels': classes, 'image_id': image_id}
+        if masks.nelement() > 0:
+            masks = masks[keep]
+            target['masks'] = masks
+
+        # for conversion to coco api
+        area = torch.tensor([obj['area'] for obj in anno])
+        iscrowd = torch.tensor([obj['iscrowd'] for obj in anno])
+        target['area'] = area
+        target['iscrowd'] = iscrowd
+        return {'image': image, 'label': target}
+
+
+class ISAID(NonGeoDataset):
+    """iSAID dataset.
+
+    The `iSAID <https://captain-whu.github.io/iSAID/>`_ dataset is a large-scale instance segmentation dataset for aerial imagery.
+    It builds upon the DOTA V1 dataset, but includes instance-level annotations for 15 object categories.
+
+    Dataset features:
+
+    * multi-class instance segmentation
+    * multi-class object detection
+    * aerial imagery over various GSDs
+
+    Dataset format:
+
+    * images are three channel RGB PNGs with various pixel dimensions
+    * labels are annotaitons in json MSCOCO format
+
+    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
+
+    If you use this dataset in your research, please cite the following paper:
+
+    * https://arxiv.org/abs/1905.12886
+    * https://arxiv.org/abs/1711.10398
+
+    .. versionadded:: 0.7
+
+    .. note::
+
+       This dataset requires the following additional library to be installed:
+
+       * `pycocotools <https://pypi.org/project/pycocotools/>`_ to load the
+         annotations
+    """
+
+    img_url = 'https://huggingface.co/datasets/torchgeo/dota/tree/main/{}'
+
+    img_files: ClassVar[dict[str, dict[str, str]]] = {
+        'train': {'filename': 'dotav1_images_train.tar.gz', 'md5': ''},
+        'val': {'filename': 'dotav1_images_val.tar.gz', 'md5': ''},
+    }
+
+    label_url = 'https://huggingface.co/datasets/torchgeo/isaid/tree/main/{}'
+
+    label_files: ClassVar[dict[str, dict[str, str]]] = {
+        'train': {'filename': 'isaid_annotations_train.tar.gz', 'md5': ''},
+        'val': {'filename': 'isaid_annotations_val.tar.gz', 'md5': ''},
+    }
+
+    classes: ClassVar[dict[int, str]] = {
+        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: 'vehicle',
+        10: 'helicopter',
+        11: 'roundabout',
+        12: 'swimming pool',
+        13: 'soccer ball field',
+        14: 'container crane',
+    }
+
+    valid_splits = ('train', 'val')
+
+    def __init__(
+        self,
+        root: Path = 'data',
+        split: str = 'train',
+        transforms: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
+        download: bool = False,
+        checksum: bool = False,
+    ) -> None:
+        """Initialize a new VHR-10 dataset instance.
+
+        Args:
+            root: root directory where dataset can be found
+            split: one of "positive" or "negative"
+            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`` argument is invalid
+            DatasetNotFoundError: If dataset is not found and *download* is False.
+            DependencyNotFoundError: if pycocotools is
+                not installed.
+        """
+        assert split in self.valid_splits, (
+            f"Invalid split '{split}', please use one of {self.valid_splits}"
+        )
+
+        self.root = root
+        self.split = split
+        self.transforms = transforms
+        self.download = download
+        self.checksum = checksum
+
+        self._verify()
+
+        pc = lazy_import('pycocotools.coco')
+        self.coco = pc.COCO(
+            os.path.join(
+                self.root, self.split, 'Annotations', f'iSAID_{self.split}.json'
+            )
+        )
+        self.coco_convert = ConvertCocoAnnotations()
+        self.ids = list(sorted(self.coco.imgs.keys()))
+
+    def __len__(self) -> int:
+        """Return the length of the dataset."""
+        return len(self.ids)
+
+    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
+        """
+        id_ = index % len(self) + 1
+
+        sample: dict[str, Any] = {
+            'image': self._load_image(id_),
+            'label': self._load_mask(id_),
+        }
+
+        sample = self.coco_convert(sample)
+        sample['labels'] = sample['label']['labels']
+        sample['boxes'] = sample['label']['boxes']
+        sample['masks'] = sample['label']['masks']
+        del sample['label']
+
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+
+        return sample
+
+    def _load_mask(self, id_: int) -> dict[str, Any]:
+        """Load mask.
+
+        Args:
+            id_: image ID for coco
+
+        Returns:
+            instance mask tensor with unique IDs
+        """
+        annot = self.coco.loadAnns(self.coco.getAnnIds(id_ - 1))
+
+        target = dict(image_id=id_, annotations=annot)
+        return target
+
+    def _load_image(self, id_: int) -> Tensor:
+        """Load an image from a given path.
+
+        Args:
+            id_: image ID for coco
+
+        Returns:
+            image tensor
+        """
+        filename = os.path.join(
+            self.root, self.split, 'images', self.coco.imgs[id_ - 1]['file_name']
+        )
+        image = Image.open(filename).convert('RGB')
+        return torch.from_numpy(np.array(image).transpose(2, 0, 1)).float()
+
+    def _verify(self) -> None:
+        """Verify the integrity of the dataset."""
+        # check presence of directories
+        dirs = ['images', 'Annotations', 'Instance_masks', 'Semantic_masks']
+        exists = [
+            os.path.exists(os.path.join(self.root, self.split, dir)) for dir in dirs
+        ]
+
+        if all(exists):
+            return
+
+        # check compressed files
+        exists = []
+        files = [
+            self.img_files[self.split]['filename'],
+            self.label_files[self.split]['filename'],
+        ]
+        md5s = [self.img_files[self.split]['md5'], self.label_files[self.split]['md5']]
+        for file, md5 in zip(files, md5s):
+            if os.path.exists(os.path.join(self.root, file)):
+                if self.checksum and not check_integrity(
+                    os.path.join(self.root, file), md5
+                ):
+                    raise RuntimeError(f'Archive {file} is found but corrupted')
+                exists.append(True)
+                extract_archive(os.path.join(self.root, file), self.root)
+            else:
+                exists.append(False)
+
+        if all(exists):
+            return
+
+        if not self.download:
+            raise DatasetNotFoundError(self)
+
+        # download the dataset
+        for file in files:
+            download_and_extract_archive(
+                self.img_url.format(file), self.root, md5=md5 if self.checksum else None
+            )