diff --git a/docs/api/datamodules.rst b/docs/api/datamodules.rst
index abb0c6eaefa..8b87c0eab77 100644
--- a/docs/api/datamodules.rst
+++ b/docs/api/datamodules.rst
@@ -114,6 +114,11 @@ FireRisk
 
 .. autoclass:: FireRiskDataModule
 
+FLAIR2
+^^^^^^
+
+.. autoclass:: FLAIR2DataModule
+
 GeoNRW
 ^^^^^^
 
diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst
index 96ca225344a..8c6b1c27d43 100644
--- a/docs/api/datasets.rst
+++ b/docs/api/datasets.rst
@@ -297,6 +297,11 @@ FireRisk
 
 .. autoclass:: FireRisk
 
+FLAIR2
+^^^^^^
+
+.. autoclass:: FLAIR2
+
 Forest Damage
 ^^^^^^^^^^^^^
 
diff --git a/docs/api/datasets/non_geo_datasets.csv b/docs/api/datasets/non_geo_datasets.csv
index f000bc1d8da..970f813fe84 100644
--- a/docs/api/datasets/non_geo_datasets.csv
+++ b/docs/api/datasets/non_geo_datasets.csv
@@ -18,6 +18,7 @@ Dataset,Task,Source,License,# Samples,# Classes,Size (px),Resolution (m),Bands
 `FAIR1M`_,OD,Gaofen/Google Earth,"CC-BY-NC-SA-3.0","15,000",37,"1,024x1,024",0.3--0.8,RGB
 `Fields Of The World`_,"S,I",Sentinel-2,"Various","70795","2,3",256x256,10,MSI
 `FireRisk`_,C,NAIP Aerial,"CC-BY-NC-4.0","91,872",7,"320x320",1,RGB
+`FLAIR2`_,S,"IGN, Sentinel-2",OPENLICENSE-2.0,7741,13--18,512x512,0.2--20,"RGB+NIR+NDSM, MSI",
 `Forest Damage`_,OD,Drone imagery,"CDLA-Permissive-1.0","1,543",4,"1,500x1,500",,RGB
 `GeoNRW`_,S,Aerial,"CC-BY-4.0","7,783",11,"1,000x1,000",1,"RGB, DEM"
 `GID-15`_,S,Gaofen-2,-,150,15,"6,800x7,200",3,RGB
diff --git a/tests/data/flair2/FLAIR2/flair-2_centroids_sp_to_patch.json b/tests/data/flair2/FLAIR2/flair-2_centroids_sp_to_patch.json
new file mode 100644
index 00000000000..b1d35abd2f0
--- /dev/null
+++ b/tests/data/flair2/FLAIR2/flair-2_centroids_sp_to_patch.json
@@ -0,0 +1 @@
+{"IMG_273729.tif": [16, 16], "IMG_660532.tif": [16, 16], "IMG_222419.tif": [16, 16], "IMG_291261.tif": [16, 16], "IMG_130148.tif": [16, 16], "IMG_866506.tif": [16, 16], "IMG_069499.tif": [16, 16], "IMG_379153.tif": [16, 16], "IMG_758165.tif": [16, 16], "IMG_446805.tif": [16, 16], "IMG_821871.tif": [16, 16], "IMG_463424.tif": [16, 16], "IMG_881682.tif": [16, 16], "IMG_966251.tif": [16, 16], "IMG_574217.tif": [16, 16], "IMG_658262.tif": [16, 16], "IMG_516804.tif": [16, 16], "IMG_359115.tif": [16, 16], "IMG_775995.tif": [16, 16], "IMG_145759.tif": [16, 16], "IMG_545250.tif": [16, 16], "IMG_492040.tif": [16, 16], "IMG_320394.tif": [16, 16], "IMG_703337.tif": [16, 16], "IMG_312966.tif": [16, 16], "IMG_215360.tif": [16, 16], "IMG_065691.tif": [16, 16], "IMG_647808.tif": [16, 16], "IMG_960096.tif": [16, 16], "IMG_863235.tif": [16, 16]}
\ No newline at end of file
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diff --git a/tests/data/flair2/FLAIR2/flair_2_sen_test.zip b/tests/data/flair2/FLAIR2/flair_2_sen_test.zip
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diff --git a/tests/data/flair2/FLAIR2/flair_2_sen_test/D159_2018/Z15_AU/sen/SEN2_sp_D159_2018_Z15_AU_masks.npy b/tests/data/flair2/FLAIR2/flair_2_sen_test/D159_2018/Z15_AU/sen/SEN2_sp_D159_2018_Z15_AU_masks.npy
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index 00000000000..59494deee14
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diff --git a/tests/data/flair2/FLAIR2/flair_2_sen_test/D159_2018/Z15_AU/sen/SEN2_sp_D159_2018_Z15_AU_products.txt b/tests/data/flair2/FLAIR2/flair_2_sen_test/D159_2018/Z15_AU/sen/SEN2_sp_D159_2018_Z15_AU_products.txt
new file mode 100644
index 00000000000..d69ab4d9800
--- /dev/null
+++ b/tests/data/flair2/FLAIR2/flair_2_sen_test/D159_2018/Z15_AU/sen/SEN2_sp_D159_2018_Z15_AU_products.txt
@@ -0,0 +1 @@
+S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921
\ No newline at end of file
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diff --git a/tests/data/flair2/FLAIR2/flair_2_sen_test/D381_2012/Z15_QT/sen/SEN2_sp_D381_2012_Z15_QT_products.txt b/tests/data/flair2/FLAIR2/flair_2_sen_test/D381_2012/Z15_QT/sen/SEN2_sp_D381_2012_Z15_QT_products.txt
new file mode 100644
index 00000000000..d69ab4d9800
--- /dev/null
+++ b/tests/data/flair2/FLAIR2/flair_2_sen_test/D381_2012/Z15_QT/sen/SEN2_sp_D381_2012_Z15_QT_products.txt
@@ -0,0 +1 @@
+S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921
\ No newline at end of file
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diff --git a/tests/data/flair2/FLAIR2/flair_2_sen_test/D388_2019/Z16_RE/sen/SEN2_sp_D388_2019_Z16_RE_products.txt b/tests/data/flair2/FLAIR2/flair_2_sen_test/D388_2019/Z16_RE/sen/SEN2_sp_D388_2019_Z16_RE_products.txt
new file mode 100644
index 00000000000..0aee5f76843
--- /dev/null
+++ b/tests/data/flair2/FLAIR2/flair_2_sen_test/D388_2019/Z16_RE/sen/SEN2_sp_D388_2019_Z16_RE_products.txt
@@ -0,0 +1 @@
+S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921
\ No newline at end of file
diff --git a/tests/data/flair2/FLAIR2/flair_2_sen_test/D590_2016/Z18_GO/sen/SEN2_sp_D590_2016_Z18_GO_data.npy b/tests/data/flair2/FLAIR2/flair_2_sen_test/D590_2016/Z18_GO/sen/SEN2_sp_D590_2016_Z18_GO_data.npy
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diff --git a/tests/data/flair2/FLAIR2/flair_2_sen_test/D590_2016/Z18_GO/sen/SEN2_sp_D590_2016_Z18_GO_products.txt b/tests/data/flair2/FLAIR2/flair_2_sen_test/D590_2016/Z18_GO/sen/SEN2_sp_D590_2016_Z18_GO_products.txt
new file mode 100644
index 00000000000..1c7c6c9ddc7
--- /dev/null
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@@ -0,0 +1 @@
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diff --git a/tests/data/flair2/FLAIR2/flair_sen_train/D798_2011/Z12_MP/sen/SEN2_sp_D798_2011_Z12_MP_masks.npy b/tests/data/flair2/FLAIR2/flair_sen_train/D798_2011/Z12_MP/sen/SEN2_sp_D798_2011_Z12_MP_masks.npy
new file mode 100644
index 00000000000..2c06dbfc16c
Binary files /dev/null and b/tests/data/flair2/FLAIR2/flair_sen_train/D798_2011/Z12_MP/sen/SEN2_sp_D798_2011_Z12_MP_masks.npy differ
diff --git a/tests/data/flair2/FLAIR2/flair_sen_train/D798_2011/Z12_MP/sen/SEN2_sp_D798_2011_Z12_MP_products.txt b/tests/data/flair2/FLAIR2/flair_sen_train/D798_2011/Z12_MP/sen/SEN2_sp_D798_2011_Z12_MP_products.txt
new file mode 100644
index 00000000000..5a239933a81
--- /dev/null
+++ b/tests/data/flair2/FLAIR2/flair_sen_train/D798_2011/Z12_MP/sen/SEN2_sp_D798_2011_Z12_MP_products.txt
@@ -0,0 +1 @@
+S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921
\ No newline at end of file
diff --git a/tests/data/flair2/FLAIR2/flair_sen_train/D839_2016/Z18_RZ/sen/SEN2_sp_D839_2016_Z18_RZ_data.npy b/tests/data/flair2/FLAIR2/flair_sen_train/D839_2016/Z18_RZ/sen/SEN2_sp_D839_2016_Z18_RZ_data.npy
new file mode 100644
index 00000000000..497e3ad825c
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diff --git a/tests/data/flair2/FLAIR2/flair_sen_train/D839_2016/Z18_RZ/sen/SEN2_sp_D839_2016_Z18_RZ_masks.npy b/tests/data/flair2/FLAIR2/flair_sen_train/D839_2016/Z18_RZ/sen/SEN2_sp_D839_2016_Z18_RZ_masks.npy
new file mode 100644
index 00000000000..da007ba3ba7
Binary files /dev/null and b/tests/data/flair2/FLAIR2/flair_sen_train/D839_2016/Z18_RZ/sen/SEN2_sp_D839_2016_Z18_RZ_masks.npy differ
diff --git a/tests/data/flair2/FLAIR2/flair_sen_train/D839_2016/Z18_RZ/sen/SEN2_sp_D839_2016_Z18_RZ_products.txt b/tests/data/flair2/FLAIR2/flair_sen_train/D839_2016/Z18_RZ/sen/SEN2_sp_D839_2016_Z18_RZ_products.txt
new file mode 100644
index 00000000000..1c7c6c9ddc7
--- /dev/null
+++ b/tests/data/flair2/FLAIR2/flair_sen_train/D839_2016/Z18_RZ/sen/SEN2_sp_D839_2016_Z18_RZ_products.txt
@@ -0,0 +1 @@
+S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921
\ No newline at end of file
diff --git a/tests/data/flair2/FLAIR2/flair_sen_train/D893_2022/Z11_XN/sen/SEN2_sp_D893_2022_Z11_XN_data.npy b/tests/data/flair2/FLAIR2/flair_sen_train/D893_2022/Z11_XN/sen/SEN2_sp_D893_2022_Z11_XN_data.npy
new file mode 100644
index 00000000000..05ba7a49e1d
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diff --git a/tests/data/flair2/FLAIR2/flair_sen_train/D893_2022/Z11_XN/sen/SEN2_sp_D893_2022_Z11_XN_masks.npy b/tests/data/flair2/FLAIR2/flair_sen_train/D893_2022/Z11_XN/sen/SEN2_sp_D893_2022_Z11_XN_masks.npy
new file mode 100644
index 00000000000..15166ff2b0d
Binary files /dev/null and b/tests/data/flair2/FLAIR2/flair_sen_train/D893_2022/Z11_XN/sen/SEN2_sp_D893_2022_Z11_XN_masks.npy differ
diff --git a/tests/data/flair2/FLAIR2/flair_sen_train/D893_2022/Z11_XN/sen/SEN2_sp_D893_2022_Z11_XN_products.txt b/tests/data/flair2/FLAIR2/flair_sen_train/D893_2022/Z11_XN/sen/SEN2_sp_D893_2022_Z11_XN_products.txt
new file mode 100644
index 00000000000..e3102a94f19
--- /dev/null
+++ b/tests/data/flair2/FLAIR2/flair_sen_train/D893_2022/Z11_XN/sen/SEN2_sp_D893_2022_Z11_XN_products.txt
@@ -0,0 +1 @@
+S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921
\ No newline at end of file
diff --git a/tests/data/flair2/FLAIR2/md5s.txt b/tests/data/flair2/FLAIR2/md5s.txt
new file mode 100644
index 00000000000..753f3b69253
--- /dev/null
+++ b/tests/data/flair2/FLAIR2/md5s.txt
@@ -0,0 +1,8 @@
+/home/mathias/Dev/forks/torchgeo/tests/data/flair2/FLAIR2/flair_2_labels_test.zip: b13c4a3cb7ebb5cadddc36474bb386f9
+/home/mathias/Dev/forks/torchgeo/tests/data/flair2/FLAIR2/flair_2_toy_dataset.zip: 850736cd9c092d4ec3445a29da01f742
+/home/mathias/Dev/forks/torchgeo/tests/data/flair2/FLAIR2/flair_2_sen_test.zip: 855540b98f86ab8acec87f9aa1a95e0e
+/home/mathias/Dev/forks/torchgeo/tests/data/flair2/FLAIR2/flair_2_aerial_test.zip: f60ab0671fc02b6c3e490c39b0a22ed0
+/home/mathias/Dev/forks/torchgeo/tests/data/flair2/FLAIR2/flair_sen_train.zip: b719b0859ab1eb7ae0fae73b90fbad80
+/home/mathias/Dev/forks/torchgeo/tests/data/flair2/FLAIR2/flair_aerial_train.zip: 4c7541ec00136e9ee42592931f90f395
+/home/mathias/Dev/forks/torchgeo/tests/data/flair2/FLAIR2/flair_labels_train.zip: b109583c8745342714a817252aeb7f84
+/home/mathias/Dev/forks/torchgeo/tests/data/flair2/FLAIR2/flair_2_centroids_sp_to_patch.zip: 82010d9917c8bce52e18d82dac292b5b
diff --git a/tests/data/flair2/data.py b/tests/data/flair2/data.py
new file mode 100644
index 00000000000..020f34f45cc
--- /dev/null
+++ b/tests/data/flair2/data.py
@@ -0,0 +1,263 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import glob
+import hashlib
+import json
+import os
+import shutil
+import string
+from collections.abc import Sequence
+
+import numpy as np
+import rasterio
+from pyproj import CRS
+
+# General hyperparams / speed up by choosing a small value (actual values will be 256)
+IMG_SIZE = 32
+DUMMY_DATA_SIZE = {'train': 10, 'test': 5}
+
+# Directory structure
+root_dir = '{0}/FLAIR2'
+splits: Sequence[str] = ('train', 'test')
+dir_names: dict[str, dict[str, str]] = {
+    'train': {
+        'img': 'flair_aerial_train',
+        'sen': 'flair_sen_train',
+        'msk': 'flair_labels_train',
+    },
+    'test': {
+        'img': 'flair_2_aerial_test',
+        'sen': 'flair_2_sen_test',
+        'msk': 'flair_2_labels_test',
+    },
+}
+dir_names_toy: dict[str, dict[str, str]] = {
+    'train': {
+        'img': 'flair_2_toy_aerial_train',
+        'sen': 'flair_2_toy_sen_train',
+        'msk': 'flair_2_toy_labels_train',
+    },
+    'test': {
+        'img': 'flair_2_toy_aerial_test',
+        'sen': 'flair_2_toy_sen_test',
+        'msk': 'flair_2_toy_labels_test',
+    },
+}
+# Replace with random digits and letters
+sub_sub_dir_format = 'D{0}_{1}/Z{2}_{3}'
+
+
+# Aerial specifics
+aerial_all_bands: Sequence[str] = ('B01', 'B02', 'B03', 'B04', 'B05')
+aerial_pixel_values: list[int] = list(range(256))
+aerial_profile = {
+    'dtype': np.uint8,
+    'count': len(aerial_all_bands),
+    'width': IMG_SIZE,
+    'height': IMG_SIZE,
+    'crs': CRS.from_epsg(2154),
+    'driver': 'GTiff',
+}
+aerial_format = '.tif'
+
+# Sentinel specifics
+sentinel_format = ['.npy', '.npy', '.txt']
+sentinel_pixel_values: list[int] = list(range(20000))
+sentinel_name_format = 'SEN2_sp_D{0}_{1}_Z{2}_{3}'
+SENTINEL_IMG_SIZE = [156, 207]
+
+
+# Label specifics
+# Theoretically the labels are between 1 and 18, although the flair2 description groups all pixels >13 into one class
+labels_pixel_values: list[int] = list(range(1, 19))
+labels_profile = {
+    'dtype': np.byte,
+    'count': 1,
+    'width': IMG_SIZE,
+    'height': IMG_SIZE,
+    # In theory the date is broken at the moment but assume correct for now
+    'crs': CRS.from_epsg(2154),
+    'driver': 'GTiff',
+}
+labels_format = '.tif'
+
+centroids_dict: dict[str, list[int]] = {}
+
+
+def populate_sub_sub_dirs(
+    dir_path: str, seed: int, type: str, domain_year_zone_location: Sequence[str]
+) -> None:
+    # Create random 6 digit number (might have leading zeros)
+    rng: np.random.Generator = np.random.default_rng(seed)
+
+    # Mimic 1:n mapping of aerial/mask to sentinel data
+    file_id_0 = rng.integers(1000000)
+    file_id_1 = rng.integers(1000000)
+    id_str_0 = f'{file_id_0:06}'
+    id_str_1 = f'{file_id_1:06}'
+    match type:
+        case 'img':
+            create_aerial_image(dir_path, rng, id_str_0)
+            create_aerial_image(dir_path, rng, id_str_1)
+        case 'sen':
+            create_sentinel_arrays(dir_path, rng, domain_year_zone_location)
+        case 'msk':
+            create_label_mask(dir_path, rng, id_str_0)
+            create_label_mask(dir_path, rng, id_str_1)
+        case _:
+            raise ValueError(f'Unknown type: {type}')
+
+
+def create_aerial_image(dir_path: str, rng: np.random.Generator, id: str) -> None:
+    centroids_dict[f'IMG_{id}.tif'] = [IMG_SIZE // 2, IMG_SIZE // 2]
+    with rasterio.open(
+        os.path.join(dir_path, f'IMG_{id}{aerial_format}'), 'w', **aerial_profile
+    ) as src:
+        for i in range(len(aerial_all_bands)):
+            data = rng.choice(
+                aerial_pixel_values, size=(IMG_SIZE, IMG_SIZE), replace=True
+            ).astype(np.uint8)
+            src.write(data, i + 1)
+
+
+def create_sentinel_arrays(
+    dir_path: str, rng: np.random.Generator, domain_year_zone_location: Sequence[str]
+) -> None:
+    # Create random numpy array of shape (T, 10, H, W) and save it as .npy
+    time_num_samples = rng.integers(1, 10)
+    data = rng.choice(
+        sentinel_pixel_values,
+        size=(
+            time_num_samples,
+            10,
+            rng.choice(SENTINEL_IMG_SIZE),
+            rng.choice(SENTINEL_IMG_SIZE),
+        ),
+        replace=True,
+    ).astype(np.uint8)
+    snow_cloud_mask = rng.choice(
+        [0, 100],
+        size=(
+            time_num_samples,
+            rng.choice(SENTINEL_IMG_SIZE),
+            rng.choice(SENTINEL_IMG_SIZE),
+        ),
+        replace=True,
+    ).astype(np.uint8)
+
+    name = sentinel_name_format.format(*domain_year_zone_location)
+    np.save(os.path.join(dir_path, f'{name}_data{sentinel_format[0]}'), data)
+    np.save(
+        os.path.join(dir_path, f'{name}_masks{sentinel_format[1]}'), snow_cloud_mask
+    )
+
+    # Create products.txt
+    with open(os.path.join(dir_path, f'{name}_products{sentinel_format[2]}'), 'w') as f:
+        for _ in range(time_num_samples):
+            f.write('S2A_MSIL2A_20210415T105021_N0300_R051_T31UDP_20210415T135921')
+
+
+def create_label_mask(dir_path: str, rng: np.random.Generator, id: str) -> None:
+    data = rng.choice(
+        labels_pixel_values, size=(IMG_SIZE, IMG_SIZE), replace=True
+    ).astype(np.byte)
+    with rasterio.open(
+        os.path.join(dir_path, f'MSK_{id}{labels_format}'), 'w', **labels_profile
+    ) as src:
+        src.write(data, 1)
+
+
+def create_metadata(root_dir: str) -> None:
+    # Create file flair-2_centroids_sp_to_patch.json
+    with open(os.path.join(root_dir, 'flair-2_centroids_sp_to_patch.json'), 'w') as f:
+        json.dump(centroids_dict, f)
+
+
+if __name__ == '__main__':
+    root_dir = root_dir.format(os.getcwd())
+
+    # Remove the root directory if it exists
+    if os.path.exists(root_dir):
+        shutil.rmtree(root_dir)
+
+    def create_dir_structure(root_dir: str, dir_names: dict) -> None:
+        # Create the directory structure
+        for split in splits:
+            for i in range(DUMMY_DATA_SIZE[split]):
+                # Reproducible and the same for all types
+                seed = int(hashlib.md5(f'{split}{i}'.encode()).hexdigest(), 16)
+                rng: np.random.Generator = np.random.default_rng(seed)
+
+                random_domain = rng.integers(100, 1000)
+                random_year = rng.integers(2010, 2023)
+                random_zone = rng.integers(10, 23)
+                random_area = ''.join(rng.choice(list(string.ascii_uppercase), size=2))
+                for type, sub_dir in dir_names[split].items():
+                    # E.g. D123_2021/Z1_UF
+                    sub_sub_dir = sub_sub_dir_format.format(
+                        random_domain, random_year, random_zone, random_area
+                    )
+
+                    # type adds last directory, one of: img, sen, msk
+                    dir_path = os.path.join(root_dir, sub_dir, sub_sub_dir, type)
+                    os.makedirs(dir_path, exist_ok=True)
+
+                    # Required for sentinel data arrays (npy) and products.txt
+                    domain_year_zone_location = [
+                        str(random_domain),
+                        str(random_year),
+                        str(random_zone),
+                        random_area,
+                    ]
+                    populate_sub_sub_dirs(
+                        dir_path, seed, type, domain_year_zone_location
+                    )
+
+                    create_metadata(root_dir)
+
+    root_dir_toy = os.path.join(root_dir, 'flair_2_toy_dataset')
+    os.makedirs(root_dir_toy, exist_ok=True)
+    create_dir_structure(root_dir, dir_names)
+    create_dir_structure(root_dir_toy, dir_names_toy)
+
+    # zip each directory/file in root_dir
+    for element in glob.glob(f'{root_dir}/**'):
+        print(element)
+        if os.path.isdir(element):
+            # Make archive with element name as root directory
+            shutil.make_archive(
+                element, 'zip', os.path.dirname(element), os.path.basename(element)
+            )
+        else:
+            shutil.make_archive(
+                element.removesuffix('.json'),
+                'zip',
+                os.path.dirname(element),
+                os.path.basename(element),
+            )
+
+    # for toy, zip the entire root_dir_toy
+    shutil.make_archive(
+        root_dir_toy,
+        'zip',
+        os.path.dirname(root_dir_toy),
+        os.path.basename(root_dir_toy),
+    )
+
+    # Rename flair-2_centroids_sp_to_patch.json to flair_2_centroids_sp_to_patch.json to replicate
+    # the inconsistency from the actual flair2 dataset
+    old_metadata_path = os.path.join(root_dir, 'flair-2_centroids_sp_to_patch.zip')
+    new_metadata_path = os.path.join(root_dir, 'flair_2_centroids_sp_to_patch.zip')
+    if os.path.exists(old_metadata_path):
+        os.rename(old_metadata_path, new_metadata_path)
+
+    # Compute md5 for each zip file
+    with open(os.path.join(root_dir, 'md5s.txt'), 'w') as md5_file:
+        for element in glob.glob(f'{root_dir}/**'):
+            if element.endswith('.zip'):
+                with open(element, 'rb') as f:
+                    md5 = hashlib.md5(f.read()).hexdigest()
+                    md5_file.write(f'{element}: {md5}\n')
diff --git a/tests/datamodules/test_flair2.py b/tests/datamodules/test_flair2.py
new file mode 100644
index 00000000000..2238219e45a
--- /dev/null
+++ b/tests/datamodules/test_flair2.py
@@ -0,0 +1,43 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+
+import matplotlib.pyplot as plt
+import pytest
+
+from torchgeo.datamodules import FLAIR2DataModule
+
+
+class TestFLAIR2DataModule:
+    @pytest.fixture
+    def datamodule(self) -> FLAIR2DataModule:
+        root = os.path.join('tests', 'data', 'flair2', 'FLAIR2')
+        batch_size = 2
+        num_workers = 0
+        dm = FLAIR2DataModule(
+            root=root,
+            batch_size=batch_size,
+            num_workers=num_workers,
+            use_sentinel=False,
+        )  # FIXME: use_sentinel=True does not work due to varying dimensions
+        return dm
+
+    def test_train_dataloader(self, datamodule: FLAIR2DataModule) -> None:
+        datamodule.setup('fit')
+        next(iter(datamodule.train_dataloader()))
+
+    def test_val_dataloader(self, datamodule: FLAIR2DataModule) -> None:
+        datamodule.setup('validate')
+        next(iter(datamodule.val_dataloader()))
+
+    def test_test_dataloader(self, datamodule: FLAIR2DataModule) -> None:
+        datamodule.setup('test')
+        next(iter(datamodule.test_dataloader()))
+
+    def test_plot(self, datamodule: FLAIR2DataModule) -> None:
+        datamodule.setup('validate')
+        batch = next(iter(datamodule.val_dataloader()))
+        sample = {'image': batch['image'][0], 'mask': batch['mask'][0]}
+        datamodule.plot(sample)
+        plt.close()
diff --git a/tests/datasets/test_flair2.py b/tests/datasets/test_flair2.py
new file mode 100644
index 00000000000..99fbb6d8c31
--- /dev/null
+++ b/tests/datasets/test_flair2.py
@@ -0,0 +1,140 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+import shutil
+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 FLAIR2, DatasetNotFoundError, FLAIR2Toy
+
+
+class TestFLAIR2:
+    @pytest.fixture(
+        params=[
+            (split, init_class)
+            for split in ['train', 'test']
+            for init_class in [FLAIR2, FLAIR2Toy]
+        ]
+    )
+    def dataset(
+        self, monkeypatch: MonkeyPatch, tmp_path: Path, request: SubRequest
+    ) -> FLAIR2:
+        md5s = {
+            'flair_2_toy_dataset': '850736cd9c092d4ec3445a29da01f742',
+            'flair-2_centroids_sp_to_patch': '82010d9917c8bce52e18d82dac292b5b',
+            'flair_aerial_train': '4c7541ec00136e9ee42592931f90f395',
+            'flair_sen_train': 'b719b0859ab1eb7ae0fae73b90fbad80',
+            'flair_labels_train': 'b109583c8745342714a817252aeb7f84',
+            'flair_2_aerial_test': 'f60ab0671fc02b6c3e490c39b0a22ed0',
+            'flair_2_sen_test': '855540b98f86ab8acec87f9aa1a95e0e',
+            'flair_2_labels_test': 'b13c4a3cb7ebb5cadddc36474bb386f9',
+        }
+
+        monkeypatch.setattr(FLAIR2, 'md5s', md5s)
+        url_prefix = os.path.join('tests', 'data', 'flair2', 'FLAIR2')
+        monkeypatch.setattr(FLAIR2, 'url_prefix', url_prefix)
+
+        root: Path = tmp_path
+        split: str = request.param[0]
+        init_class = request.param[1]
+        bands: tuple[str, ...] = ('B01', 'B02', 'B03', 'B04', 'B05')
+        transforms = nn.Identity()
+
+        flair_class: FLAIR2 = init_class(
+            root,
+            split,
+            bands,
+            transforms,
+            download=True,
+            checksum=True,
+            use_sentinel=True,
+        )
+
+        return flair_class
+
+    def test_get_num_bands(self, dataset: FLAIR2) -> None:
+        assert dataset.get_num_bands() == len(dataset.all_bands)
+
+    def test_per_band_statistics(self, dataset: FLAIR2) -> None:
+        if dataset.split != 'train':
+            return
+
+        mins, maxs, means, stdvs = dataset.per_band_statistics(dataset.split)
+        for stats in [mins, maxs, means, stdvs]:
+            assert isinstance(stats, list)
+            assert stats.__len__() == dataset.get_num_bands()
+            assert all(isinstance(stat, float) for stat in stats)
+
+    @pytest.mark.parametrize('use_sentinel', [True, False])
+    def test_getitem(self, dataset: FLAIR2, use_sentinel: bool) -> None:
+        dataset.use_sentinel = use_sentinel
+        x = dataset[0]
+        assert isinstance(x, dict)
+        assert isinstance(x['image'], torch.Tensor)
+        assert x['image'].shape == (len(dataset.all_bands), 32, 32)
+        assert isinstance(x['mask'], torch.Tensor)
+        assert x['image'].shape[-2:] == x['mask'].shape[-2:]
+
+    def test_len(self, dataset: FLAIR2) -> None:
+        if dataset.split == 'train':
+            assert len(dataset) == 20
+        else:
+            assert len(dataset) == 10
+
+        # Test the 1:n mapping of images to sentinels (i.e. 1 sentinel to 2 images)
+        if not isinstance(dataset, FLAIR2Toy):
+            assert dataset.files[0]['sentinel'] == dataset.files[1]['sentinel']
+
+    def test_already_downloaded(self, dataset: FLAIR2) -> None:
+        init_class = type(dataset)
+        init_class(root=dataset.root, split=dataset.split)
+
+    def test_not_yet_extracted(self, dataset: FLAIR2, tmp_path: Path) -> None:
+        if isinstance(dataset, FLAIR2Toy):
+            shutil.copyfile(
+                os.path.join(
+                    'tests', 'data', 'flair2', 'FLAIR2', 'flair_2_toy_dataset.zip'
+                ),
+                os.path.join(str(tmp_path), 'flair_2_toy_dataset.zip'),
+            )
+        else:
+            filenames = list(dataset.dir_names[dataset.split].values())
+            filenames.append(dataset.centroids_file)
+            dir = os.path.join('tests', 'data', 'flair2', 'FLAIR2')
+            for filename in filenames:
+                filename = filename.replace('-', '_')
+                shutil.copyfile(
+                    os.path.join(dir, f'{filename}.zip'),
+                    os.path.join(str(tmp_path), f'{filename}.zip'),
+                )
+
+        init_class = type(dataset)
+        init_class(root=str(tmp_path), split=dataset.split)
+
+    def test_invalid_split(self, dataset: FLAIR2) -> None:
+        with pytest.raises(AssertionError):
+            init_class = type(dataset)
+            init_class(split='foo')
+
+    def test_not_downloaded(self, dataset: FLAIR2, tmp_path: Path) -> None:
+        with pytest.raises(DatasetNotFoundError, match='Dataset not found'):
+            init_class = type(dataset)
+            init_class(str(tmp_path) + 'tmp', download=False)
+
+    @pytest.mark.parametrize('use_sentinel', [True, False])
+    def test_plot(self, dataset: FLAIR2, use_sentinel: bool) -> None:
+        dataset.use_sentinel = use_sentinel
+        dataset.plot(dataset[0], suptitle='Test')
+        plt.close()
+
+        sample = dataset[0]
+        sample['prediction'] = torch.clone(sample['mask'])
+        dataset.plot(sample, suptitle='Prediction')
+        plt.close()
diff --git a/torchgeo/datamodules/__init__.py b/torchgeo/datamodules/__init__.py
index dc9513a6524..acf80d3ca82 100644
--- a/torchgeo/datamodules/__init__.py
+++ b/torchgeo/datamodules/__init__.py
@@ -17,6 +17,7 @@
 from .eurosat import EuroSAT100DataModule, EuroSATDataModule, EuroSATSpatialDataModule
 from .fair1m import FAIR1MDataModule
 from .fire_risk import FireRiskDataModule
+from .flair2 import FLAIR2DataModule
 from .ftw import FieldsOfTheWorldDataModule
 from .geo import BaseDataModule, GeoDataModule, NonGeoDataModule
 from .geonrw import GeoNRWDataModule
@@ -70,6 +71,7 @@
     'EuroSATDataModule',
     'EuroSATSpatialDataModule',
     'FAIR1MDataModule',
+    'FLAIR2DataModule',
     'FieldsOfTheWorldDataModule',
     'FireRiskDataModule',
     'GID15DataModule',
diff --git a/torchgeo/datamodules/flair2.py b/torchgeo/datamodules/flair2.py
new file mode 100644
index 00000000000..e231297a33a
--- /dev/null
+++ b/torchgeo/datamodules/flair2.py
@@ -0,0 +1,79 @@
+"""This module contains the FLAIR2DataModule class for loading the FLAIR2 dataset.
+
+The FLAIR dataset is released under open license 2.0:
+- https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf
+- https://ignf.github.io/FLAIR/#FLAIR2
+
+Code for loading dataset licensed under the MIT License.
+"""
+
+from typing import Any
+
+import kornia.augmentation as K
+import torch
+from torch.utils.data import random_split
+
+from ..datasets import FLAIR2, FLAIR2Toy
+from ..samplers.utils import _to_tuple
+from ..transforms import AugmentationSequential
+from .geo import NonGeoDataModule
+
+
+class FLAIR2DataModule(NonGeoDataModule):
+    """LightningDataModule implementation for the FLAIR2 dataset.
+
+    Uses the train/test splits from the dataset.
+    """
+
+    def __init__(
+        self,
+        batch_size: int = 64,
+        patch_size: tuple[int, int] | int = 64,
+        val_split_pct: float = 0.2,
+        num_workers: int = 0,
+        use_toy: bool = False,
+        augs: AugmentationSequential | None = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a new FLAIR2DataModule instance.
+
+        Args:
+            batch_size: Size of each mini-batch.
+            patch_size: Size of each patch, either ``size`` or ``(height, width)``.
+                Should be a multiple of 32 for most segmentation architectures.
+            val_split_pct: Percentage of the dataset to use as a validation set.
+            num_workers: Number of workers for parallel data loading.
+            use_toy: Whether to use the toy version of the dataset.
+            augs: Optional augmentations to apply to the dataset.
+            **kwargs: Additional keyword arguments passed to
+                :class:`~torchgeo.datasets.FLAIR2`.
+
+            ..versionadded:: 0.7
+        """
+        self.ds_class = FLAIR2 if not use_toy else FLAIR2Toy
+
+        super().__init__(self.ds_class, batch_size, num_workers, **kwargs)
+
+        self.patch_size = _to_tuple(patch_size)
+        self.val_split_pct = val_split_pct
+
+        self.aug: AugmentationSequential = AugmentationSequential(
+            K.Normalize(mean=self.mean, std=self.std), data_keys=['image', 'mask']
+        )
+
+        self.augs = augs if augs is not None else self.aug
+
+    def setup(self, stage: str) -> None:
+        """Set up datasets.
+
+        Args:
+            stage: Either 'fit', 'validate', 'test', or 'predict'.
+        """
+        if stage in ['fit', 'validate']:
+            self.dataset = self.ds_class(split='train', **self.kwargs)
+            generator = torch.Generator().manual_seed(0)
+            self.train_dataset, self.val_dataset = random_split(
+                self.dataset, [1 - self.val_split_pct, self.val_split_pct], generator
+            )
+        if stage in ['test']:
+            self.test_dataset = self.ds_class(split='test', **self.kwargs)
diff --git a/torchgeo/datasets/__init__.py b/torchgeo/datasets/__init__.py
index 3016c3af7e2..80e0ac8f477 100644
--- a/torchgeo/datasets/__init__.py
+++ b/torchgeo/datasets/__init__.py
@@ -46,6 +46,7 @@
 from .eurosat import EuroSAT, EuroSAT100, EuroSATSpatial
 from .fair1m import FAIR1M
 from .fire_risk import FireRisk
+from .flair2 import FLAIR2, FLAIR2Toy
 from .forestdamage import ForestDamage
 from .ftw import FieldsOfTheWorld
 from .gbif import GBIF
@@ -154,6 +155,7 @@
     'ETCI2021',
     'EUDEM',
     'FAIR1M',
+    'FLAIR2',
     'GBIF',
     'GID15',
     'LEVIRCD',
@@ -208,6 +210,7 @@
     'EuroSAT',
     'EuroSAT100',
     'EuroSATSpatial',
+    'FLAIR2Toy',
     'FieldsOfTheWorld',
     'FireRisk',
     'ForestDamage',
diff --git a/torchgeo/datasets/flair2.py b/torchgeo/datasets/flair2.py
new file mode 100644
index 00000000000..0c7207bdf02
--- /dev/null
+++ b/torchgeo/datasets/flair2.py
@@ -0,0 +1,762 @@
+# Code for loading dataset licensed under the MIT License.
+#
+# FLAIR dataset is realeasd under open license 2.0
+# ..... https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf
+# ..... https://ignf.github.io/FLAIR/#FLAIR2
+#
+
+"""FLAIR2 dataset."""
+
+import glob
+import json
+import os
+from collections.abc import Callable, Collection, Sequence
+from typing import Any, ClassVar, cast
+
+import matplotlib.pyplot as plt
+import numpy as np
+import rasterio
+import torch
+from matplotlib.colors import ListedColormap
+from matplotlib.figure import Figure
+from matplotlib.patches import Patch, Rectangle
+from torch import Tensor
+
+from .errors import DatasetNotFoundError, RGBBandsMissingError
+from .geo import NonGeoDataset
+from .utils import Path, check_integrity, download_url, extract_archive
+
+
+class FLAIR2(NonGeoDataset):
+    """FLAIR #2 (The French Land cover from Aerospace ImageRy) dataset.
+
+    The `FLAIR #2 <https://github.com/IGNF/FLAIR-2>` dataset is an extensive dataset from the French National Institute
+    of Geographical and Forest Information (IGN) that provides a unique and rich resource for large-scale geospatial analysis.
+    The  dataset is sampled countrywide and is composed of over 20 billion annotated pixels of very high resolution aerial
+    imagery at 0.2 m spatial resolution, acquired over three years and different months (spatio-temporal domains).
+
+    The FLAIR2 dataset is a dataset for semantic segmentation of aerial images. It contains aerial images, sentinel-2 images and masks for 13 classes.
+    The dataset is split into a training and test set.
+
+    Dataset features:
+
+    * over 20 billion annotated pixels
+    * aerial imagery
+        * 5x512x512
+        * 0.2m spatial resolution
+        * 5 channels (RGB-NIR-Elevation)
+    * Sentinel-2 imagery
+        * 10-20m spatial resolution
+        * 10 spectral bands
+        * snow/cloud masks (with 0-100 probability)
+        * multiple time steps (T)
+        * Tx10xWxH, T, W, H are variable
+    * label (masks)
+        * 512x512
+        * 13 classes
+
+    Dataset classes:
+
+    0: "building",
+    1: "pervious surface",
+    2: "impervious surface",
+    3: "bare soil",
+    4: "water",
+    5: "coniferous",
+    6: "deciduous",
+    7: "brushwood",
+    8: "vineyard",
+    9: "herbaceous vegetation",
+    10: "agricultural land",
+    11: "plowed land",
+    12: "other"
+
+    If you use this dataset in your research, please cite the following paper:
+
+    * https://doi.org/10.48550/arXiv.2310.13336
+    """
+
+    splits: ClassVar[Sequence[str]] = ('train', 'test')
+
+    url_prefix: ClassVar[str] = (
+        'https://storage.gra.cloud.ovh.net/v1/AUTH_366279ce616242ebb14161b7991a8461/defi-ia/flair_data_2'
+    )
+    md5s: ClassVar[dict[str, str]] = {
+        'flair-2_centroids_sp_to_patch': 'f8ba3b176197c254b6c165c97e93c759',
+        'flair_aerial_train': '0f575b360800f58add19c08f05e18429',
+        'flair_sen_train': '56fbbd465726ea4dfeea02734edd7cc5',
+        'flair_labels_train': '80d3cd2ee117a61128faa08cbb842c0c',
+        'flair_2_aerial_test': 'a647e0ba7e5345b28c48d7887ee79888',
+        'flair_2_sen_test': 'aae8649dbe457620a76269d915d07624',
+        'flair_2_labels_test': '394a769ffcb4a783335eecd3f8baef57',
+    }
+
+    dir_names: ClassVar[dict[str, dict[str, str]]] = {
+        'train': {
+            'images': 'flair_aerial_train',
+            'sentinels': 'flair_sen_train',
+            'masks': 'flair_labels_train',
+        },
+        'test': {
+            'images': 'flair_2_aerial_test',
+            'sentinels': 'flair_2_sen_test',
+            'masks': 'flair_2_labels_test',
+        },
+    }
+    globs: ClassVar[dict[str, str]] = {
+        'images': 'IMG_*.tif',
+        'sentinels': 'SEN2_*{0}.npy',
+        'masks': 'MSK_*.tif',
+    }
+    centroids_file: str = 'flair-2_centroids_sp_to_patch'
+    # Sentinel super patch size according to datapaper
+    super_patch_size: int = 40
+
+    # Band information
+    rgb_bands: tuple[str, str, str] = ('B01', 'B02', 'B03')
+    all_bands: tuple[str, str, str, str, str] = ('B01', 'B02', 'B03', 'B04', 'B05')
+
+    # Note: the original dataset contains 18 classes, but the dataset paper suggests
+    # grouping all classes >13 into "other" class, due to underrepresentation
+    classes: tuple[str, ...] = (
+        'building',
+        'pervious surface',
+        'impervious surface',
+        'bare soil',
+        'water',
+        'coniferous',
+        'deciduous',
+        'brushwood',
+        'vineyard',
+        'herbaceous vegetation',
+        'agricultural land',
+        'plowed land',
+        'other',
+    )
+    # Define a colormap for the classes
+    cmap = ListedColormap(
+        [
+            'cyan',  # building
+            'lightgray',  # pervious surface
+            'darkgray',  # impervious surface
+            'saddlebrown',  # bare soil
+            'blue',  # water
+            'darkgreen',  # coniferous
+            'forestgreen',  # deciduous
+            'olive',  # brushwood
+            'purple',  # vineyard
+            'lime',  # herbaceous vegetation
+            'yellow',  # agricultural land
+            'orange',  # plowed land
+            'red',  # other
+        ]
+    )
+
+    statistics: ClassVar[dict[str, dict[str, dict[str, float]]]] = {
+        'train': {
+            'B01': {
+                'min': 0.0,
+                'max': 255.0,
+                'mean': 113.77526983072,
+                'stdv': 1.4678962001526,
+            },
+            'B02': {
+                'min': 0.0,
+                'max': 255.0,
+                'mean': 118.08112962721,
+                'stdv': 1.2889349378677,
+            },
+            'B03': {
+                'min': 0.0,
+                'max': 255.0,
+                'mean': 109.27393364381,
+                'stdv': 1.2674219560871,
+            },
+            'B04': {
+                'min': 0.0,
+                'max': 255.0,
+                'mean': 102.36417944851,
+                'stdv': 1.1057592647291,
+            },
+            'B05': {
+                'min': 0.0,
+                'max': 255.0,
+                'mean': 16.697295721745,
+                'stdv': 0.82764953440507,
+            },
+        }
+    }
+
+    @staticmethod
+    def per_band_statistics(
+        split: str, bands: Sequence[str] = all_bands
+    ) -> tuple[list[float], ...]:
+        """Get statistics (min, max, means, stdvs) for each used band in order.
+
+        Args:
+            split (str): Split for which to get statistics (currently only for train)
+            bands (Sequence[str], optional): Bands of interest, will be returned in ordered manner. Defaults to all_bands.
+
+        Returns:
+            tuple[list[float]]: Filtered, ordered statistics for each band
+        """
+        assert (
+            split in FLAIR2.statistics.keys()
+        ), f"Statistics for '{split}' not available; use: '{list(FLAIR2.statistics.keys())}'"
+        ordered_bands_statistics = list(
+            dict(
+                filter(
+                    lambda keyval: keyval[0] in bands, FLAIR2.statistics[split].items()
+                )
+            ).values()
+        )
+        mins = list(map(lambda dict: dict['min'], ordered_bands_statistics))
+        maxs = list(map(lambda dict: dict['max'], ordered_bands_statistics))
+        means = list(map(lambda dict: dict['mean'], ordered_bands_statistics))
+        stdvs = list(map(lambda dict: dict['stdv'], ordered_bands_statistics))
+
+        return mins, maxs, means, stdvs
+
+    def __init__(
+        self,
+        root: Path = 'data',
+        split: str = 'train',
+        bands: Sequence[str] = all_bands,
+        transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
+        download: bool = False,
+        checksum: bool = False,
+        use_sentinel: bool = False,
+    ) -> None:
+        """Initialize a new FLAIR2 dataset instance.
+
+        Args:
+            root: root directory where dataset can be found
+            split: which split to load, one of 'train' or 'test'
+            bands: which bands to load (B01, B02, B03, B04, B05)
+            transforms: optional transforms to apply to sample
+            download: whether to download the dataset if it is not found
+            checksum: whether to verify the dataset using checksums
+            use_sentinel: whether to use sentinel data in the dataset # FIXME: sentinel does not work with dataloader due to varying dimensions
+
+        Raises:
+            DatasetNotFoundError
+
+        ..versionadded:: 0.7
+        """
+        assert (
+            split in self.splits
+        ), f"Split '{split}' not in supported splits: '{self.splits}'"
+
+        self.root = root
+        self.split = split
+        self.transforms = transforms
+        self.download = download
+        self.checksum = checksum
+        self.bands = bands
+        self.use_sentinel = use_sentinel
+
+        self._verify()
+        self.centroids = self._load_centroids(self.centroids_file)
+
+        self.files = self._load_files()
+
+    def get_num_bands(self) -> int:
+        """Return the number of bands in the dataset.
+
+        Returns:
+            int: number of bands in the initialized dataset (might vary from all_bands)
+        """
+        return len(self.bands)
+
+    def __getitem__(self, index: int) -> dict[str, Tensor]:
+        """Return an index within the dataset.
+
+        Args:
+            index: index to return
+
+        Returns:
+            image and mask at that index with image of dimension `get_num_bands()`x512x512,
+            sentinel image of dimension Tx10x512x512 and mask of dimension 512x512
+        """
+        aerial_fn = self.files[index]['image']
+        sentinel_fn = cast(dict[str, str], self.files[index]['sentinel'])
+        mask_fn = self.files[index]['mask']
+
+        aerial = self._load_image(cast(Path, aerial_fn))
+        mask = self._load_target(cast(Path, mask_fn))
+
+        sample: dict[str, Any] = {'image': aerial, 'mask': mask}
+
+        if self.use_sentinel:
+            img_id = os.path.basename(cast(Path, aerial_fn))
+            centroid_x_y = cast(tuple[int, int], self.centroids[img_id])
+            crop_indices = self._get_crop_indices(centroid_x_y)
+            sentinel_data = self._load_sentinel(cast(Path, sentinel_fn['data']))
+            sentinel_mask = self._load_sentinel(
+                cast(Path, sentinel_fn['snow_cloud_mask'])
+            )
+            sample['sentinel_data'] = sentinel_data
+            sample['snow_cloud_mask'] = sentinel_mask
+            sample['crop_indices'] = crop_indices
+
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+
+        return sample
+
+    def __len__(self) -> int:
+        """Return the number of datapoints in the dataset.
+
+        Returns:
+            length of dataset
+        """
+        return len(self.files)
+
+    def _load_centroids(self, filename: str) -> dict[str, list[int]]:
+        """Load centroids for mapping sentinel super-areas to aerial patches.
+
+        For detailed information on super-patches, see p.4f of datapaper and  `flair-2_centroids_sp_to_patch.json`.
+        CAUTION: centroids are stored as y, x
+
+        Args:
+            filename: name of the file containing centroids
+
+        Returns:
+            dict: centroids for super-patches
+        """
+        with open(os.path.join(self.root, f'{filename}.json')) as f:
+            centroids = json.load(f)
+            return cast(dict[str, list[int]], centroids)
+
+    def _get_crop_indices(self, centroid: tuple[int, int]) -> tuple[slice, slice]:
+        """Return indices to crop a super-patch from sentinel data based centroid coordinates.
+
+        For detailed information on super-patches, see p.4f of datapaper and `flair-2_centroids_sp_to_patch.json`.
+
+        Args:
+            centroid: centroid coordinates
+
+        Returns:
+            tuple[slice, slice]: crop indices for sentinel data
+        """
+        y, x = centroid
+        eigth_size = self.super_patch_size // 8
+
+        return (
+            slice(x - eigth_size, x + eigth_size),
+            slice(y - eigth_size, y + eigth_size),
+        )
+
+    def _load_files(self) -> list[dict[str, Collection[str]]]:
+        """Return the paths of the files in the dataset.
+
+        Args:
+            root: root dir of dataset
+
+        Returns:
+            list of dicts containing paths for each pair of image, masks
+        """
+        images = sorted(
+            glob.glob(
+                os.path.join(
+                    self.root,
+                    self.dir_names[self.split]['images'],
+                    '**',
+                    self.globs['images'],
+                ),
+                recursive=True,
+            )
+        )
+
+        sentinels_data = sorted(
+            glob.glob(
+                os.path.join(
+                    self.root,
+                    self.dir_names[self.split]['sentinels'],
+                    '**',
+                    self.globs['sentinels'].format('_data'),
+                ),
+                recursive=True,
+            )
+        )
+        sentinels_mask = sorted(
+            glob.glob(
+                os.path.join(
+                    self.root,
+                    self.dir_names[self.split]['sentinels'],
+                    '**',
+                    self.globs['sentinels'].format('_masks'),
+                ),
+                recursive=True,
+            )
+        )
+        sentinels = [
+            {'data': data, 'snow_cloud_mask': mask}
+            for data, mask in zip(sentinels_data, sentinels_mask)
+        ]
+
+        masks = sorted(
+            glob.glob(
+                os.path.join(
+                    self.root,
+                    self.dir_names[self.split]['masks'],
+                    '**',
+                    self.globs['masks'],
+                ),
+                recursive=True,
+            )
+        )
+
+        # One sentinel image might contain multiple aerial images, thus we need to match them
+        # without assuming a 1:1 mapping
+        sentinel_lookup = {'/'.join(s['data'].split('/')[-4:-2]): s for s in sentinels}
+        files = [
+            dict(
+                image=image,
+                sentinel=sentinel_lookup['/'.join(image.split('/')[-4:-2])],
+                mask=mask,
+            )
+            for image, mask in zip(images, masks)
+        ]
+
+        return files
+
+    def _load_image(self, path: Path) -> Tensor:
+        """Load a single image.
+
+        Args:
+            path: path to the image
+
+        Returns:
+            Tensor: the loaded image
+        """
+        with rasterio.open(path) as f:
+            array: np.typing.NDArray[np.int_] = f.read()
+            tensor = torch.from_numpy(array).float()
+            if 'B05' in self.bands:
+                # Height channel will always be the last dimension
+                tensor[-1] = torch.div(tensor[-1], 5)
+
+        return tensor
+
+    def _load_sentinel(self, path: Path) -> Tensor:
+        # FIXME: should this really be returned as a tuple?
+        """Load a sentinel array.
+
+        Args:
+            path (Path): path to sentinel img (data or snow cloud mask)
+
+        Returns:
+            Tensor: image as tensors of shape TxCxHxW (time, channels, height, width)
+        """
+        img = torch.from_numpy(np.load(path)).float()
+
+        return img
+
+    def _load_target(self, path: Path) -> Tensor:
+        """Load a single mask corresponding to image.
+
+        Args:
+            path: path to the mask
+
+        Returns:
+            Tensor: the mask of the image
+        """
+        with rasterio.open(path) as f:
+            array: np.typing.NDArray[np.int_] = f.read(1)
+            tensor = torch.from_numpy(array).long()
+            # According to datapaper, the dataset contains classes beyond 13
+            # however, those are grouped into a single "other" class
+            # Rescale the classes to be in the range [0, 12] by subtracting 1
+            torch.clamp(tensor - 1, 0, len(self.classes) - 1, out=tensor)
+
+        return tensor
+
+    def _verify(self) -> None:
+        """Verify the integrity of the dataset."""
+        # Check if centroids metadata file or zip is present
+        # Naming inconsistencies in download url and file name (flair_2_centroids_sp_to_patch.zip vs. flair-2_centroids_sp_to_patch.json)
+        if not os.path.isfile(os.path.join(self.root, f'{self.centroids_file}.json')):
+            if not os.path.isfile(
+                os.path.join(self.root, f'{self.centroids_file}.zip'.replace('-', '_'))
+            ):
+                if not self.download:
+                    raise DatasetNotFoundError(self)
+                self._download(self.centroids_file.replace('-', '_'))
+            self._extract(self.centroids_file.replace('-', '_'))
+
+        # Files to be extracted
+        to_extract: list[str] = []
+
+        # Check if dataset files (by checking glob) are present already
+        for train_or_test, dir_name in self.dir_names[self.split].items():
+            downloaded_path = os.path.join(self.root, dir_name)
+            if not os.path.isdir(downloaded_path):
+                to_extract.append(dir_name)
+                continue
+
+            files_glob = os.path.join(downloaded_path, '**', self.globs[train_or_test])
+            # Format the glob of sentinel `SEN_{0}.npy` to match the actual file name
+            # in other cases, where it is not a format string, the glob will be the same
+            if not glob.glob(files_glob.format(''), recursive=True):
+                to_extract.append(dir_name)
+
+        if not to_extract:
+            print('Data has been downloaded and extracted already...')
+            return
+
+        # Deepcopy files to be extracted and check wether the zip is downloaded
+        to_download = list(map(lambda x: x, to_extract))
+        for candidate in to_extract:
+            zipfile = os.path.join(self.root, f'{candidate}.zip')
+            if glob.glob(zipfile):
+                print(f'Extracting: {candidate}')
+                self._extract(candidate)
+                to_download.remove(candidate)
+
+        # Check if there are still files to download
+        if not to_download:
+            return
+
+        # Check if the user requested to download the dataset
+        if not self.download:
+            raise DatasetNotFoundError(self)
+
+        print('Downloading: ', to_download)
+        for candidate in to_download:
+            self._download(candidate)
+            self._extract(candidate)
+
+    def _download(self, url: str, suffix: str = '.zip') -> None:
+        """Download the dataset."""
+        download_url(
+            os.path.join(self.url_prefix, f'{url}{suffix}'),
+            self.root,
+            md5=self.md5s.get(url, None) if self.checksum else None,
+        )
+        # FIXME: Why is download_url not checking integrity (tests run through)?
+        assert check_integrity(
+            os.path.join(self.root, f'{url}{suffix}'),
+            self.md5s.get(url, None) if self.checksum else None,
+        )
+
+    def _extract(self, file_path: str) -> None:
+        """Extract the dataset."""
+        assert isinstance(self.root, str | os.PathLike)
+        zipfile = os.path.join(self.root, f'{file_path}.zip')
+        extract_archive(zipfile)
+
+    def plot(
+        self,
+        sample: dict[str, Tensor],
+        show_titles: bool = True,
+        suptitle: str | None = None,
+    ) -> Figure:
+        """Plot a sample from the dataset.
+
+        Args:
+            sample: a sample return by :meth:`__getitem__`
+            show_titles: flag indicating whether to show titles above each panel
+            suptitle: optional suptitle to use for figure
+
+        Returns:
+            a matplotlib Figure with the rendered sample
+        """
+
+        def normalize_plot(tensor: Tensor) -> Tensor:
+            """Normalize the plot."""
+            return (tensor - tensor.min()) / (tensor.max() - tensor.min())
+
+        rgb_indices = [self.all_bands.index(band) for band in self.rgb_bands]
+        # Check if RGB bands are present in self.bands
+        if not all([band in self.bands for band in self.rgb_bands]):
+            raise RGBBandsMissingError()
+
+        # Stretch to the full range of the image
+        image = normalize_plot(sample['image'][rgb_indices].permute(1, 2, 0))
+
+        # Get elevation and NIR, R, G if available
+        if 'B05' in self.bands:
+            elevation = sample['image'][self.bands.index('B05')]
+        if 'B04' in self.bands:
+            nir_r_g_indices = [self.bands.index('B04'), rgb_indices[0], rgb_indices[1]]
+            nir_r_g = normalize_plot(sample['image'][nir_r_g_indices].permute(1, 2, 0))
+
+        # Sentinel is a time-series, i.e. use [0]->T=0
+        sentinel = None
+        if self.use_sentinel:
+            crop_indices = cast(Sequence[slice], sample['crop_indices'])
+            sentinel = sample['sentinel_data']
+            sentinel = sentinel[0]
+            sentinel = normalize_plot(sentinel[[2, 1, 0], :, :].permute(1, 2, 0))
+
+        # Obtain mask and predictions if available
+        mask = sample['mask'].numpy().astype('uint8').squeeze()
+
+        showing_predictions = 'prediction' in sample
+        predictions = None
+        if showing_predictions:
+            predictions = sample['prediction'].numpy().astype('uint8').squeeze()
+
+        # Remove none available plots
+        plot_candidates = zip(
+            [
+                'image (R+G+B)',
+                'NIR+R+G',
+                'elevation',
+                'sentinel',
+                'predictions',
+                'mask',
+            ],
+            [image, nir_r_g, elevation, sentinel, predictions, mask],
+        )
+        plots = [plot for plot in plot_candidates if plot[1] is not None]
+
+        num_panels = len(plots)
+
+        kwargs = {
+            'cmap': self.cmap,
+            'vmin': 0,
+            'vmax': len(self.classes),
+            'interpolation': 'none',
+        }
+        fig, axs = plt.subplots(1, num_panels, figsize=(num_panels * 4, 5))
+
+        for plot in plots:
+            im_kwargs = (
+                kwargs.copy() if plot[0] == 'mask' or plot[0] == 'predictions' else {}
+            )
+            if plot[0] == 'sentinel':
+                axs[0].add_patch(
+                    Rectangle(
+                        (crop_indices[0].start, crop_indices[1].start),
+                        crop_indices[0].stop - crop_indices[0].start,
+                        crop_indices[1].stop - crop_indices[1].start,
+                        fill=False,
+                        edgecolor='red',
+                        lw=0.5,
+                    )
+                )
+
+            axs[0].imshow(plot[1], **im_kwargs)
+            axs[0].axis('off')
+            if show_titles:
+                axs[0].set_title(plot[0])
+
+            axs = axs[1:]
+
+        if suptitle is not None:
+            plt.suptitle(suptitle)
+
+        # Create a legend for the mask
+        if 'mask' in [plot[0] for plot in plots]:
+            # Create a legend with class names
+            legend_elements = [
+                Patch(facecolor=self.cmap(i), edgecolor='k', label=cls)
+                for i, cls in enumerate(self.classes)
+            ]
+            fig.legend(
+                handles=legend_elements,
+                loc='upper left',
+                bbox_to_anchor=(0.92, 0.85),
+                fontsize='large',
+            )
+
+        return fig
+
+
+class FLAIR2Toy(FLAIR2):
+    """FLAIR #2 (The French Land cover from Aerospace ImageRy) dataset.
+
+    Toy Version of the dataset. For further information refer to the FLAIR2 dataset.
+    """
+
+    md5s: ClassVar[dict[str, str]] = {
+        'flair_2_toy_dataset': 'ffde17f275fc258dce19331b5e17e10a'
+    }
+
+    dir_names: ClassVar[dict[str, dict[str, str]]] = {
+        'train': {
+            'images': 'flair_2_toy_dataset/flair_2_toy_aerial_train',
+            'sentinels': 'flair_2_toy_dataset/flair_2_toy_sen_train',
+            'masks': 'flair_2_toy_dataset/flair_2_toy_labels_train',
+        },
+        'test': {
+            'images': 'flair_2_toy_dataset/flair_2_toy_aerial_test',
+            'sentinels': 'flair_2_toy_dataset/flair_2_toy_sen_test',
+            'masks': 'flair_2_toy_dataset/flair_2_toy_labels_test',
+        },
+    }
+    centroids_file: str = 'flair_2_toy_dataset/flair-2_centroids_sp_to_patch'
+
+    def __init__(
+        self,
+        root: Path = 'data',
+        split: str = 'train',
+        bands: Sequence[str] = FLAIR2.all_bands,
+        transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
+        download: bool = False,
+        checksum: bool = False,
+        use_sentinel: bool = False,
+    ) -> None:
+        """Initialize a new FLAIR2Toy dataset instance.
+
+        Args:
+            root: root directory where dataset can be found
+            split: which split to load, one of 'train' or 'test'
+            bands: which bands to load (B01, B02, B03, B04, B05)
+            transforms: optional transforms to apply to sample
+            download: whether to download the dataset if it is not found
+            checksum: whether to verify the dataset using checksums
+            use_sentinel: whether to use sentinel data in the dataset # FIXME: sentinel does not work with dataloader due to varying dimensions
+
+        Raises:
+            DatasetNotFoundError
+
+        ..versionadded:: 0.7
+        """
+        print('-' * 80)
+        print('WARNING: Using toy dataset.')
+        print('This dataset should be used for testing purposes only.')
+        print(
+            'Disabling use_toy-flag when initializing the dataset will initialize the full dataset.'
+        )
+        print('-' * 80)
+        super().__init__(
+            root, split, bands, transforms, download, checksum, use_sentinel
+        )
+
+    def _verify(self) -> None:
+        """Verify the integrity of the dataset."""
+        if os.path.isdir(os.path.join(self.root, 'flair_2_toy_dataset')):
+            print(os.path.join(self.root, 'flair_2_toy_dataset'))
+            print('Toy dataset downloaded and extracted already...')
+            return
+
+        if os.path.isfile(os.path.join(self.root, 'flair_2_toy_dataset.zip')):
+            print('Extracting toy dataset...')
+            self._extract()
+            return
+
+        if not self.download:
+            raise DatasetNotFoundError(self)
+
+        self._download('flair_2_toy_dataset')
+        self._extract()
+
+    def _download(self, url: str, suffix: str = '.zip') -> None:
+        """Download the dataset."""
+        download_url(
+            os.path.join(self.url_prefix, f'{url}{suffix}'),
+            self.root,
+            md5=self.md5s.get(url, None) if self.checksum else None,
+        )
+        # FIXME: Why is download_url not checking integrity (tests run through)?
+        # assert check_integrity(os.path.join(self.root, f"{url}{suffix}"), self.md5s.get(url, None) if self.checksum else None)
+
+    def _extract(self, file_name: str = 'flair_2_toy_dataset.zip') -> None:
+        """Extract the dataset."""
+        assert isinstance(self.root, str | os.PathLike)
+        assert os.path.isfile(os.path.join(self.root, file_name))
+        zipfile = os.path.join(self.root, file_name)
+        extract_archive(zipfile)