Datasets:

Tanishq165
/

HSI_Datasets

	---
	license: apache-2.0
	task_categories:
	- image-classification
	- zero-shot-image-classification

	tags:
	- hyperspectral imaging
	- remote-sensing
	- land-use
	- land-cover
	- HSI
	- earth-observation
	- deep-learning
	- benchmark
	- computer-vision
	pretty_name: HSI Datasets Collection
	size_categories:
	- 10G<n<100G
	language:
	- en

	---

	# 🛰️ HSI Datasets Collection

	A comprehensive collection of 24 publicly available Hyperspectral Image (HSI) datasets curated for research in hyperspectral image classification, land use/land cover (LULC) mapping, and remote sensing deep learning benchmarks.

	> Total Size: ~20.1 GB
	> License: Apache 2.0
	> Maintained by: [Tanishq Rachamalla](https://huggingface.co/Tanishq165), [Aryan Das](https://huggingface.co/aryadomain)
	> HuggingFace Dataset: [https://huggingface.co/datasets/Tanishq165/HSI_Datasets](https://huggingface.co/datasets/Tanishq165/HSI_Datasets)

	---

	## 📖 Dataset Description

	Hyperspectral imaging captures data across hundreds of narrow spectral bands, enabling fine-grained material and land cover discrimination beyond what RGB or multispectral sensors can achieve. This repository consolidates 24 widely-used HSI benchmark datasets in a single place, covering a variety of sensors (AVIRIS, ROSIS, Hyperion, CRISM), geographic regions (USA, Europe, Asia, Mars), and scene types (urban, agricultural, coastal, planetary).

	All datasets are stored in `.mat` format (MATLAB-compatible), making them directly loadable with `scipy.io` in Python.

	---

	## 📦 Included Datasets

	All datasets are hosted on the HuggingFace Hub: [`Tanishq165/HSI_Datasets`](https://huggingface.co/datasets/Tanishq165/HSI_Datasets)

	\| # \| Dataset \| Sensor \| Scene Type \| Region \|
	\|---\|---------\|--------\|------------\|--------\|
	\| 1 \| Augsburg \| DAS Specim \| Urban \| Augsburg, Germany \|
	\| 2 \| Berlin \| HyMap \| Urban \| Berlin, Germany \|
	\| 3 \| Botswana \| NASA EO-1 Hyperion \| Wetland/Vegetation \| Okavango Delta, Africa \|
	\| 4 \| Chikusei \| Headwall Photonics \| Agricultural/Urban \| Chikusei, Japan \|
	\| 5 \| Dioni \| AVIRIS-NG \| Mixed \| Dioni, Greece \|
	\| 6 \| Holden \| MRO CRISM \| Planetary \| Mars \|
	\| 7 \| Houston 2013 \| ITRES CASI-1500 \| Urban \| Houston, TX, USA \|
	\| 8 \| Houston 2018 \| AVIRIS-NG \| Urban \| Houston, TX, USA \|
	\| 9 \| Indian Pines \| NASA AVIRIS \| Agriculture/Forest \| Indiana, USA \|
	\| 10 \| KSC \| NASA AVIRIS \| Wetland/Vegetation \| Florida, USA \|
	\| 11 \| Loukia \| AVIRIS-NG \| Mixed \| Loukia, Greece \|
	\| 12 \| Muufl \| ITRES CASI-1500 \| Urban/Vegetation \| Mississippi, USA \|
	\| 13 \| NiliFossae \| MRO CRISM \| Planetary \| Mars \|
	\| 14 \| Pavia Center \| ROSIS \| Urban \| Pavia, Italy \|
	\| 15 \| Pavia University \| ROSIS \| Urban \| Pavia, Italy \|
	\| 16 \| Pingan \| — \| Urban/Rural \| China \|
	\| 17 \| Qingyun \| — \| Agricultural \| China \|
	\| 18 \| Salinas \| NASA AVIRIS \| Agriculture \| Salinas Valley, USA \|
	\| 19 \| Tangdoaowan \| — \| Coastal \| China \|
	\| 20 \| Trento \| AISA Eagle \| Rural \| Trento, Italy \|
	\| 21 \| Utopia \| MRO CRISM \| Planetary \| Mars \|
	\| 22 \| WHU-Hi-HanChuan \| Headwall Nano \| Agricultural \| HanChuan, China \|
	\| 23 \| WHU-Hi-HongHu \| Headwall Nano \| Agricultural \| HongHu, China \|
	\| 24 \| WHU-Hi-LongKou \| Headwall Nano \| Agricultural \| LongKou, China \|

	---

	## 🗂️ Repository Structure

	### HuggingFace Hub Structure

	```
	HSI_Datasets/
	├── Augsburg/
	├── Berlin/
	├── Botswana/
	├── Chikusei/
	├── Dioni/
	├── Holden/
	├── Houston13/
	├── Houston18/
	├── Indian_Pines/
	├── KSC/
	├── Loukia/
	├── Muufl/
	├── NiliFossae/
	├── Pavia_Center/
	├── Pavia_University/
	├── Pingan/
	├── Qingyun/
	├── Salinas/
	├── Tangdoaowan/
	├── Trento/
	├── Utopia/
	├── WHU-Hi-HanChuan/
	├── WHU-Hi-HongHu/
	└── WHU-Hi-LongKou/
	```
	Each dataset folder contains:
	```
	DatasetName/
	├── data.mat # Hyperspectral cube: (H × W × Bands)
	└── gt.mat # Ground truth label map: (H × W)
	```

	---

	## 🚀 Loading the Data

	### Install dependencies

	```bash
	pip install huggingface_hub scipy numpy h5py scikit-learn pyyaml matplotlib
	```

	### Load a single dataset (via project loader)

	```python
	from utils.data_loader import DatasetLoader
	import numpy as np

	loader = DatasetLoader(use_cache=True)

	# Auto-downloads from HuggingFace Hub on first use
	hsi_cube, labels = loader.load_dataset("Indian_Pines")

	print(f"HSI Cube shape : {hsi_cube.shape}")
	print(f"Labels shape : {labels.shape}")
	print(f"Num classes : {len(np.unique(labels)) - 1}") # excluding background
	```

	### Download all datasets

	```python
	from huggingface_hub import snapshot_download

	local_path = snapshot_download(
	repo_id="Tanishq165/HSI_Datasets",
	repo_type="dataset"
	)
	print(f"Downloaded to: {local_path}")
	```

	---

	## 📐 Data Format

	\| Property \| Details \|
	\|---\|---\|
	\| File Format \| `.mat` (MATLAB / scipy compatible) \|
	\| HSI Cube Shape \| `(Height × Width × Spectral Bands)` \|
	\| Ground Truth Shape \| `(Height × Width)` — integer class labels \|
	\| Background Class \| Label `0` represents unlabeled/background pixels \|
	\| Value Range \| Reflectance values (varies per dataset; typically float32 or uint16) \|

	---

	## 🔬 Applications & Use Cases

	- Hyperspectral Image Classification (HSI) — pixel-wise or patch-based
	- Land Use / Land Cover (LULC) Mapping
	- Benchmarking deep learning architectures — CNNs, Vision Transformers (ViT), Mamba/SSMs, Graph Neural Networks
	- Transfer learning & domain adaptation across HSI scenes
	- Multi-sensor data fusion (e.g., HSI + LiDAR for Trento/Augsburg)
	- Planetary surface analysis — NiliFossae, Utopia and Holden(Mars CRISM data)
	- Spectral unmixing and abundance estimation

	---

	## 📊 Dataset Specifications

	Complete reference for all 24 datasets:

	\| # \| Dataset \| H × W \| Bands \| Classes \| Labeled Samples \| Sensor \| Region \|
	\|---\|---------\|-------\|-------\|---------\|-----------------\|--------\|--------\|
	\| 1 \| Augsburg \| 332 × 485 \| 180 \| 7 \| 78,294 \| DAS Specim \| Germany \|
	\| 2 \| Berlin \| 1723 × 476 \| 244 \| 8 \| 464,671 \| HyMap \| Germany \|
	\| 3 \| Botswana \| 1476 × 256 \| 145 \| 14 \| 3,248 \| EO-1 Hyperion \| Africa \|
	\| 4 \| Chikusei \| 2517 × 2335 \| 128 \| 19 \| 77,592 \| Headwall \| Japan \|
	\| 5 \| Dioni \| 250 × 1376 \| 176 \| 12 \| 20,024 \| AVIRIS-NG \| Greece \|
	\| 6 \| Holden \| 595 × 440 \| 418 \| 6 \| 20,090 \| CRISM \| Mars \|
	\| 7 \| Houston13 \| 349 × 1905 \| 144 \| 15 \| 15,029 \| CASI-1500 \| USA \|
	\| 8 \| Houston18 \| 1202 × 4768 \| 48 \| 20 \| 150,029 \| AVIRIS-NG \| USA \|
	\| 9 \| Indian_Pines \| 200 × 145 \| 145 \| 16 \| 10,249 \| AVIRIS \| USA \|
	\| 10 \| KSC \| 512 × 614 \| 176 \| 13 \| 5,211 \| AVIRIS \| USA \|
	\| 11 \| Loukia \| 249 × 945 \| 176 \| 14 \| 13,503 \| AVIRIS-NG \| Greece \|
	\| 12 \| Muufl \| 325 × 220 \| 64 \| 11 \| 53,687 \| CASI-1500 \| USA \|
	\| 13 \| NiliFossae \| 478 × 593 \| 425 \| 9 \| 26,710 \| CRISM \| Mars \|
	\| 14 \| Pavia Center \| 1096 × 715 \| 102 \| 9 \| 148,152 \| ROSIS \| Italy \|
	\| 15 \| Pavia University \| 610 × 340 \| 103 \| 9 \| 42,776 \| ROSIS \| Italy \|
	\| 16 \| Pingan \| 1230 × 1000 \| 176 \| 10 \| 1,140,937 \| — \| China \|
	\| 17 \| Qingyun \| 880 × 1360 \| 176 \| 6 \| 954,893 \| — \| China \|
	\| 18 \| Salinas \| 512 × 217 \| 204 \| 16 \| 54,129 \| AVIRIS \| USA \|
	\| 19 \| Tangdoaowan \| 1740 × 860 \| 176 \| 18 \| 557,366 \| — \| China \|
	\| 20 \| Trento \| 166 × 600 \| 63 \| 6 \| 30,214 \| AISA Eagle \| Italy \|
	\| 21 \| Utopia \| 478 × 595 \| 432 \| 9 \| 17,338 \| CRISM \| Mars \|
	\| 22 \| WHU-Hi-HanChuan \| 1217 × 303 \| 274 \| 16 \| 257,530 \| Headwall \| China \|
	\| 23 \| WHU-Hi-HongHu \| 940 × 475 \| 270 \| 22 \| 386,693 \| Headwall \| China \|
	\| 24 \| WHU-Hi-LongKou \| 550 × 400 \| 270 \| 9 \| 204,542 \| Headwall \| China \|

	> Note: `H × W` = Height × Width (spatial dimensions). Labeled samples exclude background pixels (class 0).

	---

	## 🗂️ Dataset File Reference

	Each folder contains `.mat` files. The loader auto-detects data vs GT by filename suffix:

	\| Suffix \| Content \|
	\|--------\|---------\|
	\| `_gt.mat` or `gt.mat` \| Ground truth label map \|
	\| `_data.mat` or `data.mat` \| Hyperspectral cube \|
	\| `_corrected.mat` \| Corrected HSI cube (e.g. Indian Pines) \|


	---

	## 🏷️ Class Names Reference

	### Indian_Pines (16 classes)
	1. Alfalfa \| 2. Corn-notill \| 3. Corn-mintill \| 4. Corn \| 5. Grass-pasture \| 6. Grass-trees \| 7. Grass-pasture-mowed \| 8. Hay-windrowed \| 9. Oats \| 10. Soybean-notill \| 11. Soybean-mintill \| 12. Soybean-clean \| 13. Wheat \| 14. Woods \| 15. Buildings-Grass-Trees-Drives \| 16. Stone-Steel-Towers

	### Pavia University (9 classes)
	1. Asphalt \| 2. Meadows \| 3. Gravel \| 4. Trees \| 5. Painted metal sheets \| 6. Bare Soil \| 7. Bitumen \| 8. Self-Blocking Bricks \| 9. Shadows

	### Pavia Center (9 classes)
	1. Water \| 2. Trees \| 3. Asphalt \| 4. Self-Blocking Bricks \| 5. Bitumen \| 6. Tiles \| 7. Shadows \| 8. Meadows \| 9. Bare Soil

	### Salinas (16 classes)
	1. Brocoli_green_weeds_1 \| 2. Brocoli_green_weeds_2 \| 3. Fallow \| 4. Fallow_rough_plow \| 5. Fallow_smooth \| 6. Stubble \| 7. Celery \| 8. Grapes_untrained \| 9. Soil_vinyard_develop \| 10. Corn_senesced_green_weeds \| 11. Lettuce_romaine_4wk \| 12. Lettuce_romaine_5wk \| 13. Lettuce_romaine_6wk \| 14. Lettuce_romaine_7wk \| 15. Vinyard_untrained \| 16. Vinyard_vertical_trellis

	### KSC (13 classes)
	1. Scrub \| 2. Willow swamp \| 3. CP hammock \| 4. Slash pine \| 5. Oak/Broadleaf \| 6. Hardwood \| 7. Swamp \| 8. Graminoid marsh \| 9. Spartina marsh \| 10. Cattail marsh \| 11. Salt marsh \| 12. Mud flats \| 13. Water

	### Botswana (14 classes)
	1. Water \| 2. Hippo grass \| 3. Floodplain grasses 1 \| 4. Floodplain grasses 2 \| 5. Reeds \| 6. Riparian \| 7. Firescar \| 8. Island interior \| 9. Acacia woodlands \| 10. Acacia shrublands \| 11. Acacia grasslands \| 12. Short mopane \| 13. Mixed mopane \| 14. Exposed soils

	### Houston13 (15 classes)
	> 2013 IEEE GRSS Data Fusion Contest \| 2.5 m/pixel \| 380–1050 nm
	1. Healthy Grass \| 2. Stressed Grass \| 3. Synthetic Grass \| 4. Trees \| 5. Soil \| 6. Water \| 7. Residential \| 8. Commercial \| 9. Road \| 10. Highway \| 11. Railway \| 12. Parking Lot 1 \| 13. Parking Lot 2 \| 14. Tennis Court \| 15. Running Track

	### Houston18 (20 classes)
	> 2018 IEEE GRSS Data Fusion Contest \| 1 m/pixel \| 380–1050 nm \| Area: UH campus + surroundings
	1. Healthy Grass \| 2. Stressed Grass \| 3. Artificial Turf \| 4. Evergreen Trees \| 5. Deciduous Trees \| 6. Bare Earth \| 7. Water \| 8. Residential Buildings \| 9. Non-residential Buildings \| 10. Roads \| 11. Sidewalks \| 12. Crosswalks \| 13. Major Thoroughfares \| 14. Highways \| 15. Railways \| 16. Paved Parking Lots \| 17. Unpaved Parking Lots \| 18. Cars \| 19. Trains \| 20. Stadium Seats

	### Trento (6 classes)
	1. Apples \| 2. Buildings \| 3. Ground \| 4. Woods \| 5. Vineyard \| 6. Roads

	### Muufl (11 classes)
	1. Trees \| 2. Mostly grass \| 3. Mixed ground surface \| 4. Dirt and sand \| 5. Road \| 6. Water \| 7. Buildings shadow \| 8. Buildings \| 9. Sidewalk \| 10. Yellow curb \| 11. Cloth panels

	### NiliFossae (9 classes) — Mars
	1. Fe-Olivine \| 2. Epidote \| 3. Chlorite \| 4. Bassanite \| 5. Illite/Muscovite \| 6. Mg-Carbonate \| 7. Plagioclase \| 8. Prehnite \| 9. Serpentine

	### Utopia (9 classes) — Mars
	1. Analcime \| 2. Bassanite \| 3. High-Ca Pyroxene \| 4. Illite/Muscovite \| 5. Low-Ca Pyroxene \| 6. Mg-Smectite \| 7. Monohydrated sulfate \| 8. Plagioclase \| 9. Prehnite

	### Holden (6 classes) — Mars
	1. Analcime \| 2. Plagioclase \| 3. Prehnite \| 4. High-Ca Pyroxene \| 5. Serpentine \| 6. Margarite

	### WHU-Hi-HongHu (22 classes)
	1. Red roof \| 2. Road \| 3. Bare soil \| 4. Cotton \| 5. Cotton firewood \| 6. Rape \| 7. Chinese cabbage \| 8. Pakchoi \| 9. Cabbage \| 10. Tuber mustard \| 11. Brassica parachinensis \| 12. Brassica chinensis \| 13. Small Brassica chinensis \| 14. Lactuca sativa \| 15. Celtuce \| 16. Film covered lettuce \| 17. Romaine lettuce \| 18. Carrot \| 19. White radish \| 20. Garlic sprout \| 21. Broad bean \| 22. Tree

	### WHU-Hi-HanChuan (16 classes)
	1. Strawberry \| 2. Cowpea \| 3. Soybean \| 4. Sorghum \| 5. Water spinach \| 6. Watermelon \| 7. Greens \| 8. Trees \| 9. Grass \| 10. Red roof \| 11. Gray roof \| 12. Plastic \| 13. Bare soil \| 14. Road \| 15. Bright object \| 16. Water

	### WHU-Hi-LongKou (9 classes)
	1. Corn \| 2. Cotton \| 3. Sesame \| 4. Broad-leaf soybean \| 5. Narrow-leaf soybean \| 6. Rice \| 7. Water \| 8. Roads and houses \| 9. Mixed weed

	### Dioni (12 classes)
	1. Dense Urban Fabric \| 2. Mineral Extraction Sites \| 3. Non Irrigated Arable Land \| 4. Fruit Trees \| 5. Olive Groves \| 6. Coniferous Forest \| 7. Dense Sclerophyllous Vegetation \| 8. Sparce Sclerophyllous Vegetation \| 9. Sparsely Vegetated Areas \| 10. Rocks and Sand \| 11. Water \| 12. Coastal Water

	### Loukia (14 classes)
	1. Dense Urban Fabric \| 2. Mineral Extraction Sites \| 3. Non Irrigated Arable Land \| 4. Fruit Trees \| 5. Olive Groves \| 6. Broad Leaved Forest \| 7. Coniferous Forest \| 8. Mixed Forest \| 9. Dense Sclerophyllous Vegetation \| 10. Sparce Sclerophyllous Vegetation \| 11. Sparsely Vegetated Areas \| 12. Rocks and Sand \| 13. Water \| 14. Coastal Water

	### Berlin (8 classes)
	1. Forest \| 2. Residential \| 3. Industrial \| 4. Low Plants \| 5. Soil \| 6. Allotment \| 7. Commercial \| 8. Water

	### Augsburg (7 classes)
	1. Forest \| 2. Residential Area \| 3. Industrial Area \| 4. Low Plants \| 5. Allotment \| 6. Commercial Area \| 7. Water

	### Pingan (10 classes)
	1. Seawater \| 2. Road \| 3. Trees \| 4. Floating pier \| 5. Brick houses \| 6. Steel houses \| 7. Ship \| 8. Car \| 9. Concrete building \| 10. Grass

	### Qingyun (6 classes)
	1. Trees \| 2. Car \| 3. Asphalt road \| 4. Concrete building \| 5. Water \| 6. Grass

	### Tangdoaowan (18 classes)
	1. Rubber track \| 2. Asphalt \| 3. Grassland \| 4. Ligustrum vicaryi \| 5. Bare soil \| 6. Populus \| 7. Flagging \| 8. Boardwalk \| 9. Bulrush \| 10. Coniferous pine \| 11. Buxus sinica \| 12. Ulmus pumila L \| 13. Sandy \| 14. Roof shadows \| 15. Gravel road \| 16. Spiraea \| 17. Photinia serrulata \| 18. Seawater

	### Chikusei (19 classes)
	1. Water \| 2. Bare soil (farmland) \| 3. Bare soil (park) \| 4. Bare soil (roadside) \| 5. Pavement (asphalt) \| 6. Pavement (brick) \| 7. Farmland (paddy) \| 8. Farmland (other) \| 9. Greenhouse \| 10. Grass (park) \| 11. Grass (roadside) \| 12. Tree (park) \| 13. Tree (roadside) \| 14. Forest \| 15. Building (low-rise) \| 16. Building (high-rise) \| 17. Building (factory) \| 18. Power line \| 19. Swimming pool

	---

	## ⚠️ Data Loading Notes

	- Auto-transpose: The loader automatically detects and transposes data to `(H, W, Bands)` if needed.
	- Background class: Class `0` is always background/unlabeled and excluded from training.
	- v7.3 MAT files: Some datasets use HDF5-based `.mat` format. The loader falls back to `h5py` automatically.
	- Class imbalance: Many datasets have highly imbalanced classes (e.g. Indian Pines, Houston18). Consider weighted loss or balanced sampling.

	---

	## 📄 Citation

	If you use this dataset collection in your research, please cite:

	```bibtex
	@misc{tanishq2026hsidatasets,
	author = {Tanishq Rachamalla, Aryan Das},
	title = {HSI Datasets Collection},
	year = {2026},
	publisher = {HuggingFace},
	howpublished = {\url{https://huggingface.co/datasets/Tanishq165/HSI_Datasets}}
	}
	```

	Please also cite the original dataset papers for each specific dataset you use in your work.

	---

	## 🤝 Acknowledgements

	These datasets are sourced from publicly available repositories and the broader remote sensing research community. Full credit goes to the original dataset creators including:

	- NASA JPL / JPL AVIRIS — Indian Pines, Salinas, KSC, Botswana
	- Wuhan University — WHU-Hi series (HanChuan, HongHu, LongKou)
	- IEEE GRSS Data Fusion Contest — Houston 2013, Houston 2018
	- University of Pavia — Pavia Center, Pavia University
	- NASA MRO CRISM team — NiliFossae, Utopia, Holden (Mars datasets)
	- DLR / HyMap — Berlin, Augsburg
	- University of Southern Mississippi — Muufl

	---

	## 📬 Contact

	For questions, issues, or collaboration, feel free to open a discussion on the [HuggingFace dataset page](https://huggingface.co/datasets/Tanishq165/HSI_Datasets/discussions).