List created based on our work Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications
List of remote sensing (RS) multi-view datasets for exploring multi-view fusion learning. 📡 🌎 📡
| Dataset | RS views | Temporal? | Task | Region | URL | Additional URL |
|---|---|---|---|---|---|---|
| DeepExtremeCubes (Rodriguez et al.) | MS optical (S2), weather, DEN, land-cover | 👍 | Image Classification | Global | https://opara.zih.tu-dresden.de/items/bb45480f-f7d3-420f-85b8-b4993715b761 | ... |
| GlobalGeoTree (Mu et al.) | MS optical (S2), weather, DEM, soil properties | 👍 | Image Classification | Global | https://huggingface.co/datasets/yann111/GlobalGeoTree | ... |
| TerraMesh (Blumenstiel et al.) | MS optical (S2), SAR (S1), land-cover, DEM | 👎 | None (unsupervised) | Global | https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh | ... |
| FLAIR-HUB (Garioud et al.) | MS optical (S2, SPOT), aerial, DEM, SAR (S1) | 👎 | Image Segmentation | France | https://huggingface.co/datasets/IGNF/FLAIR-HUB | ... |
| MagicBathyNet | optical (S2, SPOT), aerial, DEM | 👎 | Classification | Cyprus/Poland | https://zenodo.org/records/10470959 | ... |
| AllClear (Zhou et al. 2024) | MS optical (S2, L8, and L9), SAR (S1) | 👎 | Image Regression | Global | https://allclear.cs.cornell.edu/ | .. |
| CropDeepTrans (Barriere et al. 2024) | MS optical (S2), metadata | 👍 | Image Classification | Netherlands and France | https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/DRLL/CropDeepTrans/ | .. |
| MMEarth ( Nedungadi et al. 2024 ) | MS optical (S2), SAR (S1), DSM (aster DEM), vegetation height, landcover, biome, ecoregion, weather (ERA5) | 👎 | None (unsupervised) | Global | https://github.com/vishalned/MMEarth-data | .. |
| Fields of The World (FTW) (Kernel et al. 2024) | two optical images from S2 | 👎 | Image Segmentation | Global | https://source.coop/repositories/kerner-lab/fields-of-the-world/description | .. |
| CropNet Dataset (Lin et al. 2024) | Optical (S2), weather | 👍 | Image Regression | USA | https://huggingface.co/datasets/fudong03/Tiny-CropNet | .. |
| SICKLE (Sani et al. 2024) | MS optical (S2 and L8), Radar (S1) | 👍 | Image Regression, Segmentation | India | https://sites.google.com/iiitd.ac.in/sickle/home | .. |
| GreenEarthNet (Benson et al. 2024) | MS optical (S2), weather, elevation | 👍 | Image Regression | Europe | https://github.com/earthnet2021/earthnet-minicuber | .. |
| Globe230k (Shi et al. 2023) | RGB+NDVI optical (S2), DEM (NASA), SAR (S1) | 👎 | Image Segmentation | Global | https://zenodo.org/records/8429200 | .. |
| Crop Yield Prediction (Perich et al. 2023) | MS optical (S2), weather | 👍 | Pixel-wise regression | Switzerland | https://www.research-collection.ethz.ch/handle/20.500.11850/595228 | .. |
| BEN-GE (Mommert et al. 2023) | MS optical (S2), SAR (S1), DSM (GLO-30), weather (ERA5) | 👎 | Image Segmentation | Europe | https://github.com/HSG-AIML/ben-ge | .. |
| TreeSatAI (Ahlswede et al. 2023) | 2 MS optical (S2, UAV), SAR (S1) | 👎 | Pixel-wise Classification | Germany | https://zenodo.org/record/6780578 | https://doi.org/10.5281/zenodo.6780578 |
| MDAS (Hu et al. 2023) | MS (S2) and HS (EnMAP, HySpex) optical, SAR (S1), DSM (DLR 3K) | 👎 | Pixel-wise Classification | Germany | https://mediatum.ub.tum.de/1657312 | https://doi.org/10.14459/2022mp1657312 |
| MultiEarth (Cha et al. 2022) | MS optical (S2 and L8), SAR (S1) | 👍 | Multiple Tasks | Global | https://sites.google.com/view/rainforest-challenge/multiearth-2023 | .. |
| Satlas (Bastani et al. 2022) | MS optical (S2 and NAIP) | 👍 | Multiple Tasks | Global | https://github.com/allenai/satlas | .. |
| CloudSEN12 (Aybar et al. 2022) | MS optical (S2), SAR (S1), DSM (MERIT) | 👎 | Image Segmentation | Global | https://cloudsen12.github.io/ | .. |
| WHU-OPT-SAR (Li et al. 2022) | MS Optical (G1), SAR (G3) | 👎 | Image Segmentation | China | https://github.com/AmberHen/WHU-OPT-SAR-dataset | .. |
| SEN12MS-CR-TS (Ebel et al. 2022) | MS optical (S2), SAR (S1) | 👍 | Image Segmentation | Global | https://patricktum.github.io/cloud_removal/ | https://patricktum.github.io/cloud_removal/sen12mscrts/ |
| PASTIS-R (Garnot et al. 2022) | MS optical (S2), SAR (S1) | 👍 | Image Segmentation | France | https://github.com/VSainteuf/pastis-benchmark | .. |
| Ombria (Drakonakis et al. 2022) | MS optical (S2), SAR (S1) | 👍 | Image Segmentation | Global | https://github.com/geodrak/OMBRIA | .. |
| DynamicEarthNet (Toker et al. 2022) | 2 MS optical (S2, PlanetFusion), SAR (S1) | 👍 | Image Segmentation | Global | https://mediatum.ub.tum.de/1483140 | https://doi.org/10.14459/2018mp1483140 |
| MultiSenGE (Wenger et al. 2022) | MS optical (S2), SAR (S1) | 👍 | Image Segmentation | France | https://zenodo.org/records/6375466 | .. |
| RapidAI4EO (Marchisio et al. 2021) | MS optical (S2), MS optical (Planet) | 👍 | Image segmentation | Europe | https://rapidai4eo.radiant.earth/ | .. |
| CropHarvest (Tseng et al. 2021) | MS optical+NDVI (S2), SAR (S1), weather (ERA5), DSM (SRTM) | 👍 | Pixel-wise Classification | Global | https://github.com/nasaharvest/cropharvest | .. |
| EarthNet (Requena et al. 2021) | MS optical (S2), weather (E-OBS), DSM (EUDEM) | 👍 | Image Regression | Europe | https://www.earthnet.tech/ | https://www.earthnet.tech/en21x/download/ |
| BigEarthNet-MM (Sumbul et al. 2021) | MS optical (S2), SAR (S1) | 👎 | Multi-label Classification | Europe | https://bigearth.net/ | .. |
| LandCoverNet (Alemohammad et al. 2021) | 2 MS optical (S2, L8), SAR (S1) | 👍 | Image Segmentation | Global | https://doi.org/10.34911/rdnt.d2ce8i | .. |
| Dams (Donchyts et al. 2021) | MS optical (S2) including VI, SAR (S1), DSM | 👎 | Image Segmentation | Global | https://www.kaggle.com/datasets/gdonchyts/global-dams-from-space | .. |
| LFMC from SAR (Rao et al) | MS Optical (L8), SAR (S1), weather, elevation, soil | 👍 | Pixel-wise Regression | USA | https://github.com/kkraoj/lfmc_from_sar | https://beta.source.coop/repositories/stanford/sar-moisture-conent/description/ |
| ForestNet (Irvin et al. 2020) | MS optical (L8), DSM (SRTM), weather (NCEP) | 👍 | Image Segmentation | Indonesia | https://stanfordmlgroup.github.io/projects/forestnet/ | .. |
| AI4Food (Kondmann et al. 2021) | 2 MS optical (S2, PlanetFusion), SAR (S1) | 👍 | Image Classification | Germany | https://doi.org/10.34911/rdnt.z9y7vu | .. |
| Sen1Floods11 (Bonafilia et al. 2020) | MS optical (S2), SAR (S1) | 👎 | Image Segmentation | Global | https://github.com/cloudtostreet/Sen1Floods11 | .. |
| So2Sat LCZ42 (Zhu et al. 2020) | MS Optical (S2), SAR (S1) | 👎 | Image Classification | Global | https://mediatum.ub.tum.de/1483140 | https://doi.org/10.14459/2018mp1483140 |
| CrisisMMD | optical, text (tweets) | 👎 | Classification | Global | https://crisisnlp.qcri.org/crisismmd | ... |
| ISPRS 2D Challenge | UAV-based: MS optical, DSM | 👎 | Image Segmentation | Germany | https://www.isprs.org/education/benchmarks/UrbanSemLab | .. |
| MSLCC | MS optical (S2), SAR (S1) | 👎 | Image Segmentation | Germany | https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-12760/22294_read-51180 | .. |
| University of Houston | UAV-based: HS and RGB optical, DSM (LiDAR) | 👎 | Image Segmentation | USA | https://hyperspectral.ee.uh.edu/?page_id=1075 | .. |
- The RS Views column contains the views (and source where it was obtained), Temporal? column indicates if the labels have a temporal scope.
Feel free to ask me to update some content!
Some abbrevations
| Abbrevation | name |
|---|---|
| DSM | Digital Surface Map |
| EnMAP | Environmental Mapping and Analysis Program |
| G1 | Gaofen-1 |
| G3 | Gaofen-3 |
| L7 | Landsat-7 |
| L8 | Landsat-8 |
| NAIP | National Agriculture Imagery Program |
| NCEP | National Centers for Environmental Prediction |
| S1 | Sentinel-1 |
| S2 | Sentinel-2 |
| SRTM | Shuttle Radar Topography Mission |
| UAV | Unnamed Aerial Vehicle |