This page presents a list of satellite imagery datasets with a temporal dimension, mainly satellite image time series (SITS) and satellite videos, for various computer vision and deep learning tasks. It covers multi-temporal datasets with more than two acquisitions but not bi-temporal datasets. We focus mainly on annotated datasets.
Datasets are sorted by annotation granularity. We note that polygons annotations are reserved for crop-type identification tasks, while pixel annotations might be considered in more general tasks such as land cover mapping.
| Dataset name | Year | Image source | Spatial resolution | Temporal resolution | Number of classes | Acquisition |
|---|
| TS-SatFire | 2024 | VIIRS | 357m | Daily acquisition & annotation | 2 | USA (2017-2021) |
| MultiEarth 2023 | 2023 | Sentinel-1 + Sentinel-2 + Landsat-5 + Landsat-8 | 10m + 10m + 30m + 30m | Weekly acquisitions depending on the source & Monthly annotation | 2 | Amazon (1984-2021) |
| MultiEarth 2022 | 2022 | Sentinel-1 + Sentinel-2 + Landsat-5 + Landsat-8 | 10m + 10m + 30m + 30m | Weekly acquisitions depending on the source & Monthly annotation | 2 | Amazon (1984-2021) |
| Dynamic World | 2022 | Sentinel-2 | 10m | Weekly acquisition and weekly automatic annotation without human verification | 9 | Global (2015-present) |
| DynamicEarthNet | 2021 | PlanetFusion | 3m | Daily acquisition & Monthly annotation | 7 | Global (2018-2019) |
| SpaceNet 7 | 2020 | PlanetScope | 4m | Monthly acquisition & annotation | 2 | Global (2017-2020) |
| Dataset name | Year | Image source | Spatial resolution | Temporal resolution | Number of classes | Acquisition |
|---|
| FLAIR-HUB | 2025 | Aerial + DEM + SPOT6-7 + Sentinel-2 + Sentinel-1 | 20cm + 1m + 1.6m + 10m + 10m | Mono-temporal and weekly acquisitions | 19 + 23 | France (2018-2021) |
| ForTy | 2025 | Sentinel-1 + Sentinel-2 + Climate + Elevation | 10m + 10m + 4km + 30m | Seasonal acquisitions | 8 | Global (2018-2020) |
| CONUS | 2025 | Harmonized Landsat and Sentinel-2 (HLS) | 30m | 2 days | 50 | USA (2013-2023) |
| FUSU | 2024 | GoogleEarth + Sentinel-1 + Sentinel-2 | 0.3m + 10m + 10m | Bi-temporal + monthly + monthly acquisitions | 17 | China (2018-2020) |
| CropRot | 2024 | Sentinel-2 | 10m | Weekly acquisitions | 2 | France (2019-2020) |
| PASTIS-HD | 2024 | Sentinel-1 + Sentinel-2 + SPOT6-7 | 5m + 10m + 1.5m | Weekly + weekly + single acquisitions | 18 | France (2019) |
| MultiSenNA | 2024 | Sentinel-1 + Sentinel-2 | 5m + 10m | Daily + weekly acquisition | 14 | Southwestern France (2019-2020) |
| DAFA-LS | 2024 | Planet | 3m | Monthly acquisition | 2 | Afghanistan (2016-2023) |
| BraDD-S1TS | 2023 | Sentinel-1 | 10m | Weekly acquisition | 2 | Brazil (2020-2021) |
| FLAIR #2 | 2023 | Sentinel-2 | 10m | Weekly acquisition | 13 | France (2018-2021) |
| MultiSenGE | 2022 | Sentinel-1 + Sentinel-2 | 5m + 10m | Daily + weekly acquisition | 14 | Eastern France (2019-2020) |
| PASTIS-R | 2021 | Sentinel-1 + Sentinel-2 | 5m + 10m | Daily + weekly acquisition | 18 | France (2019) |
| PASTIS | 2021 | Sentinel-2 | 10m | Weekly acquisition | 18 | France (2018-2019) |
| UTRNet | 2021 | Landsat-8 | 30m | Irregular acquisition | 2 | China (2013-2021) |
| MTLCC | 2018 | Sentinel-2 | 10m | Weekly acquisition & Annual annotation for 2016 and 2017 | 17 | Munich, Germany (2016-2017) |
| TiSeLaC | 2017 | Landsat-8 | 30m | Bi-monthly acquisition | 9 | Reunion Island, France (2014) |
| Dataset name | Year | Image source | Spatial resolution | Temporal resolution | Number of classes | Acquisition |
|---|
| Sen4AgriNet | 2022 | Sentinel-2 | 10m to 60m | Weekly acquisition & Annual annotation | 168 | Catalonia & France (2019-2020) |
| Deep Crop Rotation | 2021 | Sentinel-2 | 10m | Weekly acquisition & Annual annotation | 10 | France (2018-2020) |
| Campo Verde | 2018 | Landsat-8 + Sentinel-1 | 30m + 10m | Bi-monthly acquisition & annotation | 14 | Brazil (2015-2016) |
| LEM | 2018 | Landsat-8 + Sentinel-1 + Sentinel-2 | 30m + 10m + 10m | Bi-monthly (L8+S1) + weekly (S2) acquisition & Monthly annotation | 14 | Brazil (2017-2018) |
| MTLCC | 2018 | Sentinel-2 | 10m | Weekly acquisition & Annual annotation | 17 | Munich, Germany (2016-2017) |
| Dataset name | Year | Image source | Spatial resolution | Temporal resolution | Number of classes | Acquisition |
|---|
| SICKLE | 2024 | Landsat-8 + Sentinel-1 + Sentinel-2 | 30m + 3m + 10m | Bi-monthly + 12d + weekly acquistion | 21 | India (2018-2021) |
| AgriSen-COG | 2023 | Sentinel-2 | 10m | Weekly acquisition | 103 | Austria, Belgium, Spain, Denmark, Netherlands (2019-2020) |
| TimeMatch | 2022 | Sentinel-2 | 10m | Weekly acquisition | 16 | Austria, Denmark, mid-west France, southern France (2017) |
| DENETHOR | 2021 | PlanetFusion | 3m | Daily acquisition | 10 | Germany (2018-2019) |
| EuroCrops | 2021 | Sentinel-2 | / | Weekly acquisition | 43 | Europe (2015-2022) |
| TimeSen2Crop | 2021 | Sentinel-2 | 10m | Weekly acquisition | 16 | Austria (2017-2018) |
| Canadian Cropland | 2021 | Sentinel-2 | 10m | Monthly acquisition | 10 | Canada (2019) |
| ZueriCrop | 2021 | Sentinel-2 | 10m | Weekly acquisition | 48 | Zurich, Switzerland (2019) |
| Crop type in Western Cap | 2021 | PlanetScope + Sentinel-1 + Sentinel-2 | 3m + 10m + 10m | Bi-monthly (Planet+S1) + weekly (S2) acquisition | 5 | South Africa (2017) |
| Spot the crop challenge | 2021 | Sentinel-1 + Sentinel-2 | 5m + 10m | Bi-monthly + weekly acquisition | 10 | South Africa (2016) |
| BreizhCrops | 2020 | Sentinel-2 | 60m | Weekly acquisition | 9 | Brittany, France (2017) |
| Crop type in Ghana | 2020 | PlanetScope + Sentinel-1 + Sentinel-2 | 3m + 10m + 10m | Bi-monthly (Planet+S1) + weekly (S2) acquisition | 4 | Ghana (2017) |
| Crop type on South Soudan | 2020 | PlanetScope + Sentinel-1 + Sentinel-2 | 3m + 10m + 10m | Bi-monthly (Planet+S1) + weekly (S2) acquisition | 4 | South Soudan (2017) |
| CV4A Kenya | 2020 | Sentinel-2 | 10m | Bi-monthly acquisition | 7 | Kenya (2019) |
| Pixel-Set dataset | 2020 | Sentinel-2 | 10m | Weekly acquisition | 20 | France (2017) |
| Dataset name | Year | Image source | Spatial resolution | Temporal resolution | Number of classes | Acquisition |
|---|
| fMoW-Sentinel | 2022 | Sentinel-2 | 10m | Irregular acquisition | 63 | Global (2015-2019) |
| SEN12-FLOOD | 2020 | Sentinel-1 + Sentinel-2 | 10m + 10m | Bi-monthly + weekly acquisition | 2 | African, Iranian and Australian cities (2018-2019) |
| fMoW-RGB | 2018 | DigitalGlobe constellation | multiple resolutions (0.3m to 3.7m) | Irregular acquisition | 63 | Global (2002-2017) |
| Dataset name | Year | Image source | Spatial resolution | Temporal resolution | Number of classes | Acquisition |
|---|
| OPTIMUS | 2025 | Sentinel-2 | 10m | 2-month acquisitions | 2 | Global (2016-2023) |
| Sen4Map | 2024 | Sentinel-2 | 10m + 20m | Weekly acquisition | 119 | Europe (2018) |
| Planted | 2024 | Sentinel-1 + Sentinel-2 + Lansat-7 + ALOS-2 + MODIS | 10m (S1+S2) + 30m (L7+A2) + 250m (M) | Seasonal (S1+S2+L7) yearly (A2) and monthly (M) acquisitions | 64 | Global (2013-2017) |
| TreeSatAI-Time-Series | 2024 | Sentinel-1 + Sentinel-2 | 10m + 10m | Weekly acquisition | 20 | Germany (2017-2020) |
| RapidAI4EO Corpus | 2023 | PlanetFusion + Sentinel-2 | 3m + 10m | 5-day + monthly acquisition | 44 (multi-label) | Europe (2018-2019) |
| Dataset name | Year | Image source | Spatial resolution | Temporal resolution | Acquisition |
|---|
| Open Buildings 2.5D Temporal | 2024 | Sentinel-2 + ? | 10m (S2) + 50cm (?) + 50cm (GT) | Weekly + annual acquisition & Annual annotation | Africa, South Asia, South-East Asia, Latin America and the Caribbean (2016-2023) |
| Wald5Dplus/Forest5Dplus | 2024 | Sentinel-1 + Sentinel-2 | 10m | Weekly acquisition | Germany (2020-2021) |
| Multi-Modal Satellite Imagery Dataset | 2024 | Sentinel-2 + Multilabel metadata | 10m + municipality-level | Weekly (S2) acquisition | Colombia (S2: 2016-2018, metadata: 2007-2019) |
| CropNet | 2024 | Sentinel-2 + WRF-HRRR | 9km + 9km | 14d + 1d & Annual annotation | USA (2017-2022) |
| SICKLE | 2024 | Landsat-8 + Sentinel-1 + Sentinel-2 | 30m + 3m + 10m | Bi-monthly + 12d + weekly acquistion | India (2018-2021) |
| BioMassters | 2023 | Sentinel-1 + Sentinel-2 | 20m + 10m | Monthly acquisition & Annual annotation | Finland (2016-2021) |
| ABoVE | 2022 | Landsat | 30m | Annual acquisition & annotation | Boreal forests (1984-2020) |
Note
Here we list a few forecasting datasets, particularly for weather forecasting, but this list is by no means exhaustive. More weather forecasting datasets are listed here.
| Dataset name | Year | Image source | Spatial resolution | Temporal resolution | Number of classes | Acquisition |
|---|
| GreenEarthNet | 2024 | Sentinel-2 + meteorological observations | 20m | Weekly (S2) + daily | / | Europe (2017-2022) |
| SeasFire | 2023 | ERA5, MODIS, ... | 27km | 8d | / | Global (2001-2021) |
| Digital Typhoon | 2023 | Himawari | 5km | 60min | / | Western North Pacific basin (1978-2022) |
| SEN2DWATER | 2023 | Sentinel-2 | 10m | Every 2 months | / | Italy & Spain (2020-2022) |
| EarthNet2021 | 2021 | Sentinel-2 + mesodynamic models | 20m + 1,28km | Weekly (S2) + daily | / | Europe (2016-2020) |
| CloudCast | 2021 | Meteosat Second Generation | 3km | 15min | 11 | Europe (2017-2018) |
| MeteoNet | 2020 | Ground station observations, satellite images, rain radar observations, weather forecasting models and land-sea and relief masks | Variable | Variable | / | France (2016-2018) |
| SEVIR | 2020 | GOES-16 + NEXRAD | 2km + 1km | 5min | / | USA (2017-2019) |
| Dataset name | Year | Image source | Spatial resolution | Temporal resolution | Number of classes | Acquisition |
|---|
| TMS | 2024 | Jilin-1 + SkySat + Synthetic | 1m | 1 frame per second | 1 | Cities |
| AIR-MOT | 2022 | Jilin-1 | 1m | 5 to 10 frame per second | 2 | Cities |
| VISO | 2021 | Jilin-1 | 1m | 10 frame per second | 4 | Cities |
| SatSOT | 2021 | Jilin-1 + SkySat + Carbonite-2 | 1m | 10 to 25 frame per second | 4 | Cities |
| Dataset name | Year | Task | Image source | Spatial resolution | Temporal resolution | Acquisition |
|---|
| SSL4EO-S12 v1.1 | 2025 | Pre-training task | Sentinel-1 + Sentinel-2 | 10m + 10m | Seasonally acquisition | Global (2019-2021) |
| BreizhSR | 2024 | Super-resolution | Sentinel-2 + SPOT-6/7 | 10m + 2.5m | Weekly (S2) acquisition | Brittany France (2018) |
| SSL4EO-L | 2023 | Pre-training task | LandSat-4,5,7,8,9 | 30m | Seasonally acquisition | Global (2001-2002 + 2009-2010 + 2021-2022) |
| SSL4EO-S12 | 2023 | Pre-training task | Sentinel-1 + Sentinel-2 | 5m + 10m | Seasonally acquisition | Global (2021) |
| SAT-MTB | 2023 | Detection, segmentation and object tracking | Jilin-1 | 1m | 10 frame per second | Cities |
| TimeMatch | 2022 | Domain adaptation | Sentinel-2 | 10m | Weekly acquisition | Austria, Denmark, mid-west France, southern France (2017) |
| WorldStrat | 2022 | Super-resolution | Spot-6 + Spot-7 + Sentinel-2 | 1,5m + 1,5m + 10m | Weekly (S2) acquisition | Global (2017-2019) |
| Jilin-189 | 2022 | Video super-resolution | Jilin-1 | 1m | 25 frame per second | Cities |
| SEN12MS-CR-TS | 2022 | Cloud removal | Sentinel-1 + Sentinel-2 | 10m + 10m | Bi-monthly (S1) + weekly (S2) acquisition | Global (2018) |
| NASA Harvest | 2022 | Field Boundary Detection | PlanetScope | 3.7m | Monthly acquisition & Time-independant annotation | Rwanda (2021) |
| AI4Boundaries | 2022 | Field boundary detection | Sentinel-2 + aerial ortho-photo | 10m + 1m | Monthly acquisition & Yearly annotation | Europe (2019) |
| Seasonal Contrast | 2021 | Pre-training task | Sentinel-2 | 10m | Seasonally acquisition | Global (?) |
| PROBA-V Super-Resolution | 2019 | Super-resolution | PROBA-V | 300m + 100m | Daily acquisition | Global (?) |
The authors thank the French spatial agency (CNES) and the Brittany region for their financial support.
1Université Bretagne Sud, IRISA, UMR CNRS 6074, Vannes, France
2Centre National d’Études Spatiales (CNES), Toulouse, France
If you use this work, consider citing it as below.
@misc{dufourg2023sitsdatasets,
author = {Dufourg, Corentin and Pelletier, Charlotte and May, Stéphane and Lefèvre, Sébastien},
title = {Satellite Image Time Series Datasets},
url = {https://github.com/corentin-dfg/Satellite-Image-Time-Series-Datasets},
year = {2023}
}