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satellite-image-time-series-datasets

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.

https://github.com/corentin-dfg/satellite-image-time-series-datasets

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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.

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README.md

Satellite Image Time Series Datasets

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.

Table of Contents

Semantic and Instance Segmentation

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.

Pixel annotations for each image

| 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) |

Pixel annotations for each time series

| 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) |

Polygon annotations for each image

| 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) |

Polygon annotations for each time series

| 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 | Cloud-free fusion of images from various satellites | 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) |

Image-level annotations

| 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) |

Datacube-level annotations

| 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) |

Regression

| 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) |

Forecasting

[!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)

Object tracking

| 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 |

Other tasks

| 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 (?) |

Citation

The authors thank the French spatial agency (CNES) and the Brittany region for their financial support. - Corentin Dufourg1 - Dr. Charlotte Pelletier1 - Stéphane May2 - Pr. Sébastien Lefèvre1

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.

latex @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} }

Owner

  • Name: Corentin Dufourg
  • Login: corentin-dfg
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Citation (CITATION.cff) 

cff-version: 1.2.0
title: Satellite Image Time Series Datasets
message: 'If you use this work, consider citing it as below.'
type: generic
authors:
  - given-names: Corentin
    family-names: Dufourg
    affiliation: >-
      Université Bretagne Sud, IRISA, UMR CNRS 6074, Vannes,
      France
  - given-names: Charlotte
    family-names: Pelletier
    affiliation: >-
      Université Bretagne Sud, IRISA, UMR CNRS 6074, Vannes,
      France
  - given-names: Stéphane
    family-names: May
    affiliation: >-
      Centre National d’Études Spatiales (CNES), Toulouse,
      France
  - given-names: Sébastien
    family-names: Lefèvre
    affiliation: >-
      Université Bretagne Sud, IRISA, UMR CNRS 6074, Vannes,
      France
url: >-
  https://github.com/corentin-dfg/Satellite-Image-Time-Series-Datasets

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