fmenat / sits_s2coverage

Public repository of our work in spatio-temporal coverage based on Sentinel-2 Satellite Images Time Series (SITS)

SITS_S2Coverage

Sentinel-2 Coverage on Satellite Images Time Series (SITS).

Source: https://sentinel.esa.int/web/sentinel/missions/sentinel-2

Based on Scene Classification Layer (SCL)

Label	Classification
0	NO_DATA
1	SATURATED_OR_DEFECTIVE
2	DARK_AREA_PIXELS
3	CLOUD_SHADOWS
4	VEGETATION
5	NOT_VEGETATED
6	WATER
7	UNCLASSIFIED
8	CLOUD_MEDIUM_PROBABILITY
9	CLOUD_HIGH_PROBABILITY
10	THIN_CIRRUS
11	SNOW

AI4EO - Enhanced Agriculture challenge

The task is to provide a cultivated or not map (binary classification) at a higher resolution (2.5m) than the input Sentinel-2 SITS (10m). The data belongs to Slovenia country.

• Data: https://platform.ai4eo.eu/enhanced-sentinel2-agriculture-permanent/data
• Sources: https://github.com/AI4EO/enhanced-sentinel2-agriculture-challenge
• Baseline used: Tarasiewicz, T., Tulczyjew, L., Myller, M., Kawulok, M., Longépé, N., & Nalepa, J. (2022, July). Extracting High-Resolution Cultivated Land Maps from Sentinel-2 Image Series. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 175-178). IEEE. DOI: 10.1109/IGARSS46834.2022.9883919

Product generated

• Prediction Performance by Patch: ml_results/ai4eo
• Outcome (Coverage result): coverage/ai4eo

sample from assesment_spat_70_temp_70_sel_0405.csv:

filename	num_timesteps	num_timesteps_missing	avg_spatial_coverage	num_timesteps_abovecov	temporal_coverage	assesment_temporal	assesment_spatial
eopatch-841	38	38	81.87	30	78.95	high	high
eopatch-781	38	38	73.21	23	60.53	low	high
eopatch-718	38	38	61.21	18	47.37	low	low
...	...	...	...	...	...	...	...

LandCoverNet - Europe

The task is to provide a land-cover map (a classification based on 7 classes) at 10m resolution. The data is global but distributed in different regions where we executed the assessment: Africa, Asia, Australia, Europe, North America, and South America.

• Data source: https://mlhub.earth/data/ref_landcovernet_eu_v1
• Reference: Alemohammad, H., & Booth, K. (2020). LandCoverNet: A global benchmark land cover classification training dataset. arXiv preprint arXiv:2012.03111.

Product generated

• Outcome in Africa (Coverage result): coverage/landcovernet_af
• Outcome in Asia (Coverage result): coverage/landcovernet_as
• Outcome in Australia (Coverage result): coverage/landcovernet_au
• Outcome in Europe (Coverage result): coverage/landcovernet_eu
• Outcome in North America (Coverage result): coverage/landcovernet_na
• Outcome in South America (Coverage result): coverage/landcovernet_sa

Execution example

For examples on execution go to src/README.md

Authors and acknowledgment

Cristhian Sanchez and Francisco Mena.

🖊️ Citation

Sanchez, C., et al. "Assessment of Sentinel-2 Spatial and Temporal Coverage based on the Scene Classification Layer." IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2024.

@inproceedings{sitscoverage2024,
  title = {Assessment of {Sentinel-2} spatial and temporal coverage based on the {Scene} {Classification} {Layer}},
  booktitle = {{IEEE International Geoscience} and {Remote Sensing Symposium} ({IGARSS})},
  author = {Sanchez, Cristhian and Mena, Francisco and Charfuelan, Marcela and Nuske, Marlon and Dengel, Andreas},
  year = {2024},
  publisher = {{IEEE}},
}

Licence

Copyright (C) 2022 authors of this github.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.