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 |
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 |
| ... | ... | ... | ... | ... | ... | ... | ... |
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
For examples on execution go to src/README.md
Cristhian Sanchez and Francisco Mena.
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}},
}Copyright (C) 2022 authors of this github.
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You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.