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Global land use / land cover with Sentinel 2 and deep learning
Abstract
Land use/land cover (LULC) maps are foundational geospatial data products needed by analysts and decision makers across governments, civil society, industry, and finance to monitor global environmental change and measure risk to sustainable livelihoods and development. There is a strong need for high-level, automated geospatial analysis products that turn these pixels into actionable insights for non-geospatial experts. The Sentinel 2 satellites, first launched in mid-2015, are excellent candidates for LULC mapping due to their high spatial, spectral, and temporal resolution. Advances in deep learning and scalable cloud-based compute now provide the analysis capability required to unlock the value in global satellite imagery observations. Based on a novel, very large dataset of over 5 billion human-labeled Sentinel-2 pixels, we developed and deployed a deep learning segmentation model on Sentinel-2 data to create a global LULC map at 10m resolution that achieves state-of-the-art accuracy and enables automated LULC mapping from time series observations.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Cited by
Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., & Singh, G. (2022) · Remote Sensing · doi:10.3390/rs14164101 · Cited by 380
Guo, X., Lao, J., Dang, B., Zhang, Y., Yu, L., Ru, L., Zhong, L., Huang, Z., Wu, K., Hu, D., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02613 · Cited by 290
Tanalgo, K. C., Manampan-Rubio, M., Alvaro-Ele, R. J., Hilario-Husain, B. A., Murray, S. A., Delos Reyes, J. L., Pangato, N. M., Magkidong, N. S., Angcaco, K. L. D., Catulos, A. J., et al. (2024) · Discover Environment · doi:10.1007/s44274-024-00142-1 · Cited by 3
Metadata
- Authors
- Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., & Brumby, S. P.
- Venue
- 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
- Publisher
- IEEE
- Published
- 2021-07-11
- Pages
- 4704-4707
- OpenAlex
- W3207200417
- Cited by
- 1,328
- References
- 1
- FWCI
- 294.53
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., & Brumby, S. P. (2021). Global land use / land cover with Sentinel 2 and deep learning. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. https://doi.org/10.1109/igarss47720.2021.9553499
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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