Scene-to-Patch earth observation: multiple instance learning for land cover classification
Scene-to-Patch earth observation: multiple instance learning for land cover classification
Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patch models: an alternative LCC approach utilising Multiple Instance Learning (MIL) that requires only high-level scene labels. This enables much faster development of new datasets whilst still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using LCC for different scenarios. On the DeepGlobe-LCC dataset, our approach outperforms non-MIL baselines on both scene- and patch-level prediction. This work provides the foundation for expanding the use of LCC in climate change mitigation methods for technology, government, and academia.
cs.CV, cs.LG
Early, Joseph
fd4e9e4c-9251-474d-a9cf-12157a9f2f73
Deweese, Ying-Jung
159bdbeb-3836-411c-b242-529c0e076601
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
15 November 2022
Early, Joseph
fd4e9e4c-9251-474d-a9cf-12157a9f2f73
Deweese, Ying-Jung
159bdbeb-3836-411c-b242-529c0e076601
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
[Unknown type: UNSPECIFIED]
Abstract
Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patch models: an alternative LCC approach utilising Multiple Instance Learning (MIL) that requires only high-level scene labels. This enables much faster development of new datasets whilst still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using LCC for different scenarios. On the DeepGlobe-LCC dataset, our approach outperforms non-MIL baselines on both scene- and patch-level prediction. This work provides the foundation for expanding the use of LCC in climate change mitigation methods for technology, government, and academia.
More information
Published date: 15 November 2022
Additional Information:
14 pages total (4 main content; 2 acknowledgments + citations; 8 appendices); 8 figures (2 main; 6 appendix); published at "Tackling Climate Change with Machine Learning: Workshop at NeurIPS 2022"
Keywords:
cs.CV, cs.LG
Identifiers
Local EPrints ID: 472853
URI: http://eprints.soton.ac.uk/id/eprint/472853
PURE UUID: 91444aa4-570a-4117-b0b7-8fb0efc6f82d
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Date deposited: 20 Dec 2022 17:36
Last modified: 07 Jun 2024 01:57
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