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Scene-to-Patch earth observation: multiple instance learning for land cover classification

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
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]

Record 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.

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2211.08247v1
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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
ORCID for Joseph Early: ORCID iD orcid.org/0000-0001-7748-9340
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 20 Dec 2022 17:36
Last modified: 07 Jun 2024 01:57

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Contributors

Author: Joseph Early ORCID iD
Author: Ying-Jung Deweese
Author: Christine Evers ORCID iD
Author: Sarvapali Ramchurn ORCID iD

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