Meta-learning to address diverse Earth observation problems across resolutions
Abstract Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth observation problems across differ...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-023-01146-0 |
| Summary: | Abstract Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth observation problems across different resolutions. METEOR is an adaptive deep meta-learning model with several modifications that allow it to ingest images with a variable number of spectral channels and to predict a varying number of classes per downstream task. It uses knowledge mined from land cover information worldwide to adapt to new unseen target problems with few training examples. METEOR outperforms competing self-supervised approaches on five downstream tasks, showing its relevance to addressing novel and impactful geospatial problems with only a handful of labels. |
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| ISSN: | 2662-4435 |