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Land cover classification (LCC), and monitoring how land use changes overtime, is an important process in climate change mitigation and adaptation.Existing approaches that use machine learning with Earth observation data forLCC rely on fully-annotated and segmented datasets. Creating these datasetsrequires a large amount of effort, and a lack of suitable datasets has becomean obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patchmodels: an alternative LCC approach utilising Multiple Instance Learning (MIL)that requires only high-level scene labels. This enables much fasterdevelopment of new datasets whilst still providing segmentation throughpatch-level predictions, ultimately increasing the accessibility of using LCCfor different scenarios. On the DeepGlobe-LCC dataset, our approach outperformsnon-MIL baselines on both scene- and patch-level prediction. This work providesthe foundation for expanding the use of LCC in climate change mitigationmethods for technology, government, and academia.

Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification