  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.
