  With the global refugee crisis at a historic high, there is a growing need to
assess the impact of refugee settlements on their hosting countries and
surrounding environments. Because fires are an important land management
practice in smallholder agriculture in sub-Saharan Africa, burned area (BA)
mappings can help provide information about the impacts of land management
practices on local environments. However, a lack of BA ground-truth data in
much of sub-Saharan Africa limits the use of highly scalable deep learning (DL)
techniques for such BA mappings. In this work, we propose a scalable transfer
learning approach to study BA dynamics in areas with little to no ground-truth
data such as the West Nile region in Northern Uganda. We train a deep learning
model on BA ground-truth data in Portugal and propose the application of that
model on refugee-hosting districts in West Nile between 2015 and 2020. By
comparing the district-level BA dynamic with the wider West Nile region, we aim
to add understanding of the land management impacts of refugee settlements on
their surrounding environments.
