Using transfer learning to study burned area dynamics: A case study of refugee settlements in West Nile, Northern Uganda
This work addresses the problem of assessing environmental impacts of refugee settlements for humanitarian and ecological stakeholders, but it is incremental as it adapts existing methods to a new domain.
The study tackled the lack of burned area ground-truth data in sub-Saharan Africa by applying a transfer learning approach, training a deep learning model on data from Portugal and using it to analyze burned area dynamics in refugee settlements in Northern Uganda from 2015 to 2020, though no concrete numerical results are provided.
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.