Robust Burned Area Delineation through Multitask Learning
Edoardo Arnaudo Luca Barco Matteo Merlo Claudio Rossi
In recent years, wildfires have posed a significant challenge due to their
increasing frequency and severity. For this reason, accurate delineation of
burned areas is crucial for environmental monitoring and post-fire assessment.
However, traditional approaches relying on binary segmentation models often
struggle to achieve robust and accurate results, especially when trained from
scratch, due to limited resources and the inherent imbalance of this
segmentation task. We propose to address these limitations in two ways: first,
we construct an ad-hoc dataset to cope with the limited resources, combining
information from Sentinel-2 feeds with Copernicus activations and other data
sources. In this dataset, we provide annotations for multiple tasks, including
burned area delineation and land cover segmentation. Second, we propose a
multitask learning framework that incorporates land cover classification as an
auxiliary task to enhance the robustness and performance of the burned area
segmentation models. We compare the performance of different models, including
UPerNet and SegFormer, demonstrating the effectiveness of our approach in
comparison to standard binary segmentation.