  Monitoring wildfires is an essential step in minimizing their impact on the
planet, understanding the many negative environmental, economic, and social
consequences. Recent advances in remote sensing technology combined with the
increasing application of artificial intelligence methods have improved
real-time, high-resolution fire monitoring. This study explores two proposed
approaches based on the U-Net model for automating and optimizing the
burned-area mapping process. Denoted 128 and AllSizes (AS), they are trained on
datasets with a different class balance by cropping input images to different
sizes. They are then applied to Landsat imagery and time-series data from two
fire-prone regions in Chile. The results obtained after enhancement of model
performance by hyperparameter optimization demonstrate the effectiveness of
both approaches. Tests based on 195 representative images of the study area
show that increasing dataset balance using the AS model yields better
performance. More specifically, AS exhibited a Dice Coefficient (DC) of 0.93,
an Omission Error (OE) of 0.086, and a Commission Error (CE) of 0.045, while
the 128 model achieved a DC of 0.86, an OE of 0.12, and a CE of 0.12. These
findings should provide a basis for further development of scalable automatic
burned-area mapping tools.
