CaBuAr: California Burned Areas dataset for delineation
Daniele Rege Cambrin Luca Colomba Paolo Garza
Forest wildfires represent one of the catastrophic events that, over the last
decades, caused huge environmental and humanitarian damages. In addition to a
significant amount of carbon dioxide emission, they are a source of risk to
society in both short-term (e.g., temporary city evacuation due to fire) and
long-term (e.g., higher risks of landslides) cases. Consequently, the
availability of tools to support local authorities in automatically identifying
burned areas plays an important role in the continuous monitoring requirement
to alleviate the aftereffects of such catastrophic events. The great
availability of satellite acquisitions coupled with computer vision techniques
represents an important step in developing such tools. This paper introduces a
novel open dataset that tackles the burned area delineation problem, a binary
segmentation problem applied to satellite imagery. The presented resource
consists of pre- and post-fire Sentinel-2 L2A acquisitions of California forest
fires that took place starting in 2015. Raster annotations were generated from
the data released by California's Department of Forestry and Fire Protection.
Moreover, in conjunction with the dataset, we release three different baselines
based on spectral indexes analyses, SegFormer, and U-Net models.