FUELFUSION - Fusion of multi-scale and multi-sensor remote sensing for fuel type classification

Context and objectives

Wildfires have significant socio-economic and ecological impacts, posing risks to human lives, buildings, ecosystem services and biodiversity values. Climate change is expanding fire‑prone zones northward However, integrated risk assessment systems for wildfires are still lacking in many industrialized countries. This project proposal aims to facilitate bridging this gap by focusing on the accurate mapping of wildland fuel types in Belgium using diverse remote sensing techniques, exploiting the long experience obtained from similar ecosystems in Greece. Wildland fuels are a prerequisite for any type of wildfire risk assessment framework, since they represent the biomass available for burning under the current meteorological conditions. Traditionally, fuel types have been estimated through time consuming field observations, but recent advancements in satellite remote sensing and UAV (unoccupied aerial vehicle) technology in combination with multi-scale deep learning offer new opportunities. In particular, the design and implementation of novel methodologies for mapping fuel types based on the fusion of 3D point clouds and spectral data derived from UAVs – which is a research area that only recently has started being extensively investigated – offers great potential. This project specifically aims to exploit the fusion and harmonization of multi-scale remote sensing (including satellite, aerial, mobile and UAV), along with a multi-sensor (LiDAR, multispectral and RGB) approach. The hypothesis is that this fusion in combination with novel, multi-scale deep learning algorithms (including multi-scale convolutional neural networks) can support the accurate mapping of wildland fuels.