Artificial Intelligence Modeling of Forest Structure and Fuel Variables for Fire Behavior Simulation
3D structure and fuel prediction models based on remote sensing and LiDAR data
AIMFire project
Integrating advanced remote sensing technology and artificial intelligence to improve the prediction of forest fire behavior and forest management.
Global-LiDAR database
Compile, curate and acquire an exhaustive dataset of ground truth field data, point clouds and images for forest structure and LFMC modeling.
Forest structure modeling
Estimate detailed forest structure variables from point clouds (airborne (ALS), terrestrial (TLS), mobile (MLS), and drone-mounted (ULS)) using deep learning methods.
LFMC
Improve Life Fuel Moisture Content (LFMC) models from remote sensing and meteorological data using deep learning methods.
3D fuel distribution
Achieve a more realistic approach for the 3D distribution of canopy bulk density derived from LiDAR point clouds.
Fire behavior simulation
Integrate 3D bulk density derived from point clouds into physics-based fire behavior models and applying sensitivity análisis.
Research transfer and dissemination
Technology transfer, publications and interdisciplinary workshop to disseminate project results, engaging researchers, students, and society.