-
, particularly in forested and densely vegetated environments. Airborne LiDAR have demonstrated strong potential to penetrate vegetation and reveal micro-topography, yet many challenges in data processing
-
conditions over long-duration missions, including intense radiation, large temperature variations, and prolonged exposure to harsh environments. This PhD project aims to design and discover next-generation
-
Change Adaptation (CCA) simulations and activities in urban environments. The PhD project should leverage multi-scale geospatial data (remote sensing/aerial imagery, point clouds, maps, etc.) and GeoAI
-
photogrammetry now enable the digitisation of spatial environments across urban, natural and indoor settings. However, raw 3D observations generally remain largely unstructured and difficult to directly exploit
-
. The final output is a low-power, field-deployable platform tailored for the agri-food sector, capable of detecting early-stage contamination or quality shifts directly in harsh production environments