INSTANCE SEGMENTATION OF LIDAR DATA WITH VISION TRANSFORMER MODEL IN SUPPORT INUNDATION MAPPING UNDER FOREST CANOPY ENVIRONMENT

Abstract. Inundation mapping in forest and dense vegetated areas requires the ability to generate well defined Digital Terrain Models (DTM) to derive floodwater extent, depth, and duration. Due to the occlusion caused by overlapping leaves and branch structures of forest canopies, the ability to extract elevation point clouds through UAV and airborne optical imagery and photogrammetry is challenging. LiDAR is an active sensor that acquires direct 3D measurements by transmitting hundreds of thousands of laser measurements per second producing incredibly detailed mapping layers of not only the terrain but also forest attributes such as crown diameter, tree density and height that can support inundation mapping as well as hydrological models and monitoring of floods.
In this research, we propose a methodology to map the inundated areas under canopies by using photon base Geiger Mode LiDAR point cloud dataset and a deep learning model to conduct instance segmentation of tree canopy. The method is to segment the vegetation from water and determine the gap fraction between trees to quantify the penetration through canopy for the detection of water pixels in vegetated areas. To conduct the segmentation Masked-attention Mask Transformer (Mask2Former) for universal segmentation model was implemented and trained to automate the extraction of tree crown segments from the LiDAR data. Furthermore, a semi-automatic experimental approach using a Canopy Height Model and watershed segmentation was applied to develop a rapid tree crown annotation strategy.