Amodal Segmentation Considering Visible and Non-Visible Elements of Urban Surfaces

Abstract

This study addresses the challenge of amodal segmentation in computer vision, a change in basic assumptions towards perceiving objects holistically, even when partially occluded, deviating from the traditional modal perspective that predominantly focuses on visible elements. Thus, we propose a new approach for the amodal segmentation of top-view aerial images, with particular attention to the first layer of elements, constituted by asphalt and natural soils, normally occluded by different objects (trees, buildings, and vehicles). This proposed methodology is data-centric, assigning weights to specific image sections and distinguishing non-visible elements. The best model used the U-Net architecture with Efficient-net-B7 as the backbone and can accurately classify occluded segments, achieving an Intersection over Union (IoU) greater than 80% for most classes. The developed method provides a basis for exploring amodal segmentation based on data-centric models, impacting our understanding of complex and occlusion-prone environments, such as urban environments.