Automatic Detection of Tiny Drainage Outlets and Ventilations on Flat Rooftops from Aerial Imagery

Abstract. Flat rooftops on residential and industrial buildings house critical drainage and ventilation systems, which play essential roles in channeling water away from structures and preventing moisture accumulation. These utilities are vital for maintaining the structural integrity of rooftops, safeguarding against water pooling and moisture buildup that could otherwise lead to damage or even collapse, particularly during extreme weather events. However, current inspection and maintenance practices for these systems are predominantly manual, making them time-consuming, labor-intensive, and sometimes hazardous. This paper presents an automated approach to detecting drainage outlets and ventilation systems on flat rooftops, using a custom-labeled dataset of highresolution aerial imagery. We evaluated two different object detection methods, with FCOS (Fully Convolutional One-Stage Object Detection) outperforming Faster R-CNN in identifying these small utilities. The outcomes pave the way for new applications, as detected utilities can act as sparse data points that trigger constraint-based reasoning processes for estimating hidden utility networks in as-built Building Information Modeling (BIM) contexts. Embedding these identified objects into GIS or BIM models represents an initial step towards coarse-to-fine visual recognition, enabling customized semantic mission planning for autonomous exploration and inspection using Unmanned Aerial Vehicles (UAVs). The labeled dataset used in this study is publicly available by following this link https://zenodo.org/records/14040571.