Publications / 2024 Proceedings of the 41st ISARC, Lille, France
Best practice for the detection and annotation of visible defects in slated roofs is by annotation of photos, ideally orthophotos. If such a process is to be effectively automated in support of emerging Digital Twinning solutions, it is necessary to first recognise the external sub-components of the roof in the orthophotos, in particular the slated and leadwork areas. Using a dataset composed of many photos from two historic buildings, this study develops and compares different deep-learning -based semantic segmentation models to segment roof orthophotos into slated areas, leadwork, and `other' areas. Since orthophotos typically contain pixels which do not belong to the roof panel (black `background' pixels), the method employs a subsequent `background' label correction step. The best-performing model is found to be PointRend with Focal Loss: overall aAcc = 99, mIoU = 88.91, and mAcc = 92.77; for slate class, IoU and Acc is nearly 100; for leadwork class, IoU and Acc is around 90.