Automatic Detection of Specific Constructions on a Large Scale Using Deep Learning in Very High Resolution Airborne Imagery

Abstract

In the High Modernism period, from around 1914 to 1970, many system halls in steel construction were manufactured to meet the increasing demand in industry, commerce, and agriculture, among other areas. However, these types of buildings have not been the focus of any research in the field of construction history, generating a lack of knowledge regarding their construction types, distribution, and related context to enable statements on the ability and worthiness of historical monument listings. This paper proposes a methodology for the automatic detection of these buildings using aerial imagery. For this purpose, Deep Learning techniques for two tasks are evaluated: semantic segmentation and object detection. Different state-of-the-art software architectures are extensively reviewed and assessed through a series of experiments to determine which features and hyper-parameters produce the best results. Based on our experiments, the height information from nDSM improved the results by refining the detections and reducing the number of false negatives and false positives. Moreover, the Focal Loss helped boost the detections by tuning its hyper-parameter gamma, where object detection algorithms showed high sensitivity to this value. Semantic segmentation models outperformed their counterparts for object detection, with U-Net and EfficientNet B3 as the backbone, the one with the best results with a Detection Rate of up to 93%.