Accurate Detection of Historical Buildings Using Aerial Photographs and Deep Transfer Learning

Abstract

Deep learning (DL) has increasingly witnessed a lot of applications and advancements in remote sensing (RS). However, it remains unclear whether it can accurately detect historical buildings in RS imagery. Here we proposed a new deep transfer learning approach based on aerial photographs to automatically detect Hakka Weilong Houses (HWHs), a famous type of historical residence and an important cultural symbol of Hakka, a Han Chinese subgroup across the world. An RS image dataset, namely Hakka Weilong House Image Dataset (HWHID), was created by using aerial photographs of the urban and suburban Meizhou, which is called the world Hakka capital. The dataset was randomly shuffled into training and testing ones with a ratio of 8:2. Our approach used ResNet50 as the backbone transfer network and YOLO v2 as a training framework. Experimental results showed that the average precision was $0.9599\pm 0.0150$ , the loss rate was 0.0250, the Root Mean Square Error (RMSE) for training was 0.1580, and the average detecting time per image clip was $0.0383\pm 0.0150$ second, suggesting that our model has a high accuracy and an excellent performance for the HWH detection task. Our findings provide concrete evidences that aerial-imagery-based deep transfer learning can be used as a new archaeological RS method to detect historical buildings accurately and rapidly in aerial photographs.