A CNN-Based Approach for Automatic Building Detection and Recognition of Roof Types Using a Single Aerial Image

Automatic detection and reconstruction of buildings have become essential in many remote sensing and computer vision applications. In this paper, the capability of Convolutional Neural Networks (CNNs) is investigated for building detection as well as recognition of roof shapes using a single image. The major steps are including training dataset generation, model training, image segmentation, building detection and roof shape recognition. First, a CNN is trained for extracting urban objects such as trees, roads and buildings. Next, classification of different roof types into flat, gable and hip shapes is performed using the second trained CNN. The assessment results prove effectiveness of the proposed method with approximately 97% and 92% of quality rates in detection and recognition steps, respectively.