FwSVM-Net: A novel deep learning-based automatic building extraction from aerial images

Abstract

The building layer, one of the fundamental elements of mapping, features various layouts and architectures and is used in many mapping activities, such as urban planning, property management, illegal building detection, and disaster investigation. Therefore, accessing building information from remotely sensed images in a fast and automated manner is a popular topic. This paper aims to design a new Convolutional Neural Network (CNN) architecture called FwSVM-Net (Fast with Support Vector Machine Network) for building extraction. The FwSVM-Net, with 16 convolutional layers, employs a fusion mechanism to reduce the semantic gap between the encoder and decoder sections. In the architecture's classification layer, a Support Vector Machine (SVM) is used to increase segmentation accuracy while reducing parameter density. The Massachusetts Building Dataset was used for the automatic building extraction from aerial images. The performance of the FwSVM-Net on the test data was calculated at 97 %, 91 %, 87 %, 89 %, and 86 % for Overall Accuracy (OA), Precision (Pr), Recall (Rc), F1-Score (F1), and Intersection over Union (IoU), respectively. FwSVM-Net shows approximately ±2 % similarity in accuracy with the U-Net architecture, which is trained and evaluated with the same dataset. Despite this, the fact that the FwSVM-Net completed the training process approximately twice as fast as the U-Net architecture provides a significant time advantage. As a result, it is predicted that the FwSVM-Net will offer speed and convenience in terms of usability because of its performance, which is equivalent to the performance of the U-Net architecture for segmentation, and its time advantage.