@article { , title = {SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction}, abstract = {In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for the statistical work of a building area. Recent research efforts have been devoted to semantic segmentation using deep learning approaches, which can be further divided into two aspects. In this paper, we propose a single-side dual-branch network (SSDBN) based on an encoder–decoder structure, where an improved Res2Net model is used at the encoder stage to extract the basic feature information of prepared images while a dual-branch module is deployed at the decoder stage. An intermediate framework was designed using a new feature information fusion methods to capture more semantic information in a small area. The dual-branch decoding module contains a deconvolution branch and a feature enhancement branch, which are responsible for capturing multi-scale information and enhancing high-level semantic details, respectively. All experiments were conducted using the Massachusetts Buildings Dataset and WHU Satellite Dataset I (global cities). The proposed model showed better performance than other recent approaches, achieving an F1-score of 87.69\% and an IoU of 75.83\% with a low network size volume (5.11 M), internal parameters (19.8 MB), and GFLOPs (22.54), on the Massachusetts Buildings Dataset.}, doi = {10.3390/rs14030768}, issue = {3}, journal = {Remote Sensing}, publicationstatus = {Published}, publisher = {MDPI}, url = {http://researchrepository.napier.ac.uk/Output/2844958}, volume = {14}, keyword = {building extraction, dual-branch, semantic segmentation, encoder–decoder network}, year = {2022}, author = {Li, Yang and Lu, Hui and Liu, Qi and Zhang, Yonghong and Liu, Xiaodong} }