ASF-Net: Adaptive Screening Feature Network for Building Footprint Extraction From Remote-Sensing Images

Building footprint extraction plays an important role in many remote-sensing (RS) applications such as urban planning and disaster monitoring. Mainly, the exploitation of contextual information in a fixed receptive field is the focus of previous research, which makes it difficult to generically extract buildings that vary greatly in size and shape, especially when isolated large buildings are surrounded by dense small buildings. To improve this problem, we attempt to teach the network to adjust the receptive field and enhance useful feature information adaptively. In this article, we propose a novel adaptive screening feature network (ASF-Net), which can independently screen and enhance effective feature information from two aspects. On the one hand, we propose a deepened space up-sampling block to screen useful information and help establish boundaries. On the other hand, we propose an Adaptive Information Utilization Block (AIUB) to enlarge the receptive field of feature maps and refine the incomplete building footprint. As a result, the more accurate multiscale building footprint is inferred from the enhanced features. Experimental results on the popular aerial image segmentation datasets show that ASF-Net obtains competitive results [80.2% intersection over union (IoU) on the Inria aerial image labeling dataset and 74.2% IoU on the Massachusetts buildings dataset] in comparison with several state-of-the-art models. The TensorFlow implementation is available at https://github.com/jyx0516/ASF-Net.