JointNet: A Common Neural Network for Road and Building Extraction

Abstract

Automatic extraction of ground objects is fundamental for many applications of remote sensing. It is valuable to extract different kinds of ground objects effectively by using a general method. We propose such a method, JointNet, which is a novel neural network to meet extraction requirements for both roads and buildings. The proposed method makes three contributions to road and building extraction: (1) in addition to the accurate extraction of small objects, it can extract large objects with a wide receptive field. By switching the loss function, the network can effectively extract multi-type ground objects, from road centerlines to large-scale buildings. (2) This network module combines the dense connectivity with the atrous convolution layers, maintaining the efficiency of the dense connection connectivity pattern and reaching a large receptive field. (3) The proposed method utilizes the focal loss function to improve road extraction. The proposed method is designed to be effective on both road and building extraction tasks. Experimental results on three datasets verified the effectiveness of JointNet in information extraction of road and building objects.