A Sparse Sharing Multitask Framework for Building Footprint Extraction From Remote Sensing Imagery Following the Dual Lottery Ticket Hypothesis

Building footprint extraction from high-resolution remote sensing imagery is significant for urban planning, change detection, disaster management, and other applications. Recently, researchers have found that the edge features of buildings are crucial in extracting building footprints, and multitask deep learning is used to share edge feature information. However, these multitask deep learning frameworks adopt a hard sharing approach, which cannot avoid the adverse effects caused by the differences between different tasks, resulting in the problem of blurred edges and building boundaries. To address this issue, this article proposes a dual lottery ticket hypothesis (DLTH) and sparse sharing-based multitask deep learning framework, dual sparse sharing architecture (DSSA), to transmit the edge information in the edge detection to the building footprint extraction by sharing partial parameters. First, the subnetworks of building footprint extraction and edge detection are constructed according to the sparse rate and parameter sharing rate to control the dependencies between the subnetworks. Second, given the difference in the importance of the two tasks, a cosine unequal-scaled alternating training strategy is proposed to strengthen and weaken the transmission of edge information periodically. Third, following the DLTH, the loss function with ${L}3$ /2 regularization constraint is used to promote the information transmission and parameter conversion of the subnetwork by using the global information. Finally, aiming at the edge of building footprint extraction results, a pixel-based evaluation index, edge extraction accuracy ( ${\mathrm {EEA}}^{(n)})$ , is designed by morphological erosion to better evaluate the integrity of the edge of building footprint extraction results. The experiments conducted on a self-annotated dataset and two public datasets (i.e., WHU Aerial Imagery dataset and Massachusetts Building dataset) show that DSSA can achieve better edge effects than the baseline and show excellent generalization ability.