Abstract
Building footprint generation is a vital task in a wide range of applications, including, to name a few, land use management, urban planning and monitoring, and geographical database updating. Most existing approaches addressing this problem fall back on convolutional neural networks (CNNs) to learn semantic masks of buildings. However, one limitation of their results is blurred building boundaries. To address this, we propose to learn attraction field representation for building boundaries, which is capable of providing an enhanced representation power. Our method comprises two elemental modules: an Img2AFM module and an AFM2Mask module. More specifically, the former aims at learning an attraction field representation conditioned on an input image, which is capable of enhancing building boundaries and suppressing the background. The latter module predicts segmentation masks of buildings using the learned attraction field map. The proposed method is evaluated on three datasets with different spatial resolutions: the ISPRS dataset, the INRIA dataset, and the Planet dataset. From experimental results, we find that the proposed framework can well preserve geometric shapes and sharp boundaries of buildings, which brings significant improvements over other competitors. The trained model and code are available at https://github.com/lqycrystal/AFM_building.
Highlights
A UTOMATIC building footprint generation from remote sensing data has been of great interest in the community for a range of applications, such as land use management, urban planning and monitoring, and disaster management
Compared with naive semantic segmentation networks and networks with other visual cues, our method can significantly improve accuracies in terms of both semantic mask and boundary
In [26], an adversarial training strategy is proposed for building extraction from remote sensing imagery, and FC-DenseNet is exploited as a base semantic segmentation network to generate accurate building footprints
Summary
A UTOMATIC building footprint generation from remote sensing data has been of great interest in the community for a range of applications, such as land use management, urban planning and monitoring, and disaster management. Motivated by this observation, in this work, we want to make use of the attraction field to represent buildings and propose an end-toend trainable network for automatic building footprint generation. In this work, we want to make use of the attraction field to represent buildings and propose an end-toend trainable network for automatic building footprint generation This network consists of two modules: Img2AFM and AFM2Mask. 1) We propose to use the boundary-aware attraction field to represent building footprints in remote sensing images. This helps to enhance building boundaries while suppressing the impact of background clutters. To the best of our knowledge, it is the first work that utilizes the attraction field for the task of building footprint generation. 2) We propose a novel network that first learns an AFM by a subnetwork, termed Img2AFM, and uses another subnetwork called AFM2Mask to reconstruct
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