GeoSay: A geometric saliency for extracting buildings in remote sensing images

Xia, Gui-Song; Huang, Jin; Xue, Nan; Lu, Qikai; Zhu, Xiao Xiang

doi:10.1016/j.cviu.2019.06.001

Cited by 17 publications

(6 citation statements)

References 28 publications

(44 reference statements)

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“…Buildings are one of the most significant elements in urban landscapes and are highly dynamic [1]. Automatic extraction of buildings is a long-standing problem [2][3][4][5][6][7] in urban scene classification, land use analysis, and automated map updating. The research related to building extractions can be broadly categorized as "building region detection" and "building edge detection".…”

Section: Introductionmentioning

confidence: 99%

ME-Net: A Multi-Scale Erosion Network for Crisp Building Edge Detection from Very High Resolution Remote Sensing Imagery

Wen

Zhang

et al. 2021

Remote Sensing

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The detection of building edges from very high resolution (VHR) remote sensing imagery is essential to various geo-related applications, including surveying and mapping, urban management, etc. Recently, the rapid development of deep convolutional neural networks (DCNNs) has achieved remarkable progress in edge detection; however, there has always been the problem of edge thickness due to the large receptive field of DCNNs. In this paper, we proposed a multi-scale erosion network (ME-Net) for building edge detection to crisp the building edge through two innovative approaches: (1) embedding an erosion module (EM) in the network to crisp the edge and (2) adding the Dice coefficient and local cross entropy of edge neighbors into the loss function to increase its sensitivity to the receptive field. In addition, a new metric, Ene, to measure the crispness of the predicted building edge was proposed. The experiment results show that ME-Net not only detects the clearest and crispest building edges, but also achieves the best OA of 98.75%, 95.00% and 95.51% on three building edge datasets, and exceeds other edge detection networks 3.17% and 0.44% at least in strict F1-score and Ene. In a word, the proposed ME-Net is an effective and practical approach for detecting crisp building edges from VHR remote sensing imagery.

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Section: Introductionmentioning

confidence: 99%

ME-Net: A Multi-Scale Erosion Network for Crisp Building Edge Detection from Very High Resolution Remote Sensing Imagery

Wen

Zhang

et al. 2021

Remote Sensing

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“…This information is generally collected through various censuses and surveys or their fusion, requiring costly, expansive, and time-consuming efforts. One way to tackle this challenge is to make the process cost-effective and labor-saving by using deep learning-based methods on satellite images to get an automatic estimate of building counts [34] or through the extraction of buildings [47] .…”

Section: Introductionmentioning

confidence: 99%

Cross-Region Building Counting in Satellite Imagery using Counting Consistency

Zakria¹,

Rawal²,

Sultani³

et al. 2021

Preprint

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Estimating the number of buildings in any geographical region is a vital component of urban analysis, disaster management, and public policy decision. Deep learning methods for building localization and counting in satellite imagery, can serve as a viable and cheap alternative. However, these algorithms suffer performance degradation when applied to the regions on which they have not been trained. Current large datasets mostly cover the developed regions and collecting such datasets for every region is a costly, time-consuming, and difficult endeavor. In this paper, we propose an unsupervised domain adaptation method for counting buildings where we use a labeled source domain (developed regions) and adapt the trained model on an unlabeled target domain (developing regions). We initially align distribution maps across domains by aligning the output space distribution through adversarial loss. We then exploit counting consistency constraints, within-image count consistency, and across-image count consistency, to decrease the domain shift. Within-image consistency enforces that building count in the whole image should be greater than or equal to count in any of its sub-image. Across-image consistency constraint enforces that if an image contains considerably more buildings than the other image, then their sub-images shall also have the same order. These two constraints encourage the behavior to be consistent across and within the images, regardless of the scale. To evaluate the performance of our proposed approach, we collected and annotated a large-scale dataset consisting of challenging South Asian regions having higher building densities and irregular structures as compared to existing datasets. We perform extensive experiments to verify the efficacy of our approach

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“…Extracting building outline is one of the primary topics of unmanned aerial vehicle (UAV) remote sensing image segmentation (Niethammer et al., 2011; L. Wang et al., 2019; Xiang & Tian, 2011), which is important for urban planning, emergency rescue, and traffic management (Griffiths & Boehm, 2019; Guo et al., 2010; Nielsen, 2015; Xia et al., 2019). Building segmentation deciding the accuracy of boundary extraction is an important step in building extraction.…”

Section: Introductionmentioning

confidence: 99%