Building Extraction of Multi-source Data Based on Deep Learning

Yang, Le; Wang, Hui; Yan, Ke; Yu, Xiangzhou; Li, Jing; Man, Dan

doi:10.1109/icivc47709.2019.8980990

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…where, Decpl( )  denotes the feature decoupling process. Note that the DBEA module is embedded before feature map 1 y , 2 y and 3 y , respectively, and the CDA module is embedded before the final prediction. For more information, please refer to next Section.…”

Section: B Body-mask-edge Consistency Constraint Base Networkmentioning

confidence: 99%

“…As one of the main constituents of the resource environment, buildings have inevitably become the focus of researchers' attention [1]. Remote sensing imagery is an important means to obtain information on land features, and the extraction of buildings from high resolution remote sensing imagery is crucial for applications such as the unauthorized building monitoring, automatic extraction of urban areas, map updating, urban change monitoring, urban planning, 3D modelling and digital city establishment [2].…”

Section: Introductionmentioning

confidence: 99%

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MDBES-Net: Building Extraction From Remote Sensing Images Based on Multiscale Decoupled Body and Edge Supervision Network

Xu,

Du,

Meng

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The extraction of buildings in aerial remote sensing applications is an important and challenging task. Most existing methods extract buildings based on local area attention, ignoring the loss of accuracy due to the global structure of the building. However, global structural features of buildings with strong coupling relationships in complex scenes are difficult to extract, such as the edges and bodies of buildings, leading to discontinuous results. Therefore, Multiscale Decoupled Body and Edge Supervision Network (MDBES-Net), which can consider both edge optimization and inner consistency, is proposed to solve these problems. MDBES-Net consists of the Body-Mask-Edge Consistency Constraint base network (BMECC), Decoupling the Body and Edge Aware module (DBEA), and the Channel Decoupled Attention module (CDA). First, Body-Mask-Edge consistency constraint supervision is established by body and edge labels to jointly improve the segmentation effect in the BMECC base network. Second, In the mutiscale DBEA module, building features are warped by a learnable flow field to make body parts more consistent and edges more detailed. Finally, the CDA module performs adaptive calibration of the re-coupled feature map channel response to minimize external background noise interference. Experiments on the open Massachusetts Building Dataset, WHU Building Dataset show that the proposed MDBES-Net can accurately extract buildings in complex scenarios, enabling complete building segmentation with refined boundaries and improved internal consistency.

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Section: B Body-mask-edge Consistency Constraint Base Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MDBES-Net: Building Extraction From Remote Sensing Images Based on Multiscale Decoupled Body and Edge Supervision Network

Xu,

Du,

Meng

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…With the continuous development of remote sensing technology, the obtained remote sensing images with higher spatial resolution contain abundant building information. Extracting buildings from high-resolution remote sensing images has become a research hotspot [9,[13][14][15][16]. Traditional building extraction methods, based on optical remote sensing images, mainly consider low-level semantic features such as color, texture, and shape to extract buildings.…”

Section: Introductionmentioning

confidence: 99%

Attention Enhanced U-Net for Building Extraction from Farmland Based on Google and WorldView-2 Remote Sensing Images

Zhu

et al. 2021

Remote Sensing

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High-resolution remote sensing images contain abundant building information and provide an important data source for extracting buildings, which is of great significance to farmland preservation. However, the types of ground features in farmland are complex, the buildings are scattered and may be obscured by clouds or vegetation, leading to problems such as a low extraction accuracy in the existing methods. In response to the above problems, this paper proposes a method of attention-enhanced U-Net for building extraction from farmland, based on Google and WorldView-2 remote sensing images. First, a Resnet unit is adopted as the infrastructure of the U-Net network encoding part, then the spatial and channel attention mechanism module is introduced between the Resnet unit and the maximum pool and the multi-scale fusion module is added to improve the U-Net network. Second, the buildings found on WorldView-2 and Google images are extracted through farmland boundary constraints. Third, boundary optimization and fusion processing are carried out for the building extraction results on the WorldView-2 and Google images. Fourth, a case experiment is performed. The method in this paper is compared with semantic segmentation models, such as FCN8, U-Net, Attention_UNet, and DeepLabv3+. The experimental results indicate that this method attains a higher accuracy and better effect in terms of building extraction within farmland; the accuracy is 97.47%, the F1 score is 85.61%, the recall rate (Recall) is 93.02%, and the intersection of union (IoU) value is 74.85%. Hence, buildings within farming areas can be effectively extracted, which is conducive to the preservation of farmland.

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Comparison of Semantic Segmentation Deep Learning Methods for Building Extraction

Aizatin

Nugraha

2022

2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)

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Building Extraction of Multi-source Data Based on Deep Learning

Cited by 3 publications

References 2 publications

MDBES-Net: Building Extraction From Remote Sensing Images Based on Multiscale Decoupled Body and Edge Supervision Network

MDBES-Net: Building Extraction From Remote Sensing Images Based on Multiscale Decoupled Body and Edge Supervision Network

Attention Enhanced U-Net for Building Extraction from Farmland Based on Google and WorldView-2 Remote Sensing Images

Comparison of Semantic Segmentation Deep Learning Methods for Building Extraction

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