BCD-Net: building change detection based on fully scale connected U-Net and subpixel convolution

Shafique, Ayesha; Seydi, Seyd Teymoor; Cao, Guo

doi:10.1080/01431161.2023.2285737

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Comparative studies demonstrated that ChangeOS outperformed existing methods in terms of speed and accuracy, also showing enhanced generalization capabilities for anthropogenic disasters. Shafique [13] proposed a new deep learning algorithm that replaces the upsampling layer in U-Net3+ with a sub-pixel count convolutional layer, thus improving the problem of poor segmentation including irrelevant change information and inconsistent boundaries present in building change detection. Bai [14] suggested employing a U-net convolutional network for the semantic segmentation of building damage in high-resolution remote sensing imagery.…”

Section: Introductionmentioning

confidence: 99%

BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data

Liu,

Luo,

Chen

et al. 2024

Applied Sciences

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Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unmanned aerial vehicle (UAV) and remote sensing data. The proposed BDHE-Net includes the following three modules: a data augmentation module (DAM), a building damage attention module (BDAM), and a multilevel feature adaptive fusion module (MFAF), which are used to alleviate the weight deviation of intact and slightly damaged categories during model training, pay attention to the heterogeneous characteristics of damaged buildings, and enhance the extraction of house integrity contour information at different resolutions of the image. In addition, a combined loss function is used to focus more attention on the small number of severely damaged and collapsed classes. The proposed model was tested on remote sensing and UAV images acquired from the Afghanistan and Baoxing earthquakes, and the combined loss function and the role of the three modules were studied. The results show that compared with the state-of-the-art methods, the proposed BDHE-Net achieves the best results, with an F1 score improvement of 6.19–8.22%. By integrating the DBA, BDAM, and MFAF modules and combining the loss functions, the model’s classification accuracy for severely damaged and collapsed categories can be improved.

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Section: Introductionmentioning

confidence: 99%

BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data

Liu,

Luo,

Chen

et al. 2024

Applied Sciences

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Using deep learning algorithms for built-up area extraction from high-resolution GÖKTÜRK-1 satellite imagery

ARIKAN İSPİR,

YILDIZ

2024

Earth Sci Inform

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Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review

Saidi,

Idbraim,

Karmoude

et al. 2024

Remote Sensing

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Remote sensing images provide a valuable way to observe the Earth’s surface and identify objects from a satellite or airborne perspective. Researchers can gain a more comprehensive understanding of the Earth’s surface by using a variety of heterogeneous data sources, including multispectral, hyperspectral, radar, and multitemporal imagery. This abundance of different information over a specified area offers an opportunity to significantly improve change detection tasks by merging or fusing these sources. This review explores the application of deep learning for change detection in remote sensing imagery, encompassing both homogeneous and heterogeneous scenes. It delves into publicly available datasets specifically designed for this task, analyzes selected deep learning models employed for change detection, and explores current challenges and trends in the field, concluding with a look towards potential future developments.

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BCD-Net: building change detection based on fully scale connected U-Net and subpixel convolution

Cited by 3 publications

References 38 publications

BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data

BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data

Using deep learning algorithms for built-up area extraction from high-resolution GÖKTÜRK-1 satellite imagery

Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review

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