BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge

Ismail, Ali Mohamed Ali; Awad, Mariette

doi:10.48550/arxiv.2201.10389

Cited by 2 publications

(2 citation statements)

References 53 publications

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“…This model's efficacy was evaluated on the xView2 dataset for building damage assessment, demonstrating precise damage scale classification and building segmentation. Ismail et al developed a semi-supervised framework BLDNet based on CNN, applied to various scenarios of damage detection in the xBD dataset [91,92]. A refined Swin-Unet method was introduced by Xu et al in [93] and applied in different scenarios, such as highresolution Gaofen-2/Jilin-1 multi-temporal optical images and satellite image datasets (xBD).…”

Section: View2 Challenge and Xbd Datasetmentioning

confidence: 99%

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Gu,

Xie,

Zhang

et al. 2024

Buildings

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After a disaster, ascertaining the operational state of extensive infrastructures and building clusters on a regional scale is critical for rapid decision-making and initial response. In this context, the use of remote sensing imagery has been acknowledged as a valuable adjunct to simulation model-based prediction methods. However, a key question arises: how to link these images to dependable assessment results, given their inherent limitations in incompleteness, suboptimal quality, and low resolution? This article comprehensively reviews the methods for post-disaster building damage recognition through remote sensing, with particular emphasis on a thorough discussion of the challenges encountered in building damage detection and the various approaches attempted based on the resultant findings. We delineate the process of the literature review, the research workflow, and the critical areas in the present study. The analysis result highlights the merits of image-based recognition methods, such as low cost, high efficiency, and extensive coverage. As a result, the evolution of building damage recognition methods using post-disaster remote sensing images is categorized into three critical stages: the visual inspection stage, the pure algorithm stage, and the data-driven algorithm stage. Crucial advances in algorithms pertinent to the present research topic are comprehensively reviewed, with details on their motivation, key innovation, and quantified effectiveness as assessed through test data. Finally, a case study is performed, involving seven state-of-the-art AI models, which are applied to sample sets of remote sensing images obtained from the 2024 Noto Peninsula earthquake in Japan and the 2023 Turkey earthquake. To facilitate a cohesive and thorough grasp of these algorithms in their implementation and practical application, we have deliberated on the analytical outcomes and accentuated the characteristics of each method through the practitioner’s lens. Additionally, we propose recommendations for improvements to be considered in the advancement of advanced algorithms.

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Section: View2 Challenge and Xbd Datasetmentioning

confidence: 99%

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Gu,

Xie,

Zhang

et al. 2024

Buildings

View full text Add to dashboard Cite

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“…The network uses reconstruction loss values as a detection criterion [42]. Ali et al propose a novel graph formulation (BLDNet) and use GCN learning relationships and representations from both non-stationary neighborhoods and local patterns [43]. There are also works built based on attention schemes which will be introduced in the next subsection.…”

Section: Related Workmentioning

confidence: 99%

Partitioning and Migration of Trace Elements during Coal Combustion in Two Coal-Fired Power Plants in Hefei City, Anhui Province, Eastern China

Jiang

Chen

et al. 2023

Minerals

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Coal-fired thermal power plants that meet the huge energy demand of China largely contribute to carbon emissions, environmental pollution, and human health issues. To investigate the impact of coal-fired power plants on the environment in the most developed region of eastern China, samples of feed coal, limestone, slag, fly ash, and flue gas desulfurization (FGD) gypsum were collected from two coal-fired power plants in Hefei City, Anhui Province, China. The concentrations of trace elements in these samples were determined using inductively coupled plasma mass spectrometry (ICP-MS). The results show that the feed coal was slightly rich in V, Cr, Se, In, Sb, Pb, and Ga compared to common Chinese coal. Most trace elements were concentrated in fly ash, especially in the fine particles. Only Sc, Cr, Rb, and Ba were enriched in slag. The smaller the particle size of the fly ash, the higher the fraction of volatile trace elements. The elemental concentrations of old fly ash, which was precipitated in the well of the flue gas pipeline, did not show noticeable differences from fresh fly ash. In addition, the short-time storage of fly ash did not influence the trace elements. To understand the possibility of recovering rare earth elements from coal combustion by-products, the outlook coefficients (Coutl) of coal combustion by-products for the Wanneng (WN) and Tianyuan (TY) power plants were calculated, and they were found to be promising for rare earth elements plus yttrium (REY) recovery. However, as per the evaluation diagram of cut-off grade and Coutl values, all feed coal and its by-products from these two coal-fired power plants are projected to be ‘unpromising’. Therefore, the coal combustion by-products from the WN and TY coal-fired power plants were not candidates for REY recovery.

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BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge

Cited by 2 publications

References 53 publications

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey

Partitioning and Migration of Trace Elements during Coal Combustion in Two Coal-Fired Power Plants in Hefei City, Anhui Province, Eastern China

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