Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images

Kalantar, Bahareh; Ueda, Naonori; Al-Najjar, Husam Abdulrasool H.; Halin, Alfian Abdul

doi:10.3390/rs12213529

Cited by 36 publications

(26 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In case of a landslide that occurs in the areas of tailings ponds and dumps, the sudden release of radioactively contaminated sediments into the local surface waters encompasses both physical and chemical processes and may cause instantaneous hydraulic changes, redistribution of sediments, and an increase in suspended particulate matter (SPM) [12][13][14]. Due to the significant societal, economic, and environmental impacts related to landslide-induced mass movements, quantifying the mass underlying the subsequence of landslides has become key to prevent radioactive pollution through surface waters, particularly through river networks [13,15,16]. The methods for mass estimation can be basically classified into five types: field surveys, physically-based modeling, empirical modeling, multi-temporal digital elevation model (DEM) analysis, and geometrical estimation [17,18].…”

Section: Introductionmentioning

confidence: 99%

Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia

Torgoev

Zaredinov

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Seismically triggered landslides are a major hazard and have caused severe secondary losses. This problem is especially important in the seismic prone Mailuu-Suu catchment in Kyrgyzstan, as it hosts disproportionately sensitive active or legacy uranium sites with deposited radioactive extractive wastes. These sites show a quasi-continuous release of radioactive contamination into surface waters, and especially after natural hazards, a sudden and massive input of pollutants into the surface waters is expected. However, landslides of contaminated sediments into surface waters represent a substantial exposure pathway that has not been properly addressed in the existing river basin management to date. To fill this gap, satellite imagery was massively employed to extract topography and geometric information, and the seismic Scoops3D and the one-dimensional numerical model, Hydrologic Engineering Centre, River Analysis System (HEC-RAS), were chosen to simulate the landslide-induced mass transport of total suspended solids (TSS) and natural radionuclides (Pb-210 as a proxy for modeling purposes) within the Mailuu-Suu river networks under two earthquake and two hydrological scenarios. The results show that the seismically vulnerable areas dominated in the upstream areas, and the mass of landslides increased dramatically with the increase of earthquake levels. After the landslides, the concentrations of radionuclides increased suddenly and dramatically. The peak values decreased along the longitudinal gradient of river networks, with the concentration curves becoming flat and wide in the downstream sections, and the transport speed of radionuclides decreased along the river networks. The conclusions of this study are that landslides commonly release a significant amount of pollutants with a relatively fast transport along river networks. Improved quantitative understanding of waterborne pollution dispersion across national borders will contribute to better co-ordination between governments and regulatory authorities of riparian states and, consequently, to future prevention of transnational political conflicts that have flared up in the last two decades over alleged pollution of transboundary water bodies.

show abstract

Section: Introductionmentioning

confidence: 99%

Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia

Torgoev

Zaredinov

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Many remote sensing technologies are used in building damage detection after disasters, such as optical, light detection and ranging (LiDAR), and synthetic aperture radar (SAR) [5]. LiDAR can obtain three-dimensional information of disaster areas and is a useful tool for building damage detection [6].…”

Section: Introductionmentioning

confidence: 99%

“…LiDAR can obtain three-dimensional information of disaster areas and is a useful tool for building damage detection [6]. However, the LiDAR dataset is not always available [5]. Optical images provide an intuitive view of the observed area and are easy to interpret.…”

Section: Introductionmentioning

confidence: 99%

“…Optical-based methods have been studied widely and can obtain accurate detection results of building damage. However, optical remote sensing greatly depends on sun illumination for imaging and is easily affected by atmospheric conditions, such as cloud coverage, which limits its application as an emergency tool directly following a disaster [5,12].…”

Section: Introductionmentioning

confidence: 99%

“…Var, variance; 2 Hom, homogeneity; 3 Con, contrast; 4 Dis, dissimilarity;5 Entr, entropy; 6 SeM, second moment;7 Cor, correlation.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

et al. 2021

View full text Add to dashboard Cite

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: ); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: ); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: ). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

show abstract

Automated detection of damaged buildings in post-disaster scenarios: a case study of Kahramanmaraş (Türkiye) earthquakes on February 6, 2023

Serifoglu Yilmaz,

Yilmaz,

Tansey

et al. 2023

Nat Hazards

View full text Add to dashboard Cite

Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images

Cited by 36 publications

References 35 publications

Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia

Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

Automated detection of damaged buildings in post-disaster scenarios: a case study of Kahramanmaraş (Türkiye) earthquakes on February 6, 2023

Contact Info

Product

Resources

About