Estimation of pixel-level seismic vulnerability of the building environment based on mid-resolution optical remote sensing images

Fan, Xiwei; Nie, Guigen; Xia, Chaoxu; Zhou, Junxue

doi:10.1016/j.jag.2021.102339

Cited by 11 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies previously attempted to estimate vulnerability to various hazards like floods (Guo et al, 2021) and seismicity (Fan et al, 2021). However, attempts to assess the spatiotemporal changes are still sporadic.…”

Section: Correlation Of Ntl With Factors Driving Flood Vulnerabilitymentioning

confidence: 99%

“…However, studies related to vulnerability assessment using NTL data are very limited. Only a few attempts have been made in recent years to assess vulnerability to different hazards using NTL data (Ceola et al, 2015;Fan et al, 2021;Y. Li et al, 2021;Mård et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Li et al, 2021;Mård et al, 2018). Fan et al (2021) estimated the seismic vulnerability of buildings using NTL and MODIS vegetation index data. Y.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring spatiotemporal changes in urban flood vulnerability from satellite nighttime light data

Ziarh

Chung

Shahid

et al. 2023

Preprint

View full text Add to dashboard Cite

Urban flood vulnerability monitoring needs a large amount of socioeconomic and environmental data collected at regular time intervals. Collecting such data volume is a significant constraint in assessing changes in flood vulnerability. This study proposed a novel method to monitor spatiotemporal changes in urban flood vulnerability from satellite nighttime light (NTL) data. Peninsular Malaysia was chosen as the research region. A flood vulnerability index (FVI), estimated from socioeconomic and environmental data for a year, was linked to NTL data using a machine learning algorithm called Adaptive neuro-fuzzy inference system (ANFIS). The model was calibrated and validated with administrative unit scale data and subsequently used to predict FVI at a spatial resolution of 10 km for 2000‒2018 using NTL data. Finally, changes in estimated FVI at different grid points were evaluated using the Mann-Kendall trend method to determine changes in flood vulnerability over time and space. Results showed a nonlinear relationship between NTL and flood vulnerability factors such as population density, Gini coefficient and percentage of foreign nationals. The ANFIS technique showed a good performance in estimating FVI from NTL data with a normalized root-mean-square error of 0.68 and Kling-Gupta Efficiency of 0.73. The FVI revealed a high vulnerability in the urbanized western coastal region (FVI ~ 0.5 to 0.54), which matches well with major contributing regions to flood losses in Peninsular Malaysia. Trend assessment showed a significant increase in flood vulnerability in the study area from 2000 to 2018. The spatial distribution of the trend indicated an increase in FVI in the urbanized coastal plains, particularly in rapidly developing western and southern urban regions. The results indicate the potential of the technique in urban flood vulnerability assessment using freely available satellite NTL data.

show abstract

Section: Correlation Of Ntl With Factors Driving Flood Vulnerabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring spatiotemporal changes in urban flood vulnerability from satellite nighttime light data

Ziarh

Chung

Shahid

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In contrast to that, e.g., Borfecchia et al (2010), Geiß et al (2015a, 2017b, 2018, Liuzzi et al (2019), Liu et al (2019), Torres et al (2019), and An et al (2021) combined limited in situ ground truth information characterizing the building inventory with features from remote sensing and use techniques of statistical inference for a complete labeling of the residual building inventory according to specific vulnerability levels or more generic properties such as construction material or occupancy, respectively. Related methodological principles were also exploited by, e.g., Wieland et al (2012Wieland et al ( , 2016, Wieland (2013), Geiß et al (2016), Pittore et al (2020), andFan et al (2021) to assess seismic vulnerability or related parameters on a coarser spatial level to allow for the use of data with larger spatial coverage. Recently, Aravena Pelizari et al (2021) deployed street-level imagery that was extracted from the GoogleStreetView platform and classified various seismic structural types with deep learning models to automatically compile relevant in situ data.…”

Section: Introductionmentioning

confidence: 99%

Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile

et al. 2022

View full text Add to dashboard Cite

Exposure is an essential component of risk models and describes elements that are endangered by a hazard and susceptible to damage. The associated vulnerability characterizes the likelihood of experiencing damage (which can translate into losses) at a certain level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) of risk assessment procedures. Existing models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow the collection of spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of constituting morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we establish dasymetric methods for disaggregation. The results are presented for the city of Santiago de Chile, which is prone to natural hazards such as earthquakes. We present loss estimations due to seismic ground shaking and corresponding sensitivity as a function of the resolution properties of the exposure data used in the model. The experimental results underline the benefits of deploying modern earth observation technologies for refined exposure mapping and related earthquake loss estimation with enhanced accuracy properties.

show abstract

“…A high-resolution optical satellite imagery in Germany allowed the extraction of building shape, position, and height to some extent [5]. In Yancheng, China, a mid-resolution satellite imagery in the study areas provided seismic vulnerability estimation while field investigation calibrated the assessment [6]. Given the limitations of these methods, further inquiry in the potential of remote sensing can provide a convenient, but not ultimate, assessment of seismic vulnerability of buildings.…”

mentioning

confidence: 99%

Utilizing Google Street View for Rapid Seismic Vulnerability Assessment: Case Study in the City of Manila, Philippines

Evangelista

Aquino

Ramos

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Earthquake Impact Reduction Study for Metro Manila (MMEIRS) estimated that around 40% of the total number of residential buildings within Metro Manila will either collapse or be affected by the M7.2 generated by the West Valley Fault. Need arises to evaluate the seismic vulnerability of structures in the area to enhance the city’s resilience to seismic hazards. Rapid seismic vulnerability assessments are typically conducted by means of sidewalk surveys. However, advances in digital technologies such as Google Street View (GSV) provide the potential to do remote assessments, particularly amid mobility restrictions brought about by the COVID-19 pandemic. This paper aims to demonstrate the usefulness of GSV in collecting data needed for rapid seismic vulnerability assessments through the case of buildings in the City of Manila. Six 300 m x 300 m blocks were evaluated using GSV for identifying seismic-related building parameters. Results show the ease of use of GSV in data collection on areas encompassing commercial and residential zones within the city and poses difficulty for blocks dominated by informal settlements. Among the challenges observed in formal zones include blockages in views due to fences, trees, and/or vehicles parked in front. For informal settlements, much of the buildings are not visible in GSV for evaluation thereby necessitating supplemental data collection. Overall, GSV demonstrates usefulness, and has the potential to speed up seismic vulnerability assessments in urban areas in conjunction with existing in situ assessments currently conducted.

show abstract

Estimation of pixel-level seismic vulnerability of the building environment based on mid-resolution optical remote sensing images

Cited by 11 publications

References 34 publications

Monitoring spatiotemporal changes in urban flood vulnerability from satellite nighttime light data

Monitoring spatiotemporal changes in urban flood vulnerability from satellite nighttime light data

Benefits of global earth observation missions for disaggregation of exposure data and earthquake loss modeling: evidence from Santiago de Chile

Utilizing Google Street View for Rapid Seismic Vulnerability Assessment: Case Study in the City of Manila, Philippines

Contact Info

Product

Resources

About