Extraction of Element at Risk for Landslides Using Remote Sensing Method

Hasan, Rozaimi Che; Rosle, Qalam Azad; Asmadi, Mohd; Kamal, N. A. Mohd

doi:10.5194/isprs-archives-xlii-4-w9-181-2018

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The term "exposure element" and "exposed element" is limited in the peer reviewed literature. The term "element at risk" is more common (Bell and Glade, 2004;Papathoma-Kohle et al, 2007;van Westen et al, 2012;Papathoma-Kohle, 2016;Rozaimi et al, 2018). In many cases, elements at risk refers to critical infrastructure (Forzieri et al, 2016;Di Felice, 2020;Hawchar et al, 2020;Papilloud et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Exposure Elements in Disaster Databases and Availability for Local Scale Application: Case Study of Kuala Lumpur, Malaysia

2021

View full text Add to dashboard Cite

Exposure elements in open-access disaster databases that are relevant to critical infrastructure and basic services in the Sendai Framework on Disaster Risk Reduction (SFDRR) were transformed into spatial data, to investigate the impact of flash flood hazards in Kuala Lumpur, Malaysia. In this era of big data and hyper-connectivity, the availability of open-access data on exposure elements across scales and systems is largely unknown. Information on exposure elements and hazard susceptibility provide important insights to enhance community resilience, to move away from merely managing disasters to managing the risk of disasters, in line with the SFDRR. The case study of Kuala Lumpur enabled an assessment of information availability in existing disaster databases and within the national system, to facilitate informed decision-making. Findings reveal that there are a total of 26 databases on the internet that provide information on disasters and related elements; of which 18 are global, three are regional and four provide information at the national scale. However, only ten databases are open access where the user is able to easily retrieve information while others provide a “view only option”. The coverage of exposure elements in disaster databases is very poor where only five databases carried such information; and it is not useful for local scale application. Thus, information was sought from multiple open data sources within the national system and transformed into spatial data, to develop an exposure element data inventory for the city. There are 509 exposure elements within Kuala Lumpur, covering 33 private and government hospitals and community clinics; 189 public and private schools and institutions higher education; 261 facilities that provide basic services; and 26 features that represent of social and economic aspects. The exposure elements, which is coherent with the SFDRR, benefits decision-making when overlain with existing flood hazard zones and susceptible areas. Moving forward, emerging hazards due to climate change will be evaluated to strengthen informed decision-making and build community resilience in the city. The empowerment of local level research has great potential to advance open sharing of information on disaster and climate risks in the region.

show abstract

Section: Introductionmentioning

confidence: 99%

Exposure Elements in Disaster Databases and Availability for Local Scale Application: Case Study of Kuala Lumpur, Malaysia

2021

View full text Add to dashboard Cite

show abstract

Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence

et al. 2022

View full text Add to dashboard Cite

The mapping and characterisation of building footprints is a challenging task due to inaccessibility and incompleteness of the required data, thus hindering the estimation of loss caused by natural and anthropogenic hazards. Major advancements have been made in the collaborative mapping of buildings with platforms like OpenStreetMap, however, many parts of the world still lack this information or the information is outdated. We created a semi-automated workflow for the development of elements-at-risk (EaR) databases of buildings by detecting building footprints using deep learning and characterising the footprints with building occupancy information using building morphological metrics and open-source auxiliary data. The deep learning model was used to detect building EaR footprints in a city in Kerala (India) with an F1 score of over 76%. The footprints were classified into 13 building occupancy types along with information such as average number of floors, total floor space area, building density, and percentage of built-up area. We analysed the transferability of the approach to a different city in Kerala and obtained an almost similar F1 score of 74%. We also examined the exposure of the buildings and the associated occupancies to floods using the 2018 flood susceptibility map of the respective cities. We notice certain shortcomings in our research particularly, the need for a local expert and good quality auxiliary data to obtain reasonable building occupancy information, however, our research contributes to developing a rapid method for generating a building EaR database in data-scarce regions with attributes of occupancy types, thus supporting regional risk assessment, disaster risk mitigation, risk reduction initiatives, and policy developments.

show abstract

Extraction of Element at Risk for Landslides Using Remote Sensing Method

Cited by 2 publications

References 9 publications

Exposure Elements in Disaster Databases and Availability for Local Scale Application: Case Study of Kuala Lumpur, Malaysia

Exposure Elements in Disaster Databases and Availability for Local Scale Application: Case Study of Kuala Lumpur, Malaysia

Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence

Contact Info

Product

Resources

About