5D World Map System for Disaster-Resilience Monitoring from Global to Local: Environmental AI System for Leading SDG 9 and 11

Shiori Sasaki, Yasushi Kiyoki, Madhurima Sarkar-Swaisgood, Jinmika Wijitdechakul, Irene Erlyn Wina Rachmawan, Sanjay Srivastava, Rajib Shaw, Chalisa Veesommai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a 5D World Map System's application for disasterresilience monitoring as "Environmental AI System" of each player's implementation of United Nation's SDG 9 and DGS 11 from global-level to regional-level, country-level, sub-regional-level and city-level. In Asia-Pacific, disaster risk is outpacing disaster resilience. The gap between risk and resiliencebuilding is growing in those countries with the least capacity to prepare for and respond to disasters. Using the Sensing-Processing-Actuation (SPA) functions of 5D World Map System, a disaster risk analysis can be conducted in multiple contexts, including regional, national, and sub-national. At the regional, national and subnational levels, the analysis will focus on identifying disaster risk hotspots through incorporating existing multi-hazard disaster risk and socio-economic risk information. The system will further be used to assess future risks through integration of global climate scenarios downscaled to the region as well as countries. This paper presents the design of two new actuation functions of 5D World Map System: (1) Short-term warning with prediction and push alert and (2) Long-term warning with context-dependent multidimensional visualization, and examines the applicability of these functions by indicating that (1) will support both resident and those who are working at the operational level by being customized to disaster risk analysis for each target region/country/area, and (2) assist both policy-makers and sectoral ministries in target countries to use the analysis for evidence-based policy formulation, planning and investment towards building disaster-resilient society.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXXI
EditorsAjantha Dahanayake, Janne Huiskonen, Yasushi Kiyoki, Bernhard Thalheim, Hannu Jaakkola, Naofumi Yoshida
PublisherIOS Press
Pages306-323
Number of pages18
ISBN (Electronic)9781643680446
DOIs
Publication statusPublished - 2019 Dec 13
Event29th International Conference on Information Modeling and Knowledge Bases, EJC 2019 - Lappeenranta, Finland
Duration: 2019 Jun 32019 Jun 7

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume321
ISSN (Print)0922-6389

Conference

Conference29th International Conference on Information Modeling and Knowledge Bases, EJC 2019
CountryFinland
CityLappeenranta
Period19/6/319/6/7

Keywords

  • Actuation
  • Ai
  • Big data
  • Climate change
  • Cyber-physical system
  • Data mining
  • Environmental research
  • Global warming
  • Iot
  • Processing
  • Sdgs
  • Semantic computing
  • Semantic search
  • Sensing
  • Spa
  • Spatiotemporal
  • Sustainable development
  • Un
  • United nation
  • Visualization
  • Warning

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of '5D World Map System for Disaster-Resilience Monitoring from Global to Local: Environmental AI System for Leading SDG 9 and 11'. Together they form a unique fingerprint.

  • Cite this

    Sasaki, S., Kiyoki, Y., Sarkar-Swaisgood, M., Wijitdechakul, J., Rachmawan, I. E. W., Srivastava, S., Shaw, R., & Veesommai, C. (2019). 5D World Map System for Disaster-Resilience Monitoring from Global to Local: Environmental AI System for Leading SDG 9 and 11. In A. Dahanayake, J. Huiskonen, Y. Kiyoki, B. Thalheim, H. Jaakkola, & N. Yoshida (Eds.), Information Modelling and Knowledge Bases XXXI (pp. 306-323). (Frontiers in Artificial Intelligence and Applications; Vol. 321). IOS Press. https://doi.org/10.3233/FAIA200022