In recent years, various types of disasters have occurred frequently in Japan. Such incidents require a rapid response. It is necessary to grasp the full extent of the disaster at an early stage. Research and development of effective methods to achieve this are in progress. Although each method has its own characteristics, from a business perspective it is necessary to know when and which method should be used to obtain the full extent of the damage. As of yet, there is no comparison among methods to answer this question. Therefore, the purpose of this study is to position the time-cost per unit area as one of the evaluation criteria to understand or estimate damage. To achieve this objective, the procedure of each method is clarified, the area to be analyzed by each method is identified, and the time-cost of each procedure is estimated. The time-cost per unit area is calculated by dividing the time-cost by the area of interest. Particularly, the time required for the preparation of each method, which is independent on the area, is positioned as the initial time-cost that is also derived and added. Based on the above, a linear function with the area of damage as a variable is determined. Simulations are performed to derive the estimated time-cost. Depending on the assumed area of damage, results are obtained when each method is applied.

1. [1] H. Hayashi and M. Inoguchi, “What Should Be Considered to Realize ICT Support for Effective Disaster Response and Recovery?,” IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E98-A, No.08, pp. 1594-1601, 2015.
2. [2] K. Abe et al., “Support for disaster situation awareness by collaboration of image processing and crowdsourcing,” Proc. of the 77th National Convention of Information Processing Society of Japan (DVD-ROM), p. 2, 2015 (in Japanese).
3. [3] M. Inoguchi, S. Hara, K. Shirai, and A. Imai, “Challenge of Roof Damage Housings Detection from Satellite Images by Applying Deep Learning Methodology –A Case Study of Ibaraki City at 2018 Osaka Earthquake–,” Proc. of the 35th Int. Technical Conf. on Circuits/Systems, Computers and Communications (ITC-CSCC2020), 2020.
4. [4] M. Inoguchi, K. Tamura, and R. Hamamoto, “Establishment of Work-Flow for Roof Damage Detection Utilizing Drones, Human and AI based on Human-in-the-Loop Framework,” Proc. of IEEE Int. Conf. on Big Data 2019, pp. 4618-4623, 2019.
5. [5] Cabinet Office of Japan, “Guideline for building damage certification standard operation at disaster,” 2020, http://www.bousai.go.jp/taisaku/pdf/r203shishin_all.pdf (in Japanese) [accessed June 24, 2020]
6. [6] M. Inoguchi, K. Tanyra, K. Hirue, and H. Hayashi, “Implementation of Web-based System for Building Damage Assessment on Online Network: Case studies of Typhoon MAN-YI (1318) and Typhoon WIPHA (1326) in Japan,” 2014 IEEE Asia Pacific Conf. on Circuits and Systems (APCCAS), pp. 395-398, 2014.
7. [7] M. Inoguchi et al., “Utilization of Aerial Photos Taken by Drone for Capturing Roof Damage and Survivors Support –A Case Study of Response in Murakami City at 2019 Yamagata-oki Earthquake–,” Proc. on the 60th Conf. of Civil Engineering Planning Research (CD-ROM), Vol.60, p. 6, 2019.
8. [8] International Disasters Charter, “Disaster Relief Organisations,” https://disasterscharter.org/web/guest/about-the-charter [accessed March 8, 2021]
9. [9] National Research Institute for Earth Science and Disaster Resilience (NIED), “Response in theme-2 to Typhoon No.19 in 2019,” 2020, https://www.bosai.go.jp/nr/result/result_detail_02.html (in Japanese) [accessed June 24, 2020]
10. [10] Water and Disaster Management Bureau, Ministry of Land, Infrastructure, Transport and Tourism, “Guidebook for utilization of satellite at disaster –Flood version–,” p. 16, 2018, https://www.mlit.go.jp/common/001227723.pdf (in Japanese) [accessed June 24, 2020]
11. [11] The Remote Sensing Society of Japan, “Guideline for utilization of satellite data at disaster,” 2017, http://rssj-kokudo.civil.ibaraki.ac.jp/file/saigai-guide.pdf (in Japanese) [accessed June 24, 2020]
12. [12] National Research Institute for Earth Science and Disaster Resilience (NIED), “Crisis Response Site on Typhoon No.19, 2019,” 2019, http://crs.bosai.go.jp/DynamicCRS/index.html?appid=9424c7b32d784b60a9b70d59ff32ac96 (in Japanese) [accessed June 24, 2020]
13. [13] M. Inoguchi, S. Hara, K. Shirai, and A. Imai, “Challenge of Roof Damage Housings Detection from Satellite Images by Applying Deep Learning Methodology –A Case Study of Ibaraki City at 2018 Osaka Earthquake–,” Proc. of the 35th Int. Technical Conf. on Circuits/Systems, Computers and Communications, pp. 172-176, 2020.
14. [14] A. Morishima, M. Inoguchi, K. Tajima, and I. Kitahara, “Digital Knowledge Era and the Future of Work: Challenges and the Current Status of JST Cyborg Crowd Project,” Workshop on Addressing SDGs Through Data Analytics and AI, Int. Conf. on Digital Landscape 2019, pp. 68-78, 2019.
15. [15] Geospatial Information Authority of Japan, “Information on July rainfall disaster in 2018,” 2018, https://www.gsi.go.jp/BOUSAI/H30.taihuu7gou.html (in Japanese) [accessed Septemer 11, 2020]
16. [16] M. Inoguchi, K. Tamura, K. Uo, and M. Kobayashi, “Validation of CyborgCrowd Implementation Possibility for Situation Awareness in Urgent Disaster Response –Case study of International Disaster Response in 2019–,” Proc. of 2020 IEEE Int. Conf. on BigData, 2020.
17. [17] Cabinet Office of Japan, “About points to note regarding the efficiency and speeding up the damage certification investigations of housings at Typhoon No.19 in 2019,” October 2019, http://www.bousai.go.jp/oyakudachi/pdf/siryo_26.pdf (in Japanese) [accessed September 11, 2020]
18. [18] Ministry of Internal Affairs and Communications (Statistics Bureau), “Chapter1: Characteristics and history of regional mesh statistics,” http://www.stat.go.jp/data/mesh/pdf/gaiyo1.pdf (in Japanese) [accessed June 24, 2020]