Method to Generate Building Damage Maps by Combining Aerial Image Processing and Crowdsourcing
Hidehiko Shishido, Koyo Kobayashi, Yoshinari Kameda, and Itaru Kitahara
University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
Building damage maps that show the damage status of buildings are an essential information source for various disaster countermeasures, such as evacuation, rescue, and reconstruction. Therefore, they must be generated as quickly as possible. However, to generate a building damage map, it is necessary to collect disaster information and estimate the damage situation over a wide area, which is time consuming. (In this paper, we consider disaster information collection as capturing aerial images.) In recent years, crowdsourcing has been widely used to understand the damage situation. Crowdsourcing achieves large-scale work by dividing it into microtasks that can be solved by anyone and by distributing the microtasks among an unspecified number of workers. We believe that crowdsourcing is suitable for gathering information and assessing damage situations as it can adjust the type and number of workers in a scalable manner and allocate resources according to the size of the disaster. Therefore, crowdsourcing has been used for gathering information and assessing the situation during disaster management. However, usually, the two types of crowdsourcing tasks (i.e., gathering information and assessing the damage) are performed independently; consequently, the collected information is often not utilized effectively. More efficient work can be expected by linking the two crowdsourcing tasks. This paper proposes a framework for efficiently generating a building damage map by combining the two methods of information collection on disaster areas and assessment of disaster situations using aerial image processing. The results of an experiment using a prototype of our proposed framework clarify the range of applications in the collection and assessment crowdsourcing tasks. The experimental results indicate the feasibility of understanding disaster situations using our method. In addition, it is possible to install artificial intelligence workers that can support human workers to estimate the damage situation more quickly.
-  Cabinet Office Japan, “White Paper on Disaster Management in Japan 2018,” 2018, http://www.bousai.go.jp/kaigirep/hakusho/pdf/H30_hakusho_english.pdf [accessed July 23, 2020]
-  Pix4D, DRONEBIRD, https://www.pix4d.com/blog/drone-disaster-rescue [accessed July 23, 2020]
-  Crowd4U, http://crowd4u.org/en/projects [accessed July 23, 2020]
-  A. J. Flanagin and M. J. Metzger, “The Credibility of Volunteered Geographic Information,” GeoJournal, Vol.72, No.3, pp. 137-148, 2008.
-  M. F. Goodchild and J. A. Glennon, “Crowdsourcing Geographic Information for Disaster Response: A Research Frontier,” Int. J. of Digital Earth, Vol.3, No.3, pp. 231-241, 2010.
-  K. Starbird, “Digital Volunteerism During Disaster: Crowdsourcing Information Processing,” Conf. on Human Factors in Computing Systems, 2011.
-  M. Zook, M. Graham, T. Shelton, and S. Gorman, “Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake,” World Medical & Health Policy, Vol.2, No.2, pp. 7-33, 2010.
-  S. Naito, N. Monma, H. Nakamura, H. Fujiwara, H. Shimomura, and T. Yamada, “Investigation of Building Damages Caused by the 2016 Kumamoto Earthquake Utilizing Aerial Photographic Interpretation,” J. of Japan Society of Civil Engineers, Ser. A1 (Structural Engineering & Earthquake Engineering (SE/EE)), Vol.74, No.4, pp. I_464-I_480, 2018 (in Japanese).
-  H. Tanji, A. Morishima, M. Inoguchi, and H. Kitagawa, “An Attempt to Develop Crowdsourcing Techniques for Identifying Tornado’s Paths Based on Web Data,” IPSJ Trans. on Databases (TOD), Vol.6, No.5, pp. 95-106, 2013 (in Japanese).
-  N. Snavely, S. M. Seitz, and R. Szeliski, “Photo Tourism: Exploring Photo Collections in 3D,” ACM Trans. on Graphics, Vol.25, No.3, pp. 835-846, 2006.
-  Geospatial Information Authority of Japan (GSI), https://www.gsi.go.jp/ENGLISH/index.html [accessed July 23, 2020]
-  K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” Proc. of 2017 IEEE Int. Conf. on Computer Vision (ICCV), pp. 2980-2988, 2017.
-  crowdAI, “Mapping Challenge,” https://www.crowdai.org/challenges/mapping-challenge [accessed July 23, 2020]
-  Q. X. Yi, H. Shishido, Y. Kameda, and I. Kitahara, “Bullet-Time Book: Augmentation of Visual Information in Figures by Bullet-Time Video Display,” The 2nd Asia-Pacific Workshop on Mixed and Augmented Reality (APMAR), p. 4, 2018.
-  Tsubame City, “Niigata Prefecture Comprehensive Disaster Prevention Drill,” 2018, http://www.city.tsubame.niigata.jp/life/041001144.html [accessed December 23, 2020]
-  P. W. Koh and P. Liang, “Understanding Black-box Predictions via Influence Functions,” Proc. of the 34th Int. Conf. on Machine Learning (Proc. of Machine Learning Research (PMLR) Vol.70), pp. 1885-1894, 2017.
-  L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, and T. Darrell, “Generating Visual Explanations,” Proc. of the 14th European Conf. on Computer Vision (ECCV 2016), pp. 3-19, 2016.
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