Drone-Based Water Level Detection in Flood Disasters

Hamada Rizk, Yukako Nishimur, Hirozumi Yamaguchi and Teruo Higashino
Additional contact information
Hamada Rizk: Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
Yukako Nishimur: Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
Hirozumi Yamaguchi: Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
Teruo Higashino: Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan

IJERPH, 2021, vol. 19, issue 1, 1-15

Abstract: Japan was hit by typhoon Hagibis, which came with torrential rains submerging almost eight-thousand buildings. For fast alleviation of and recovery from flood damage, a quick, broad, and accurate assessment of the damage situation is required. Image analysis provides a much more feasible alternative than on-site sensors due to their installation and maintenance costs. Nevertheless, most state-of-art research relies on only ground-level images that are inevitably limited in their field of vision. This paper presents a water level detection system based on aerial drone-based image recognition. The system applies the R-CNN learning model together with a novel labeling method on the reference objects, including houses and cars. The proposed system tackles the challenges of the limited and wild data set of flood images from the top view with data augmentation and transfer-learning overlaying Mask R-CNN for the object recognition model. Additionally, the VGG16 network is employed for water level detection purposes. We evaluated the proposed system on realistic images captured at disaster time. Preliminary results show that the system can achieve a detection accuracy of submerged objects of 73.42% with as low as only 21.43 cm error in estimating the water level.

Keywords: drone-based vision; emergency recovery; flood disaster assessment; water level detection (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/1/237/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/1/237/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2021:i:1:p:237-:d:711422

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().