Flood Magnitude Assessment from UAV Aerial Videos Based on Image Segmentation and Similarity

Ananya Sharma, Ujjwal Verma

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

2 Citations (Scopus)


Natural disasters such as floods cause huge loss of life and property every year. Hence, it is imperative to detect and estimate the magnitude of a flood in a flood-Affected area. Besides, it is essential to assess the damage caused by the flood as quickly as possible for an effective post-disaster relief and rescue effort. However, the longer frequency of data acquisition from the existing remote sensing-based methods for post-disaster damage assessment can delay relief. In this work, we propose an approach to estimate the magnitude of the flooded region by analyzing the aerial images acquired from unmanned aerial vehicles (UAV). The proposed method computes two parameters: one based on unsupervised image segmentation and another on image similarity between input and flooded images. These parameters are then utilized to develop a model to estimate the flood magnitude in the aerial image. The proposed approach is evaluated on the FloodNet dataset, and an Fl-score of 0.90 was obtained. demonstrating the proposed algorithm's robustness.

Original languageEnglish
Title of host publicationTENCON 2021 - 2021 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665495325
Publication statusPublished - 2021
Event2021 IEEE Region 10 Conference, TENCON 2021 - Auckland, New Zealand
Duration: 07-12-202110-12-2021

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2021 IEEE Region 10 Conference, TENCON 2021
Country/TerritoryNew Zealand

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


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