Flood Mapping through Sentinel-1, Sentinel-2 Imagery and U-NET Deep Learning Model

Authors

  • Fernando Pech May Tecnológico Nacional de México
  • Omar Álvarez Cárdenas Universidad de Colima
  • Germán Ríos Toledo Tecnológico Nacional de México

DOI:

https://doi.org/10.13053/cys-29-1-5539

Keywords:

Deep Flood mapping, Sentinel-1 to flood mapping, Flood detection with U-Net

Abstract

Natural disasters are inevitable phenomena that occur more frequently around the world. They often cause severe damage to populations, infrastructure and economic activities. Floods are the most common occurrences due to extreme meteorological events suchas intense rain fall, tropical storms and hurricanes. In Mexico, floods happen every year in different states ofthe country. The state of Tabasco is one of the most affected due to its numerous water bodies. In the state, floods impact agriculture, livestock and other economic sectors, causing significant damage to the population.This has led to efforts to develop strategies to reducethe impact on populations. In recent years, various studies have been conducted to detect floods. Most of these studies rely on the use of satellite images and deep learning algorithms with the purpose of mapping areas affected by floods. The combination of these technologies is becoming one of the most effective methods. This paper presents a methodology for flood mapping in the Rıos zone of Tabasco State using Sentinel-1 SAR, Sentinel-2, and U-Net deep learning architecture. The study period was from 2019 to 2023. The results obtained show that with more data and training periods, accuracy in detecting floods improves.

Downloads

Published

2025-03-24

Issue

Vol. 29 No. 1 (2025): 29(1) 2025

Section

Articles

License 

Hereby I transfer exclusively to the Journal "Computación y 
Sistemas", published by the Computing Research Center (CIC-IPN),
the Copyright of the aforementioned paper. I also accept that these 
rights will not be transferred to any other publication, in any other 
format, language or other existing means of developing.
I certify that the paper has not been previously disclosed or simultaneo
usly submitted to any other publication, and that it does not contain 
material whose publication would violate the Copyright or other 
proprietary rights of any person, company or institution. I certify that 
I have the permission from the institution or company where I work or 
study to publish this work.
The representative author accepts the responsibility for the publication
of this paper on behalf of each and every one of the authors. 
This transfer is subject to the following conditions:
  • The authors retain all ownership rights (such as patent rights) of this work, except for the publishing rights transferred to the CIC, through this document.
  • Authors retain the right to publish the work in whole or in part in any book they are the authors or publishers. They can also make use of this work in conferences, courses, personal web pages, and so on.
  • Authors may include working as part of his thesis, for non-profit distribution only.