Development of Three-Dimensional Convolutional Neural Network for Urban Flood Classification Using Synthetic Aperture Radar Multi-Temporal Image

Dodi Sudiana, Indra Riyanto, Mia Rizkinia, Rahmat Arief, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo, Ketut Wikantika

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Urban flooding is a significant catastrophe due to its widespread impact on the population. Typically, floods occur concurrently with heavy rainfall, rendering the affected area obscured by clouds when observed through optical sensors on satellites. A novel approach is proposed in this study to address this issue, aiming to classify flooded urban areas using a remote sensing synthetic aperture radar (SAR) sensor on a satellite. Unlike optical sensors, SAR can penetrate clouds. The framework was developed using the 3D convolutional neural network (3DCNN) method to preserve the temporal variability, which processed multi-temporal SAR data from Sentinel-1 (S-1). The dataset used in this research comprised 24 S-1 scenes covering Jakarta Province, Indonesia, with Dual VV and VH polarization, between March 2019 and February 2020, divided into two co-event images, 18 pre-event images, and four post-event images. We employed hyper-parameters of 150 epochs, a batch size of 100, and a learning rate of 0.001, with a training/testing data split of 80/20 in the training phase. The 3DCNN achieved an average overall accuracy of 70.3%, with a maximum accuracy of 71.4%, and each epoch took 113 seconds on average to process. These results demonstrate the potential of the 3DCNN method to accurately estimate the extent of flooding and identify areas at risk of flooding, thereby aiding early detection and flood prevention efforts in other urban areas in the future.

Original languageEnglish
Title of host publicationAPSAR 2023 - 2023 8th Asia-Pacific Conference on Synthetic Aperture Radar
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350393590
DOIs
Publication statusPublished - 2023
Event8th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2023 - Bali, Indonesia
Duration: 23 Oct 202327 Oct 2023

Publication series

NameAPSAR 2023 - 2023 8th Asia-Pacific Conference on Synthetic Aperture Radar

Conference

Conference8th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2023
Country/TerritoryIndonesia
CityBali
Period23/10/2327/10/23

Keywords

  • 3D Convolutional Neural Network
  • machine learning
  • multi-temporal data
  • Synthetic Aperture Radar (SAR)
  • urban floods

Fingerprint

Dive into the research topics of 'Development of Three-Dimensional Convolutional Neural Network for Urban Flood Classification Using Synthetic Aperture Radar Multi-Temporal Image'. Together they form a unique fingerprint.

Cite this