Disaster Impact Analysis Uses Land Cover Classification, Case study: Petobo Liquefaction

Rachmat Hidayat, Aniati Murni Arymurthy, Dimas Sony Dewantara

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

Abstract

Analysis of changes in the conditions of an area can be done through satellite image analysis. This study utilizes the classification of satellite imagery to determine the impact of disasters and liquefaction disaster recovery efforts in the Petobo region, Palu, Central Sulawesi. The deep learning approach, namely Convolutional Neural Network (CNN) and CNN combined with ResNet as the Transfer Learning model, were selected as classification methods that would be compared in determining the approach with the best performance. The classification of satellite imagery is mapped into two main classes, namely natural land cover and artificial land cover. This research subsequently succeeded in mapping land cover changes that occurred as a result of liquefaction disasters and recovery efforts that have been carried out with promising performance

Original languageEnglish
Title of host publication2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
EditorsIndra Hermawan, Muhammad Yusuf Bagus Rasyidin, Malisa Huzaifa, Iklima Ermis Ismail, Asep Taufik Muharram, Anggi Mardiyono, Noorlela Marcheeta, Dewi Kurniawati, Ade Rahma Yuly, Ariawan Andi Suhanda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages432-436
Number of pages5
ISBN (Electronic)9781728182476
DOIs
Publication statusPublished - 15 Sep 2020
Event3rd International Conference on Computer and Informatics Engineering, IC2IE 2020 - Depok, Indonesia
Duration: 15 Sep 202016 Sep 2020

Publication series

Name2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020

Conference

Conference3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
CountryIndonesia
CityDepok
Period15/09/2016/09/20

Keywords

  • convolutional neural network
  • disaster impact analysis
  • land cover classification
  • petobo liquefaction
  • resnet
  • spatial data analysis

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