TY - GEN
T1 - Disaster Impact Analysis Uses Land Cover Classification, Case study
T2 - 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
AU - Hidayat, Rachmat
AU - Arymurthy, Aniati Murni
AU - Dewantara, Dimas Sony
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/15
Y1 - 2020/9/15
N2 - 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
AB - 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
KW - convolutional neural network
KW - disaster impact analysis
KW - land cover classification
KW - petobo liquefaction
KW - resnet
KW - spatial data analysis
UR - http://www.scopus.com/inward/record.url?scp=85098987945&partnerID=8YFLogxK
U2 - 10.1109/IC2IE50715.2020.9274573
DO - 10.1109/IC2IE50715.2020.9274573
M3 - Conference contribution
AN - SCOPUS:85098987945
T3 - 2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
SP - 432
EP - 436
BT - 2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
A2 - Hermawan, Indra
A2 - Rasyidin, Muhammad Yusuf Bagus
A2 - Huzaifa, Malisa
A2 - Ermis Ismail, Iklima
A2 - Muharram, Asep Taufik
A2 - Mardiyono, Anggi
A2 - Marcheeta, Noorlela
A2 - Kurniawati, Dewi
A2 - Yuly, Ade Rahma
A2 - Suhanda, Ariawan Andi
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 September 2020 through 16 September 2020
ER -