Patch based classification using ResNet for land cover changes detection of Batu City

Fahmi, Hisyam ORCID: https://orcid.org/0000-0002-2665-1536 (2022) Patch based classification using ResNet for land cover changes detection of Batu City. Matics: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology), 14 (2). pp. 64-69. ISSN 24772550

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Abstract

The purpose of this study is to analyze the variations in land cover in Batu City, East Java Province, Indonesia, utilizing a patch-based classification strategy and deep learning. This study provides a preliminary estimation of land cover change in Batu City. The research also highlights the possibility of using deep learning techniques to analyze land use and land cover (LULC) variations in other urban areas with greater precision and efficiency. The EuroSAT dataset is used to train a classification model for patch labeling using the ResNet-50 architecture. Comparing the land cover of Batu City in 2001 and 2022 allows us to detect LULC changes, with almost 50% of the patch changing. The results indicate that ‘Housing’ and ‘Road’ become the most changed categories, while the vegetation areas decrease in number. The results demonstrate that the ResNet-50 architecture is capable of classifying patches and detecting LULC changes with an accuracy of 88% and an execution time of approximately 126.53 seconds.

Item Type:	Journal Article
Keywords:	Batu City; land cover and land use changes; patch-based classification; ResNet-50
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified 17 PSYCHOLOGY AND COGNITIVE SCIENCES > 1702 Cognitive Sciences > 170203 Knowledge Representation and Machine Learning
Divisions:	Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User:	Hisyam Fahmi
Date Deposited:	17 Apr 2023 11:07

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