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All Journal International Journal of Electrical and Computer Engineering MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) JUTI: Jurnal Ilmiah Teknologi Informasi Sinergi Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat Publication Library and Information Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control LibTech : Library and Information Science Journal Jurnal Riset Mahasiswa Matematika Engagement: Jurnal Pengabdian Kepada Masyarakat Journal of Research on Community Engagement
Hisyam Fahmi
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences

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Patch based Classification using ResNet for Land Cover changes detection of Batu City Hisyam Fahmi
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 14, No 2 (2022): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v14i2.20946

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.
Co-Authors Abdul Aziz Achmad Nizar Hidayanto Ahmad Abtokhi Ahmad Saikhu Angga Dwi Mulyanto Aniati Murni Arymurthy Ari Kusumastuti Bella Nafa Savitri Devi Fitrianah Devi Fitrianah Devi Fitrianah Elnaz Putri, Hilda Zaqya Erna Herawati, Erna Evawati Alisah Farrah Nurmalia Sari Habibullah, Muhamad Hamdani Heni Widayani Mohammad Nafie Jauhari Mufidah, Hanifatul Muhammad Khudzaifah Muhammad Khudzaifah Musataklima Noor Rizqiya Fimaulidina Pang Ning-Tan Prayugo, Mohammad Bagus Dimas Puji Lestari Ridwan Riyan Fahmi Syihabuddin Sari, Farrah Nurmalia Savitri, Bella Nafa Shalih Husni Syihabuddin, Riyan Fahmi Syurifah, Gita Ramadhani Wardatus Wina Permana Sari