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All Journal Geoplanning : Journal of Geomatics and Planning Jurnal Pengembangan Kota GEA, Jurnal Pendidikan Geografi Jurnal Penelitian Karet Journal of Applied Geospatial Information Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH GEOMATIKA Jurnal Geografi Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Warta Perkaretan Sriwijaya Bioscientia
Muhammad Kamal
Departemen Sains Informasi Geografi Fakultas Geografi Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Education Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Social Sciences Other

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Multi-Class Semantic Segmentation of Oil Palm Areas Using a VGG-19 U-Net Improvement Maura Widyaningsih; Tri Kuntoro Priyambodo; Moh Edi Wibowo; Muhammad Kamal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7062

Abstract

UAV imagery-based semantic segmentation is crucial for mapping tropical agricultural areas such as oil palm plantations. The main challenges are overlapping vegetation objects, unclear boundaries, and spectral similarities between classes, which reduce the accuracy of conventional models. This study proposes a modified U-Net architecture with a VGG-19 backbone, achieved through hyperparameter tuning (M7) and the integration of residual blocks (M8), to enhance multi-class segmentation performance. Experiments were conducted on aerial imagery with two resolutions (512×512 and 256×256) using four-class and three-class scenarios. The results show that M7 and M8 consistently outperform the baseline model (M2) in terms of accuracy, precision, recall, and average Intersection over Union (IoU). In the 512x512 four-class scenario, M8 achieved the highest accuracy (87.40%), precision (88.32%), recall (86.32%), and MIoU (0.132). M7 reached similar accuracy (>86%) but trained significantly faster than the baseline. In the 256x256 scenario, M8 maintained strong performance with 86.44% accuracy and 0.302 MIoU. For the three-class experiment, M8 reached a top MIoU of 0.178. Accuracy, precision, and recall were all above 87%, showing improved recognition of minority classes such as waterways. Confusion matrix analysis confirmed that M8 provided more balanced class predictions. It also reduced false negatives for oil palm vegetation. M7 showed slight fluctuations, suggesting possible overfitting. These findings support M8 as a robust solution for UAV-based oil palm mapping and large-scale monitoring.
Co-Authors Aan Dianto Basmala Asliha Afida Danang Sri Hadmoko Dian Utari Gathot Winarso Hartono Hartono Iqbal Arrahman Iwan Ridwansyah Jamin Saputra Jamin Saputra Jatna Supriatna Luki Subehi Maulana Abdul Hakim Maura Widyaningsih Moh Edi Wibowo Muhammad Sufwandika Wijaya Novi Susetyo Adi Pamaditya Wicaksono Pramaditya Wicaksono Pramaditya Wicaksono Pramaditya Wicaksono Prima Widayani Putu Wirabumi Rima Hermayani Rosid, Mohammad Syamsu Sanjiwana Arjasakusuma Sanjiwana Arjasakusuma Tito Kanekaputra Tri Kuntoro Priyambodo Wikanti Asriningrum