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Fitrianingsih Fitrianingsih
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Cloud Computing-Based U-Net Integration for Post-Landslide Satellite Image Segmentation Pratiwi , Swelandiah Endah; Asnur, Paranita; Fitrianingsih, Fitrianingsih; Senjaya, Remi; Nurdin, Muhammad Sahal
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5617

Abstract

Landslides are geological disasters that cause severe impacts on human life, infrastructure, and ecosystems, highlighting the need for post-disaster mapping methods that are fast, accurate, and scalable. This study aims to develop a post-landslide satellite image segmentation framework based on U-Net integrated with cloud computing to support large-scale and operational disaster mapping. While U-Net has been widely applied for landslide analysis, most existing studies focus on local-scale assessments or susceptibility mapping and lack integration with cloud-based pipelines and multi-source data for post-disaster operations. The novelty of this research lies not in modifying the U-Net architecture, but in integrating multi-source geospatial data, system workflow, and scalable cloud deployment. The proposed framework utilises a global multi-source dataset consisting of RGB imagery, Normalized Difference Vegetation Index (NDVI), slope, and elevation to enhance robustness and generalisation across diverse geomorphological conditions. Experimental results show stable model convergence with a final loss of 0.0357, an F1-score exceeding 0.75, and an AUC-PR of 0.8391. Evaluation on the testing dataset achieves a precision of 0.7692, recall of 0.7519, F1-score of 0.7604, and Intersection over Union of 0.6135. Qualitative analysis demonstrates strong spatial agreement between predicted segmentation and ground truth, with minor deviations mainly along complex slope boundaries. From an Informatics perspective, this study contributes by operationalizing deep learning through cloud computing to enable scalable computation, parallel processing, and system-level deployment, while providing object-level estimates of landslide events and affected areas to support disaster response and risk mitigation.
Co-Authors ., Elisma Afifah Azzahra Agusdini Banun Saptaningsih Agusriani, Agusriani Agustin, Ovi Amelia Amalia Nasution, Riska Aminuyati Andriani, Annisa Arimbi Kurniasari Azizah, Lailan Chytra Bertdiana Ersa Diah Tri Utami Diah Tri Utami Dinda Arum Carolina Dewi Dody Pernadi Dody Pernadi Elisma Elisma Elisma Elisma Em Yunir Emay Mastiani Erma Triawati Christina Ersa, Chytra Bertdiana Farida Titik Kristanti Fatnur Sani K Halimatussa’diyah Hernama Hernik Alfia Anjani Indriyani, Desti Irayani, Tri Lestari Octavia M. Rifqi Efendi Maharani, Ni Wayan Devina Maharini , Indri Maharini, Indri Maimum Maimum Maman Suherman maulana, Ade Hilman Mia Prajuwita Muhammad Syukri Munawir Nasruloh, Ujang Neldi, Vina Novia Tri Astuti Nugraha, Rahmat Mulya Nurdin, Muhammad Sahal Nurfadilah Nurfadilah Patriya, Eka Pondawinata, Marizki Pratiwi , Swelandiah Endah Puspa Dwi Pratiwi Rachmawati, Lilis Rani Sauriasari Rela Sonia Remi Senjaya Revis Asra Rini Arianty Runtao, Zha Sadli, Nurul Kamilah Sani K, Fathnur Sania, Gina Santi Perawati, Santi Setiaji, Dini Anggraeni Saputri Sonia, Rela Sri Hayuningsih Sri Hermawati Susetianingtias, Diana Tri Syamsurizal Syamsurizal - - Tissa Maharani Tutik Ekasari Uce Lestari Utami, Diah Tri Utami, Karen Putri Wirdayanti Wirdayanti Yuliandani, Yuliandani Yuliawati Yuliawati Yuliawati Yuliawati , Yuliawati Yuliawati Yuliawati Yusnelti Yusnelti Yusnelti Yusnelti Yusup, Aldian