Cloud Computing-Based U-Net Integration for Post-Landslide Satellite Image Segmentation

Swelandiah Endah  Pratiwi; Paranita Asnur; Fitrianingsih Fitrianingsih; Remi  Senjaya; Muhammad Sahal Nurdin

doi:10.52436/1.jutif.2026.7.2.5617

Authors

Swelandiah Endah Pratiwi Computer Engineering Study Program, Directorate of Information Technology Diploma, Gunadarma University, Depok, Indonesia
Paranita Asnur Agrotechnology Study Program, Faculty of Agricultural Industrial Technology, Gunadarma University, Depok, Indonesia
Fitrianingsih Diploma Program in Informatics Management, Universitas Gunadarma, Jakarta, Indonesia
Remi Senjaya Information Systems Study Program, Faculty of Computer Science, Gunadarma University, Depok, Indonesia
Muhammad Sahal Nurdin Information Systems Study Program, Faculty of Computer Science, Gunadarma University, Depok, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5617

Keywords:

Big data analytics, Disaster mitigation, Multispectral imagery, Semantic segmentation, Spatial generalization

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.

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