Anjar Dimara Sakti, Kamal Nur Fauzan, Cokro Santoso, Hazel Yordan Komara, Kurnia Putri Adillah, Dzun Nurwinas Saepudin, Tania Septi Anggraini, Bimastyaji Surya Ramadan, Siti Sendari, Beata Ratnawati
Landfill fires pose severe environmental and public health risks, particularly in developing regions where waste accumulation is poorly managed. This study develops a multi-sensor machine-learning model for monitoring open landfill burning, using the 2023 Sarimukti Landfill fire in West Bandung, Indonesia, as a case study. UAV imagery and Sentinel-2 data were integrated into map fire-affected zones using Random Forest (RF) and Gradient Tree Boosting (GTB) algorithms. The model achieved high classification performance, with overall accuracies of 80.55% and 81.31% for two UAV datasets and high cross-validated AUC values that were comparable across classifiers and across the UAV-only and integrated inputs. A fire-mitigation priority model was then developed by combining the Burned Severity Index (BSI) and a Digital Surface Model (DSM) to evaluate the influence of elevation on fire persistence. The current study is limited to a single landfill site; future work should extend the model to multiple landfill locations and incorporate additional variables, such as thermal and meteorological data, to improve generalizability. These findings highlight the potential of UAV-satellite integration and machine learning for rapid fire monitoring and risk assessment in waste-management systems. © 2026 Elsevier Ltd
Geographic Information Sciences and Technology Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Indonesia; Center for Remote Sensing, Institut Teknologi Bandung, Indonesia; Geospatial Information Agency of Indonesia, Cibinong, Indonesia; Geographic Information Sciences, Faculty of Social Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia; Environmental Sustainability Research Group, Department of Environmental Engineering, Faculty of Engineering, Universitas Diponegoro, Semarang, Indonesia; Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Malang, Malang, Indonesia; Program Study of Engineering and Environmental Management, Vocational School, Institut Pertanian Bogor, Bogor, Indonesia
