Rapid urban population growth in Bandung City has led to the development of slums due to inadequate housing facilities and urban planning. However, it remains unclear how these slums are distributed and evolve spatially and temporally. Therefore, it is necessary to map their distribution and trends effectively. This study aimed to classify slum areas in Bandung City using a machine learning-based local knowledge approach; this classification exercise contributes towards Sustainable Development Goal 11 related to sustainable cities and communities. The methods included settlement and commercial/industrial classification from 2021 SPOT-6 satellite data by the Random Forest classifier. A knowledge-based classifier was used to derive slum and non-slum settlements from the settlement and commercial/industrial classification, as well as railway, river, and road buffering. Our findings indicate that these methods achieved an overall accuracy of 82%. The producer’s accuracy for slum areas was 70%, while the associated user’s accuracy was 92%. Meanwhile, the Kappa coefficient was 0.63. These findings suggest that local knowledge could be a potent option in the machine learning algorithm.

The disposal of waste materials such as plastic, foam, cloth, paper, rubber, and wood in river areas significantly impacts local environments, affecting regions several kilometers away and even reaching the open sea. This study utilized data from the Sentinel-1 C-SAR spanning 2017–2024 to detect water bodies. Additionally, an adaptive decision tree algorithm method with the Otsu threshold was applied to the Sentinel-2 optical data of March 19, 2024, to identify aquatic vegetation and floating waste (non-vegetative objects) within the water body. The study focused on the inlet of the Saguling Reservoir, where the Citarum River flows near the Bridge of Babakan Sapan (BBS) in West Java Province. This location is known for frequent waste accumulation. The methodology was then employed to monitor accumulated waste along the Citarum River on June 17, 22, and July 12, 2024. The model demonstrated an overall accuracy of 86% and a Kappa coefficient of 79% in distinguishing waste, water, and vegetation, with most errors occurring in the vegetation and waste classes. Additionally, the model's effectiveness in detecting waste in narrow water bodies with densely populated buildings on both sides, as observed in a narrow river along slum areas in Cirebon City, was comparatively lower.