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 significant losses and negative impacts due to forest and land fires cause the need for an effort to prevent forest and land fires from an early stage. Therefore doing prevention before a disaster occurs has a low cost and smaller losses compared to efforts to manage disasters that have already occurred. The Remote Sensing Applications Center-LAPAN has produced fire hazard rating information. Information on fire hazard ratings which are up to date, fast and precise can be used to monitor forestry resources. The development of an accountable monitoring system can be a reference in efforts to control forest and land fires. At the same time participate in maintaining the sustainability of Indonesia’s natural resources sustainably. The development of an Android-based information dissemination system was created to facilitate the dissemination of information on fire hazard ratings to the public. This android-based information system provides information about the hazard rating of forest/land fires and analyzes the potential for forest/land fires and their spread through smartphones. It can be used to support decisionmaking to make strategic steps in preventing forest/land fires in Indonesia more widely.