Machine Learning-Based Local Knowledge Approach to Mapping Urban Slums in Bandung City, Indonesia

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...

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Bibliographic Details
Published in:Urban science 2024-10, Vol.8 (4), p.189
Main Authors: Chulafak, Galdita Aruba, Khomarudin, Muhammad Rokhis, Roswintiarti, Orbita, Mehmood, Hamid, Nugroho, Gatot, Nugroho, Udhi Catur, Ardha, Mohammad, Sukowati, Kusumaning Ayu Dyah, Putra, I Kadek Yoga Dwi, Permana, Silvan Anggia Bayu Setia
Format: Article
Language:English
Subjects:
Accuracy
Cities
Classification
Ghettos
Housing
Infrastructure
Land settlement
Living conditions
Local knowledge
Low income groups
Machine learning
Mapping
Population density
Population growth
Poverty
Redevelopment
Remote sensing
SDG 11
slum
Slums
Spatial data
Sustainability
sustainable cities and communities
Sustainable development
Urban areas
Urban planning
Urban population
Waste management
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Summary: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.
ISSN:2413-8851
2413-8851
DOI:10.3390/urbansci8040189