Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India

• Main Authors: Elena Ranguelova, Berend Weel, Debraj Roy, Monika Kuffer, Karin Pfeffer, Michael Lees
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-03-01
• Series: European Journal of Remote Sensing
• Subjects: Image segmentation; informal settlements; support vector machines; bag of visual words; speeded-up robust features
• Online Access: http://dx.doi.org/10.1080/22797254.2018.1535838

Slums, characterized by sub-standard housing conditions, are a common in fast growing Asian cities. However, reliable and up-to-date information on their locations and development dynamics is scarce. Despite numerous studies, the task of delineating slum areas remains a challenge and no general agreement exists about the most suitable method for detecting or assessing detection performance. In this paper, standard computer vision methods – Bag of Visual Words framework and Speeded-Up Robust Features have been applied for image-based classification of slum and non-slum areas in Kalyan and Bangalore, India, using very high resolution RGB images. To delineate slum areas, image segmentation is performed as pixel-level classification for three classes: Slums, Built-up and Non-Built-up. For each of the three classes, image tiles were randomly selected using ground truth observations. A multi-class support vector machine classifier has been trained on 80% of the tiles and the remaining 20% were used for testing. The final image segmentation has been obtained by classification of every 10th pixel followed by a majority filtering assigning classes to all remaining pixels. The results demonstrate the ability of the method to map slums with very different visual characteristics in two very different Indian cities.