A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Urban slum detection using texture and spatial metrics derived from satellite imagery
Slum detection from satellite imagery is challenging due to the variability in slum types and definitions. This research aimed at developing a method for slum detection based on the morphology of the built environment. The method consists of segmentation followed by hierarchical classification using object-oriented image analysis and integrating expert knowledge in the form of a local slum ontology. Results show that textural feature contrast derived from a grey-level co-occurrence matrix was useful for delineating segments of slum areas or parts thereof. Spatial metrics such as the size of segments and proportions of vegetation and built-up were used for slum detection. The percentage of agreement between the reference layer and slum classification was 60 percent. This is lower than the accuracy achieved for land cover classification (80.8 percent), due to large variations. We conclude that the method produces useful results and has potential for successful application in contexts with similar morphology.
Urban slum detection using texture and spatial metrics derived from satellite imagery
Slum detection from satellite imagery is challenging due to the variability in slum types and definitions. This research aimed at developing a method for slum detection based on the morphology of the built environment. The method consists of segmentation followed by hierarchical classification using object-oriented image analysis and integrating expert knowledge in the form of a local slum ontology. Results show that textural feature contrast derived from a grey-level co-occurrence matrix was useful for delineating segments of slum areas or parts thereof. Spatial metrics such as the size of segments and proportions of vegetation and built-up were used for slum detection. The percentage of agreement between the reference layer and slum classification was 60 percent. This is lower than the accuracy achieved for land cover classification (80.8 percent), due to large variations. We conclude that the method produces useful results and has potential for successful application in contexts with similar morphology.
Urban slum detection using texture and spatial metrics derived from satellite imagery
Kohli, Divyani (author) / Sliuzas, Richard / Stein, Alfred
2016
Article (Journal)
English
| Engineering Index Backfile | 1942
Defining the Bull'S Eye: Satellite Imagery-Assisted Slum Population Assessment in Hyderabad, India
| Online Contents | 2013
Urban Poverty, Spatial Representation and Mobility: Touring a Slum in Mexico
| Online Contents | 2012
Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data
| Online Contents | 2015