Journal of Geographical Sciences

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Comparative analysis of different machine learning algorithms for urban footprint extraction in diverse urban contexts using high-resolution remote sensing imagery

GUI Baoling, Anshuman BHARDWAJ, Lydia SAM

Journal of Geographical Sciences, 2025, 35(3): 664-696. DOI: 10.1007/s11442-025-2339-y

City	Urban core characteristics	Suburban characteristics	Green spaces	Challenges
London	Dense commercial and residential high-rise buildings, iconic landmarks	Suburbs have wider green belts compared to the city centre	Overall green space distribution is very regular and evenly spread	Complex road network and traffic congestion in central areas
Beijing	Dense centre with historical sites like the Forbidden City	Rapidly developing, with mixed residential, commercial, and industrial areas	Green space is irregular, with inconsistent building types and urban structure	Shadows from tall buildings and irregular city layout complicate urban classification
Kisangani	Densely populated informal settlements in the core	Scattered small buildings with large green spaces between them	Green spaces are extensive but fragmented, especially between scattered buildings	Lack of urban planning, rapid informal settlement growth, and infrastructure shortages
Phan Thiet	Blend of traditional and modern styles, compact city core	Suburban areas feature large barren spaces, with spectral similarities to urban areas	Sparse green spaces, with some parks and green belts for ecological balance	Spectral similarities of barren land and mountains interfere with UF extraction
New Delhi	Planned development with historical landmarks like India Gate	Suburban areas have extensive residential and commercial zones	Significant green spaces like Lodhi Gardens and Delhi Ridge	Rapid informal settlement growth and inconsistent green space integration
Porto Alegre	Historical core with compact residential and commercial sectors	Suburbs are a mix of residential and industrial areas	Clear distinction between urban and non-urban areas, with irregular small green spaces	Uneven green space distribution and irregular patches within the urban core
Mansouria	Densely populated core with a mix of formal and informal settlements	Peripheral developments have more green space integration	Sparse greenery in the central area, with improved green planning in new areas	Lack of greenery in the central areas and uncoordinated urban expansion
New York	Densely populated Manhattan with a grid layout	Outer boroughs have diverse residential and industrial areas	Central Park and other green spaces are widely distributed across suburban areas	Balancing green space with high-density urban development in the central city

Table 1 Detailed description of the characteristics of different cities

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Figure 1 Schematic representation of UF coverage (a. original image; b. UF coverage)
Figure 2 Map of the study area (eight cities across the world)
Figure 3 Flowchart of the operation of this experiment
Figure 4 Illustration of an example of sample selection for UF extraction (a-j represent the layout of different cities and the selection of samples. a and b correspond to Beijing, c to New Delhi, d to Mansouria, e to Porto Alegre, f and g to Kisangani, h and i to London, and j to New York.)
Figure 5 Segmentation results based on the Mean Shift algorithm (a. original image; b. segmentation result)
Figure 6 Schematic diagram of the classification principle of SVM
Figure 7 Kappa accuracy values for all images under different classification methods
Figure 8 Correlation analysis diagram of UF extraction accuracy (Kappa) with different algorithms
Figure 9 Pixel-based UF extraction Kappa precision numerical graph based on four traditional algorithms
Figure 10 a to h images of UF extraction results based on four traditional algorithms, both pixel-based and object-based. Among them, Image a and b are from Beijing, China, c and d are from New Delhi, India, e and f are from Mansouria, Egypt, and g and h are from Porto Alegre, Brazil.
Figure 11 The results of UF extraction based on four traditional algorithms, both pixel-based and object-based, are presented for images i through p. Among them, Image i and j from New York, USA, k and l from Kisangani, Sudan, m and n from London, UK, o and p from Phan Thiet, Vietnam.
Figure 12 Partial classification misclassification region visualization results (pixel-based)
Figure 13 Object-based Kappa precision numerical graph for UF extraction based on four traditional algorithms
Figure 14 Partial classification misclassification region visualization results (object-based)
Figure 15 Kappa precision numerical curve of UF extraction based on deep learning
Figure 16 a to h images of UF extraction results based on two deep learning algorithms. Among them, Image a and b are from Beijing, China, c and d are from New Delhi, India, e and f are from Mansouria, Egypt, and g and h are from Porto Alegre, Brazil. In the legend, 1 represents urban areas and 2 represents non-urban areas.
Figure 17 The results of UF extraction based on two deep learning algorithms, are presented for images i through p. Among them, Images i and j are from New York, USA, k and l from Kisangani, Sudan, m and n from London, UK, o and p from Phan Thiet, Vietnam. In the legend, 1 represents urban areas and 2 represents non-urban areas.
Figure 18 Comparison of UF extraction details between the two deep learning algorithms. Graph a shows that U-Net misclassifies some of the urban shadows, and graph b shows that DeeplabV3 misclassifies some of the smaller inner-city public green spaces. The circle represents the misjudged area.
Table 2 Summary of Kappa precision values for UF extraction based on SAM
Figure 19 SAM-based UF extraction result diagrams, the extracted urban areas show the original colour that distinguishes them from non-urban areas, with a blue border distinguishing the two types of environments. Among them, Images a and b are from Beijing, China, c and d are from New Delhi, India, e and f are from Mansouria, Egypt, and g and h are from Porto Alegre, Brazil. In the legend, i and j from New York, USA, k and l from Kisangani, Sudan, m and n from London, UK, o and p from Phan Thiet, Vietnam.
Table 3 A detailed comparative analysis of the different segmentation methods

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