Extracting built-up areas from spectro-textural information using machine learning

Maqsoom, Ahsen and Aslam, Bilal and Yousafzai, Arbaz and Ullah, Fahim ORCID: https://orcid.org/0000-0002-6221-1175 and Ullah, Sami and Imran, Muhammad (2022) Extracting built-up areas from spectro-textural information using machine learning. Soft Computing. ISSN 1432-7643


Abstract

Extraction of built-up area (BUA) is essential for proper city planning and management. It enables the concerned authorities to formulate better city development policies and manage emergent disasters. However, the traditionally used optical data present spectral confusion where BUAs are mixed with other features adding to management complexities. Therefore, an advanced automated method is required to extract the spectral and textural features from satellite data for the pattern recognition of BUA. Landsat-8 Operational Land Imager (OLI) has been used in the current study to identify the pattern and extract BUA of Gujranwala, Pakistan. First, eight textural features based on the gray-level co-occurrence matrix (GLCM) are selected and combined with multispectral data. Then, feature selection methods are applied to select optimal features used to train the proposed support vector machine (SVM) classifier. Finally, the results from SVM classifiers are compared with k-nearest neighbor (k-NN) and backpropagation neural network (BP-NN) to highlight any improvements in results. The comparisons show that the proposed approach increases the overall accuracy of linear-SVM by 8.41%, radial basis function SVM by 8.3%, BP-NN by 7.63%, and k-NN by 6.6%. This can help city managers and planners to extract critical BUA information in otherwise unplanned and rapidly expanding cities to move toward smart and sustainable cities.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Surveying and Built Environment (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Surveying and Built Environment (1 Jan 2022 -)
Date Deposited: 28 Feb 2022 23:55
Last Modified: 01 Mar 2022 00:11
Uncontrolled Keywords: built-up areas; support vector machine; spectro-textural information; pattern recognition; city planning
Fields of Research (2008): 08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
12 Built Environment and Design > 1299 Other Built Environment and Design > 129999 Built Environment and Design not elsewhere classified
10 Technology > 1099 Other Technology > 109999 Technology not elsewhere classified
12 Built Environment and Design > 1205 Urban and Regional Planning > 120505 Regional Analysis and Development
Fields of Research (2020): 33 BUILT ENVIRONMENT AND DESIGN > 3399 Other built environment and design > 339999 Other built environment and design not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
33 BUILT ENVIRONMENT AND DESIGN > 3304 Urban and regional planning > 330408 Strategic, metropolitan and regional planning
33 BUILT ENVIRONMENT AND DESIGN > 3302 Building > 330201 Automation and technology in building and construction
46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460199 Applied computing not elsewhere classified
40 ENGINEERING > 4013 Geomatic engineering > 401302 Geospatial information systems and geospatial data modelling
Identification Number or DOI: https://doi.org/10.1007/s00500-022-06794-6
URI: http://eprints.usq.edu.au/id/eprint/46991

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