Geomatics and Environmental Engineering, vol. 16, no. 2

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Vol. 16 No. 2 (2022): Geomatics and Environmental Engineering

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Submitted
Jul 22, 2021
Accepted
Nov 26, 2021
Published
Feb 9, 2022
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This work is licensed under a Creative Commons Attribution 4.0 International License.

A Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo

https://doi.org/10.7494/geom.2022.16.2.39
Lamyaa Gamal El-deen Taha
National Authority for Remote Sensing and Space Sciences, Cairo, Egypt
https://orcid.org/0000-0003-2890-2498
Rania Elsayed Ibrahim
National Authority for Remote Sensing and Space Sciences, Cairo, Egypt
http://orcid.org/0000-0001-5116-5866

Corresponding Author(s) : Rania Elsayed Ibrahim

ranyaalsayed@gmail.com

Geomatics and Environmental Engineering, Vol. 16 No. 2 (2022): Geomatics and Environmental Engineering

Abstract

Building detection in Ashwa´iyyat is a fundamental yet challenging problem, mainly because it requires the correct recovery of building footprints from images with high-object density and scene complexity.
A classification model was proposed to integrate spectral, height and textural features. It was developed for the automatic detection of the rectangular, irregular structure and quite small size buildings or buildings which are close to each other but not adjoined. It is intended to improve the precision with which buildings are classified using scikit learn Python libraries and QGIS. WorldView-2 and Spot-5 imagery were combined using three image fusion techniques. The Grey-Level Co-occurrence Matrix was applied to determine which attributes are important in detecting and extracting buildings. The Normalized Digital Surface Model was also generated with 0.5-m resolution.
The results demonstrated that when textural features of colour images were introduced as classifier input, the overall accuracy was improved in most cases. The results show that the proposed model was more accurate and efficient than the state-of-the-art methods and can be used effectively to extract the boundaries of small size buildings. The use of a classifier ensample is recommended for the extraction of buildings.

Keywords

multi-source image fusion random forest support vector machine DEM extraction unplanned unsafe areas remote sensing
Taha, L. G. E.- deen, & Ibrahim, R. E. (2022). A Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo. Geomatics and Environmental Engineering, 16(2), 39–58. https://doi.org/10.7494/geom.2022.16.2.39
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