Open AccessA Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo
Author(s) -
Lamyaa Gamal El-deen Taha,
Rania Ibrahim
Publication year - 2022
Publication title -
geomatics and environmental engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.144
H-Index - 3
eISSN - 2300-7095
pISSN - 1898-1135
DOI - 10.7494/geom.2022.16.2.39
Subject(s) - python (programming language) , computer science , digital surface , classifier (uml) , artificial intelligence , building model , grey level , pattern recognition (psychology) , computer vision , data mining , image (mathematics) , remote sensing , geography , simulation , lidar , operating system
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.