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

• Autorzy: Lamyaa Gamal El-deen Taha , Rania Elsayed Ibrahim
• Języki publikacji: EN

Abstrakty

EN

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. (original abstract)

Słowa kluczowe

EN

Flight photos; Geodesy; Machine learning; Cartography

PL

Fotografia lotnicza; Geodezja; Uczenie maszynowe; Kartografia

• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2022
• Numer: nr 16/2
• Strony: 39--58

Twórcy

autor

Lamyaa Gamal El-deen Taha

• National Authority for Remote Sensing and Space Sciences, Cairo, Egypt

autor

Rania Elsayed Ibrahim

• National Authority for Remote Sensing and Space Sciences, Cairo, Egypt

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Identyfikatory

DOI

10.7494/geom.2022.16.2.39