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

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

Identyfikatory

DOI

10.7494/geom.2022.16.2.39

• 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.

Słowa kluczowe

EN

multi-source image fusion; random forest; support vector machine; DEM extraction; unplanned unsafe areas; remote sensing

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Geomatics and Environmental Engineering
• Rocznik: 2022
• Tom: Vol. 16, no. 2
• Strony: 39--59
• Opis fizyczny: Bibliogr. 53 poz., fot., rys., tab.

Twórcy

autor

Taha Lamyaa Gamal El-deen

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

• https://orcid.org/0000-0003-2890-2498

autor

Ibrahim Rania Elsayed

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

• https://orcid.org/0000-0001-5116-5866

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)