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A Novel Framework for Road Data Surveying Based on Deep-learning and GIS

Authors:

S. Wanniarachchi ,

University of Moratuwa, LK
About S.
Urban Simulation Lab, Department of Town & Country Planning
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H. N. Kalpana,

University of Moratuwa, LK
About H. N.
Urban Simulation Lab, Department of Town & Country Planning
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A. Jayasinghe,

University of Moratuwa, LK
About A.
Department of Town & Country Planning
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S. Bandara

University of Moratuwa, LK
About S.
Department of Town & Country Planning
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Abstract

This paper proposes a novel framework for road data surveying and 3D urban visualisation based on deep-learning technologies, open data, and GIS techniques. Existing road inventory preparation methods are time-consuming, labour-intensive, inefficient, and lack an acceptable method for 3D urban visualisation in Sri Lanka. Thus, modern approaches are not applicable due to a lack of resources, technology, and financial capacity; the proposed framework will enable us to overcome these weaknesses.

 

The study comprised of three main stages: a literature review, development of the framework, and its validation in Ranna area. The framework was applied to two consecutive model validation events: the first related to the road data surveying model and the second to the 3D urban visualisation model. Its proposed returned KAPPA Accuracy Scores were 92% and 90% respectively.

 

The findings of this study further the use of cutting-edge deep learning and mapping techniques in transport and urban planning, making the preparation of road inventory surveys and 3D urban visualisations more cost-effective and efficient. Transport engineers, urban and regional planners, geographers, and GIS experts can employ the proposed framework for road data collection and 3D urban visualisation.

How to Cite: Wanniarachchi, S., Kalpana, H. N., Jayasinghe, A., & Bandara, S. (2022). A Novel Framework for Road Data Surveying Based on Deep-learning and GIS. Journal of South Asian Logistics and Transport, 2(1), 1–21. DOI: http://doi.org/10.4038/jsalt.v2i1.40
Published on 30 Mar 2022.
Peer Reviewed

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