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Deep learning aided web-based procedural modelling of LOD2 city models

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Abstract

In a large variety of smart city applications, the processes are settled with LOD2 (Level of Details) and the generation of the LOD2 models requires the proper generation of the roof geometries. In general, obtaining roof type information and succeeding generations of the LOD2 models requires expensive aerial surveys and time-consuming construction processes. In this study, a methodology to generate LOD2 building models using only 2D building footprints and aerial imagery is explained to overcome these challenges. The roof type information has been obtained from an aerial image that covers the entire study area using a CNN (Convolutional Neural Network) model. Then, the roof geometries have been constructed procedurally by extending and implementing a well-known Straight Skeleton (SS) algorithm for three main types of roofs: flat, gable and hipped. These constructed roof geometries have been combined with LOD1 block models generated by extruding the 2D footprints according to the height attribute. The overall accuracy of the CNN is 89.9% and the class-wise accuracies are over 84% for all classes. The least recall value is observed for the gable roof class, the enhancement options are discussed in the relevant section. The proposed methodology has been developed as a web-based solution utilizing RESTful web services with modern web technologies. In summary, the main novelty of the study is based on two contributions: using DL for gathering roof-type information without any end-user interference and the extension of the SS algorithm for the construction of roof geometries. The final product of this study is a web-based architecture for the rapid generation of the LOD2 building models.

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Data availability

The data and code used in this study are available at https://github.com/alpertungakin/Roof-Type-Classification.

Notes

  1. as used by Land registry Offices in Turkey.

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Acknowledgements

We would like to express our sincere gratitude to the Scientific and Technological Research Council of Türkiye (TUBITAK) for their support, their assistance has been instrumental in enabling the successful completion of this study.

Funding

This work was supported by The Scientific And Technological Research Council Of Türkiye (TUBITAK) with the grant ID of 118Y452.

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Authors and Affiliations

  1. Department of Geomatics Engineering, Artvin Coruh University, Artvin, 08100, Turkey

    Ziya Usta

  2. Department of Geomatics Engineering, Karadeniz Technical University, Trabzon, 61080, Turkey

    Alper Tunga Akın & Çetin Cömert

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  1. Ziya Usta
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  2. Alper Tunga Akın
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  3. Çetin Cömert
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Contributions

Ziya Usta and Alper Tunga Akın performed algorithmic development stages and prepared figures. Ziya Usta, Alper Tunga Akın and Çetin Cömert wrote the main article text.

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Correspondence to Ziya Usta.

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If the article is accepted by the Editor-in-chief after the review process, all authors consent to the manuscript being published in Earth Science Informatics. The work did not include human participants in order to obtain their consent.

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Communicated by: H. Babaie

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Usta, Z., Akın, A.T. & Cömert, Ç. Deep learning aided web-based procedural modelling of LOD2 city models. Earth Sci Inform 16, 2559–2571 (2023). https://doi.org/10.1007/s12145-023-01053-0

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