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Building footprint extraction and counting on very high-resolution satellite imagery using object detection deep learning framework

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Abstract

Building footprints are the most visible features in urban areas. Detecting building footprint has a substantial position in decision-making problems such as city planning and development, urban mapping and management, population estimation, etc. In this paper, we aim to automatically detect and count building footprints by leveraging deep learning techniques and the potential availability of remote sensing datasets at high spatial resolutions. We build object detection deep learning models by integrating the partitioning segmentation and convolutional neural networks (CNN) architecture using the WorldView-3 very high-resolution optical satellite imagery data. Our detection model is implemented on data from the Paris, France, and Khartoum, Sudan regions. These regions were chosen because it has very different characteristics and large data variation, making it challenging to extract building features from satellite imagery. This challenge is even greater because one area is taken perpendicularly and the other area is taken at an angle of several degrees which causes more noise, one of which is the shadow of the building. The detection models can detect and count the building footprints and have promising performance on large variable data with an Average Precision (AP) of 64.19%. Paris was detected better by 74.66% compared to Khartoum by 56.19%. The partitioning segmentation technique is used to tune the anchor boxes of CNN input images. Our result offers a potential practical implementation for rapid yet accurate estimation of urban monitoring and city planning, particularly in metropolitan regions.

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Data availability

Data derived from public domain resources, i.e. https://spacenet.ai/datasets/.

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Acknowledgements

The authors are grateful to acknowledge the support from Statistics Indonesia (BPS) and Politeknik Statistika STIS, and would like to thank for Van Etten and DigitalGlobe for providing the satellite imagery very high-resolution WorldView-3 for the building footprint mapping challenge (SpaceNet at Amazon Web Services (AWS). “Datasets.” The SpaceNet Catalog. Last modified October 1st, 2018. Accessed on [December 18th, 2021]. https://spacenet.ai/datasets/)

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Authors and Affiliations

  1. Department of Statistical Computing, Politeknik Statistika STIS, Jakarta, Indonesia

    Wahidya Nurkarim & Arie Wahyu Wijayanto

  2. Statistics Indonesia (BPS), Jakarta, Indonesia

    Arie Wahyu Wijayanto

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  1. Wahidya Nurkarim
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  2. Arie Wahyu Wijayanto
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Contributions

All authors contributed to the study conception, design and analysis. Data collection, experimental simulations, and writing of original draft preparation were performed by Wahidya Nurkarim. Selecting the methodology, supervising the project, review and editing the manuscript were performed by Arie Wahyu Wijayanto. All authors read and approved the final manuscript.

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Correspondence to Arie Wahyu Wijayanto.

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The authors declare that they have no known competing financial or personal relationships that could have appeared to influence the work reported in this paper.

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Communicated by: H. Babaie

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Nurkarim, W., Wijayanto, A.W. Building footprint extraction and counting on very high-resolution satellite imagery using object detection deep learning framework. Earth Sci Inform 16, 515–532 (2023). https://doi.org/10.1007/s12145-022-00895-4

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