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GIScience News Blog » Blog Archive » Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning

Determining the location of a building’s entrance is crucial to location-based services, such as wayfinding for pedestrians. Unfortunately, entrance information is often missing from current mainstream map providers. Frequently, automatic approaches for detecting building entrances are based on street-level images that are not widely available. To address this issue, in a recently published paper in IJGIS we propose a more general approach for inferring the main entrances of public buildings based on the association between spatial elements extracted from OpenStreetMap.
In particular, we adopt three binary classification approaches, weighted random forest, balanced random forest, and smooth-boost to model the association relationship. There are two types of features considered in the classification: intrinsic features derived from building footprints and extrinsic features derived from spatial contexts, such as roads, green spaces, bicycle parking areas, and neighboring buildings. We conducted extensive experiments on 320 public buildings with an average perimeter of 350 m. The experimental results showed that the locations of building entrances estimated by the weighted random forest and balanced random forest models have a mean linear distance error of 21 m and a mean path distance error of 22 m, ruling out 90% of the incorrect locations of the main entrance of buildings. Further details can be found in https://www.tandfonline.com/doi/abs/10.1080/13658816.2020.1861282?journalCode=tgis20

Hu, X., Noskov, A., Fan, H., Novack, T., Gu, F., Li, H., Shang, J.: Tagging the Buildings’ Main Entrance based on OpenStreetMap and Binary Imbalanced Learning. International Journal of Geographical Information Science, DOI: 10.1080/13658816.2020.18

Related Publications:

Hu, X., Fan, H., Noskov, A., Wang, Z., Zipf, A., Gu, F., Shang, J. (2020). Room Semantics Inference Using Random Forest and Relational Graph Convolutional Network: A Case Study of Research Building. Transactions in GIS. https://doi.org/10.1111/tgis.12664

Hu, X., Ding, L., Shang, J., Fan, H., Novack, T., Noskov, A., Zipf, A. (2019). A Data- driven Approach to Learning Saliency Model of Indoor Landmarks by Using Genetic Programming. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2019.1701109

Selected earlier work:

Goetz, M. & Zipf, A. (2013): The Evolution of Geo-Crowdsourcing: Bringing Volunteered Geographic Information to the Third Dimension. In: Sui, D.Z., Elwood, S. & Goodchild, M.F. (eds.): Crowdsourcing Geographic Knowledge. Volunteered Geographic Information (VGI) in Theory and Practice. Berlin: Springer. 2013, XII, 396 pp. 139-159.

Goetz, M. & Zipf, A. (2012): OpenStreetMap in 3D – Detailed Insights on the Current Situation in Germany. AGILE 2012. Avignon, France.

Chen, J & Zipf, A. (2017): DeepVGI: Deep learning with volunteered geographic information. Proceedings of the 26th International Conference on World Wide Web Compagnon. Pages 771-772. https://doi.org/10.1145/3041021.3054250

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