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Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Srivastava Shivangi, Vargas Muñoz John E., Lobry Sylvain, Tuia Devis,
Project Multimodal machine learning for remote sensing information fusion
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Original article (peer-reviewed)

Journal International Journal of Geographical Information Science
Title of proceedings International Journal of Geographical Information Science
DOI 10.1080/13658816.2018.1542698

Open Access

URL https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1542698
Type of Open Access Publisher (Gold Open Access)

Abstract

© 2018, © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization.
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