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M. McCutchan, S. Özdal-Oktay, I. Giannopoulos:
"Urban Growth Predictions with Deep Learning and Geosemantics";
Vortrag: VIENNA Young Scientists Symposium (VSS 2019), Wien; 13.06.2019 - 14.06.2019; in: "VIENNA Young Scientists Symposium (VSS 2019)", K. Ehrmann, H. Mansouri Khosravi et al. (Hrg.); Book-of-Abstracts.com, Gumpoldskirchen (2019), ISBN: 978-3-9504017-9-0; S. 30 - 31.

This work outlines a novel approach for the prediction of urban growth. The method extracts semantic information of geospatial data and predicts if urban and non-urban areas are going to change in the future, using a deep neural network. The scored prediction accuracy is higher than any other urban growth prediction model. This superiority is based on two novelties: (1) The effective modeling of the geospatial configurations using semantics, (2) the use of deep learning. The proposed method is therefore an effective tool to predict one of the global challenges of urban sprawl and support the future development strategies.

This work outlines a novel approach for the prediction of urban growth. The method extracts semantic information of geospatial data and predicts if urban and non-urban areas are going to change in the future, using a deep neural network. The scored prediction accuracy is higher than any other urban growth prediction model. This superiority is based on two novelties: (1) The effective modeling of the geospatial configurations using semantics, (2) the use of deep learning. The proposed method is therefore an effective tool to predict one of the global challenges of urban sprawl and support the future development strategies.

Deep learning, geosemantics, urban growth