The organisers and the GIScience Research Group (GIScRG) of the Royal Geographical Society invite authors to submit 250-word abstracts for presentation at the Deep learning approaches in GIScience session at the RGS-IBG Annual International Conference 2020.

Instructions for Authors

Please submit abstracts of no more than 250 words (excluding references) for 15-minute presentations to before January 31st, 2020.



In its broader definition, machine learning has long been part of GIScience and geocomputation approaches to data analysis. That is primarily due to research on unsupervised learning approaches to geographic data mining, such as geodemographic classification (Delmelle, 2016; Gale et al., 2016) and dimensionality reduction (Wolf and Knaap, 2019), and supervised methods of inference from a sample, such as spatial autocorrelation (Ord and Getis, 1995) and geographically weighted regression (Brunsdon et al., 1998; Yu et al., 2019). More recently, deep machine learning approaches had a transformative impact in a variety of fields. For instance, the introduction of AlexNet (Krizhevsky et al., 2012) was a watershed moment in image processing, demonstrating the effectiveness of the use of convolutional neural networks (CNN) in the field.

Deep machine learning approaches have been somewhat neglected in GIScience and quantitative human geography (Harris et al., 2017) until quite recently. However, there is a clear interest in exploring the applicability and effectiveness of deep learning to study geographic phenomena and growing literature in the field. To mention a few: Chen et al. (2018) have been exploring the use of CNNs to identify ground objects from satellite images; De Sabbata and Liu (2019) explored a geodemographic classification approach based on deep embedding clustering; Liu and De Sabbata (2019) proposed a semi-supervised, deep neural network approach to classify geolocated social media posts; Xu et al. (2017) proposed the use of deep autoencoders to perform quality assessment of building footprints for OpenStreetMap.

This session aims to be a forum to discuss advances, opportunities, and limits of the use of deep machine learning approaches in the field of GIScience, showcasing both applications of deep learning methods applied to geography – including both human and physical geography contexts – and geospatial extensions and variants of deep learning methods.


Brunsdon, C., Fotheringham, S. and Charlton, M. (1998). Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician), 47(3), pp.431-443.

Chen, J., Y. Zhou, A. Zipf and H. Fan (2018). Deep Learning from Multiple Crowds: A Case Study of Humanitarian Mapping. IEEE Transactions on Geoscience and Remote Sensing (TGRS). 1-10.

De Sabbata, S. and Liu, P. (2019). Deep learning geodemographics with autoencoders and geographic convolution. In Proceedings of the 22nd AGILE conference on Geographic Information Science, Limassol, Greece.

Delmelle, E.C. (2016). Mapping the DNA of urban neighborhoods: clustering longitudinal sequences of neighborhood socioeconomic change. Annals of the American Association of Geographers, 106(1), pp.36-56.

Gale, C.G., Singleton, A., Bates, A.G. and Longley, P.A. (2016). Creating the 2011 area classification for output areas (2011 OAC). Journal of Spatial Information Science, 12, pp.1-27.

Harris, R., O’Sullivan, D., Gahegan, M., Charlton, M., Comber, L., Longley, P., Brunsdon, C., Malleson, N., Heppenstall, A., Singleton, A. and Arribas-Bel, D. (2017). More bark than bytes? Reflections on 21+ years of geocomputation. Environment and Planning B: Urban Analytics and City Science, 44(4), pp.598-617.

Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).

Liu, P. and De Sabbata, S. (2019). Learning Digital Geographies through a Graph-Based Semi-supervised Approach. In Proceedings of the 15th International Conference in GeoComputation. Queenstown, New Zealand.

Ord, J.K. and Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical analysis, 27(4), pp.286-306.

Wolf, L. J., & Knaap, E. (2019). Learning Geographical Manifolds: A Kernel Trick for Geographical Machine Learning. SocArXiv.

Xu, Y., Chen, Z., Xie, Z. and Wu, L. (2017). Quality assessment of building footprint data using a deep autoencoder network. International Journal of Geographical Information Science, 31(10), pp.1929-1951.

Yu, H., Fotheringham, A.S., Li, Z., Oshan, T., Kang, W. and Wolf, L.J. (2019). Inference in multiscale geographically weighted regression. Geographical Analysis.