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ISPRS Int. J. Geo-Inf. 2018, 7(4), 147; https://doi.org/10.3390/ijgi7040147

Foreword to the Special Issue on Machine Learning for Geospatial Data Analysis

1
Department of Civil, Environmental and Geomatic Engineering, Institute of Geodesy and Photogrammetry, ETH Zürich, Stefano-Franscini-Platz 5, 8093 Zürich, Switzerland
2
Remote Sensing Group, IGG, University of Bonn, Nussallee 15, 53115 Bonn, Germany
3
Swiss Data Science Center, ETH Zürich, Universitätstrasse 25, 8006 Zürich, Switzerland
4
Crop and Environmental Sciences Department, Harper Adams University, Edgmond, Newport TF10 8NB, UK
*
Author to whom correspondence should be addressed.
Received: 9 April 2018 / Revised: 9 April 2018 / Accepted: 10 April 2018 / Published: 13 April 2018
(This article belongs to the Special Issue Machine Learning for Geospatial Data Analysis)
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Abstract

Advances in machine learning research are pushing the limits of geographical information sciences (GIScience) by offering accurate procedures to analyze small-to-big GeoData. This Special Issue groups together six original contributions in the field of GeoData-driven GIScience that focus mainly on three different areas: extraction of semantic information from satellite imagery, image recommendation, and map generalization. Different technical approaches are chosen for each sub-topic, from deep learning to latent topic models. View Full-Text
Keywords: geospatial machine learning; big data; classification; remote sensing; GIS; GIScience geospatial machine learning; big data; classification; remote sensing; GIS; GIScience
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wegner, J.D.; Roscher, R.; Volpi, M.; Veronesi, F. Foreword to the Special Issue on Machine Learning for Geospatial Data Analysis. ISPRS Int. J. Geo-Inf. 2018, 7, 147.

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