Deep Learning for Classification Tasks on Geospatial Vector Polygons

The ability to analyse vector shapes of geospatial objects is useful for many tasks, such as quality assessment or enrichment of map data (Fan et al, 2014) or the classification of topographical objects (Keyes and Winstanley, 1999). An increasingly more common method for shape analysis is through machine learning. For example, machine learning can be applied to assess correct building types (Xu et al, 2017) or classify road sections (Andrášik and Bíl, 2016). The prediction of house prices (Montero et al, 2018) and the estimation of pedestrian side walk widths (Brezina et al, 2017) are tasks that could possibly also benefit from the application of machine learning analysis on geometric shapes. Current machine learning methods applied to geospatial vector data rely on extracting information from a geometry that characterizes its shape. This preprocessing step is known in machine learning as feature extraction (LeCun et al, 2015, 438) or feature engineering (Domingos, 2012, 84).