Deep Learning for Classification Tasks on Geospatial Vector Polygons
This addresses the challenge of automating geospatial data analysis for researchers and practitioners, but it is incremental as it builds on existing deep learning techniques.
The paper tackled the problem of classifying geospatial vector polygons by evaluating deep learning models that learn directly from geometry coordinates, showing they are competitive with traditional methods using extracted features.
In this paper, we evaluate the accuracy of deep learning approaches on geospatial vector geometry classification tasks. The purpose of this evaluation is to investigate the ability of deep learning models to learn from geometry coordinates directly. Previous machine learning research applied to geospatial polygon data did not use geometries directly, but derived properties thereof. These are produced by way of extracting geometry properties such as Fourier descriptors. Instead, our introduced deep neural net architectures are able to learn on sequences of coordinates mapped directly from polygons. In three classification tasks we show that the deep learning architectures are competitive with common learning algorithms that require extracted features.