The amount of the available geospatial data grows at an ever faster pace. This leads to the constantly increasing demand for processing power and storage in order to provide data analysis in a timely manner. At the same time, a lot of geospatial processing is visual and exploratory in nature, thus having bounded precision requirements. We present DeepSPACE, a deep learning-based approximate geospatial query processing engine which combines modest hardware requirements with the ability to answer flexible aggregation queries while keeping the required state to a few hundred KiBs.
@article{arxiv.1906.06085,
title = {DeepSPACE: Approximate Geospatial Query Processing with Deep Learning},
author = {Dimitri Vorona and Andreas Kipf and Thomas Neumann and Alfons Kemper},
journal= {arXiv preprint arXiv:1906.06085},
year = {2019}
}