@inproceedings{e48061298062448a9847b4e868dda9d6,
title = "DeepSPACE: Approximate geospatial query processing with deep learning",
abstract = "The amount of available geospatial data grows at an ever faster pace. This leads to a 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.",
keywords = "Approximate query processing, Deep learning, Geospatial",
author = "Dimitri Vorona and Andreas Kipf and Thomas Neumann and Alfons Kemper",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright held by the owner/author(s).; 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 ; Conference date: 05-11-2019 Through 08-11-2019",
year = "2019",
month = nov,
day = "5",
doi = "10.1145/3347146.3359112",
language = "English",
series = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems",
publisher = "Association for Computing Machinery",
pages = "500--503",
editor = "Farnoush Banaei-Kashani and Goce Trajcevski and Guting, {Ralf Hartmut} and Lars Kulik and Shawn Newsam",
booktitle = "27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019",
}