Name
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3347146.3359112 | SIGSPATIAL/GIS |
Keywords | DocType | Volume |
geospatial, deep learning, approximate query processing | Conference | abs/1906.06085 |
ISBN | Citations | PageRank |
978-1-4503-6909-1 | 3 | 0.39 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitri Vorona | 1 | 9 | 3.23 |
| Andreas Kipf | 2 | 32 | 11.03 |
| Thomas Neumann | 3 | 2523 | 156.50 |
| Alfons Kemper | 4 | 3519 | 769.50 |