- The AtlasHDF Mapping of OSM to HDF5 containers for GeoAI2023 H. Li & M. Werner
Hierarchical Data Format for Water-related Big Geodata (HDF4Water)
Project Description
The HDF4Water project is funded by DFG (project no. 460036893) as an 6-month incubator project via the NFDI Consortium Earth System Sciences (NFDI4Earth).
In this project, we developed a big data framework based on the modern HDF5 technology, called AtlasHDF (Werner & Li, 2022), in which we designed lossless data mappings (i.e., immediate mapping and analysis-ready mapping) from OpenStreetMap (OSM) vector data into a single HDF5 data container to facilitate fast and flexible GeoAI applications, for instance, surface water mapping with OSM and Sentinel-2 data. This document describes concisely all decisions that we took towards adapting the flexible AtlasHDF data storage framework to the requirements of a sustainable, simple, (almost) dependency-free data representation for GeoAI across multiple modalities.
Publications
- Werner, M., & Li, H. (2022). AtlasHDF: An Efficient Big Data Framework for GeoAI. 1–7. https://doi.org/10.1145/3557917.3567615
[PDF]
[Online]
[BibTeX]
@inproceedings{2022_atlashdf_Werner,
author = {Werner, Martin and Li, Hao},
title = {AtlasHDF: An Efficient Big Data Framework for GeoAI},
year = {2022},
isbn = {9781450395311},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3557917.3567615},
doi = {10.1145/3557917.3567615},
pages = {1–7},
numpages = {7},
keywords = {GeoAI, OpenStreetMap, big data, hierarchical data format, immediate mapping},
location = {Seattle, Washington},
series = {BigSpatial '22}
}