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Title: GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond

Abstract

Recent progress in Artificial Intelligence (AI) techniques, the large-scale availability of high-quality data, as well as advances in both hardware and software to efficiently process these data, are transforming a range of fields from computer vision and natural language processing to autonomous driving and healthcare. For example, the availability of high-resolution geographic data and high-performance computing techniques together with deep learning fuel progress in fast and accurate object detection. Recent examples of GeoAI work include the detections of terrain features and densely-distributed building footprints, information extraction from scanned historical maps, semantic classification (e.g. LiDAR point clouds), novel methods for spatial interpolation, and advances in traffic forecasting. Similarly, machine learning and natural language processing are facilitating the extraction of geographic information from unstructured (textual) data, such as news articles and Wikipedia as well as the matching of natural features in multiple gazetteers.

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [5]
  1. Univ. of California, Santa Barbara, CA (United States)
  2. Univ. of Wisconsin, Madison, WI (United States)
  3. McGill Univ., Montreal, QC (Canada)
  4. Univ. at Buffalo, NY (United States)
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE; National Science Foundation (NSF); Natural Sciences and Engineering Research Council of Canada (NSERC); Wisconsin Alumni Research Foundation
OSTI Identifier:
1619012
Grant/Contract Number:  
AC05-00OR22725; 1936677; 1940091
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
International Journal of Geographical Information Science
Additional Journal Information:
Journal Volume: 34; Journal Issue: 4; Journal ID: ISSN 1365-8816
Publisher:
Informa UK Limited
Country of Publication:
United States
Language:
English
Subject:
96 KNOWLEDGE MANAGEMENT AND PRESERVATION

Citation Formats

Janowicz, Krzysztof, Gao, Song, McKenzie, Grant, Hu, Yingjie, and Bhaduri, Budhendra. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. United States: N. p., 2019. Web. doi:10.1080/13658816.2019.1684500.
Janowicz, Krzysztof, Gao, Song, McKenzie, Grant, Hu, Yingjie, & Bhaduri, Budhendra. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. United States. https://doi.org/10.1080/13658816.2019.1684500
Janowicz, Krzysztof, Gao, Song, McKenzie, Grant, Hu, Yingjie, and Bhaduri, Budhendra. 2019. "GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond". United States. https://doi.org/10.1080/13658816.2019.1684500. https://www.osti.gov/servlets/purl/1619012.
@article{osti_1619012,
title = {GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond},
author = {Janowicz, Krzysztof and Gao, Song and McKenzie, Grant and Hu, Yingjie and Bhaduri, Budhendra},
abstractNote = {Recent progress in Artificial Intelligence (AI) techniques, the large-scale availability of high-quality data, as well as advances in both hardware and software to efficiently process these data, are transforming a range of fields from computer vision and natural language processing to autonomous driving and healthcare. For example, the availability of high-resolution geographic data and high-performance computing techniques together with deep learning fuel progress in fast and accurate object detection. Recent examples of GeoAI work include the detections of terrain features and densely-distributed building footprints, information extraction from scanned historical maps, semantic classification (e.g. LiDAR point clouds), novel methods for spatial interpolation, and advances in traffic forecasting. Similarly, machine learning and natural language processing are facilitating the extraction of geographic information from unstructured (textual) data, such as news articles and Wikipedia as well as the matching of natural features in multiple gazetteers.},
doi = {10.1080/13658816.2019.1684500},
url = {https://www.osti.gov/biblio/1619012}, journal = {International Journal of Geographical Information Science},
issn = {1365-8816},
number = 4,
volume = 34,
place = {United States},
year = {Wed Oct 30 00:00:00 EDT 2019},
month = {Wed Oct 30 00:00:00 EDT 2019}
}

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Cited by: 72 works
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