Geospatial AI in Earth Observation, Remote Sensing, and GIScience-Reference-Cited by-同舟云学术

Geospatial AI in Earth Observation, Remote Sensing, and GIScience

Published:2023-11-10 Issue:22 Volume:13 Page:12203
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Liu Shan¹^ORCID,Li Kenan²^ORCID,Liu Xuan³^ORCID,Yin Zhengtong⁴^ORCID

Affiliation:

1. School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China

2. Department of Epidemiology and Biostatistics, College of Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA

3. School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 610054, China

4. College of Resource and Environment Engineering, Guizhou University, Guiyang 550025, China

Abstract

Geospatial artificial intelligence (Geo-AI) methods have revolutionarily impacted earth observation and remote sensing [...]

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/22/12203/pdf

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3. Bi, X., Fan, Q., He, L., Zhang, C., Diao, Y., and Han, Y. (2023). Analysis and Evaluation of Extreme Rainfall Trends and Geological Hazards Risk in the Lower Jinshajiang River. Appl. Sci., 13.

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