Our session is to allow the attendees to discuss the current situation, development and future of AI technologies such as machine learning and deep learning in Geospatial events detections and investigations. Our session is calling for work that is looking into the methods about how AI and big data related technologies can be involved in Geospatial events studies.
With the advancements of AI and Big Data techniques, machine and deep learning methods have investigated Geospatial phenomena and predicted natural events. These technologies have advantages such as high speed and efficiency compared with traditional retrievals. These studies are offered as a contribution to combine the advantage of AI methodology with the detection, segmentation and modeling of Geospatial phenomena, a relatively innovative domain needing more exploration. We call for paper in the following domain:
1. Innovative methodologies to improve AI methods in geospatial event detection and segmentation;
2. Applications of AI methods in natural phenomena and geospatial event detections;
3. Applications of AI methods in natural phenomena and geospatial event segmentations;
4. Applications of AI methods in natural phenomena and geospatial event trajectories and temporal evolutions;
5. Review of AI methods involving in geospatial events.
|Presenter||Qian Liu*, George Mason University, Chaowei Yang, George Mason University, Yun Li, George Mason University, Rainy Cloud Classification Based on Deep Neural Network Method Using IR and VIS Images||15|
|Presenter||Sheila Steinberg*, Brandman University, Applying UN Spatial Sustainable Development Goal Data for Better Decision-Making: A Focus Group Assessment||15|
|Presenter||Pengfei LIU*, , Hongquan SONG, Henan University, Analyzing the influence of meteorological conditions and anthropogenic precursor emissions and of their interactions on ground-level ozone concentrations in Chinese cities||15|
|Presenter||Jia-Huei Chen*, , Kuo-Chen Chang, Department of Geography, National Taiwan Normal University , Application of neural network to construct validation model of air quality IoT sensing data||15|
|Presenter||HUIHUI ZHANG*, Wuhan University,School of Resource and Environmental Sciences, Hugo A Loáiciga, University of California Santa Barbara,Geography Department, DA HA, Tianjin University,School of Civil Engineering, FU REN, Wuhan University, School of Resource and Environmental Sciences, QINGYUN DU, Wuhan University,School of Resource and Environmental Sciences, Spatial and Temporal Downscaling of TRMM Images Using a Novel Hybrid Algorithm||15|
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