GeoAI and Deep Learning Symposium: Spatial Data Science for Ecosystem Conservation and Biodiversity
The session recording will be archived on the site until June 25th, 2023
This session was streamed but not recorded
Date: 3/24/2023
Time: 8:30 AM - 9:50 AM
Room: Capitol Ballroom 3, Hyatt Regency, Fourth Floor
Type: Paper,
Theme:
Curated Track:
Sponsor Group(s):
No Sponsor Group Associated with this Session
Organizer(s):
Orhun Aydin Saint Louis University
Somayeh Dodge University of California Santa Barbara
Chair(s):
Orhun Aydin Saint Louis University
Somayeh Dodge University of California Santa Barbara
Description:
Conservation problems are increasingly multidisciplinary and require fusing multi-sourced data and prior knowledge of various modalities. Research on human-environment interaction, biodiversity forecasts, and ecosystem conservation all utilize a geographic information system (GIS) of systems approach for modeling and analyzing inherently interconnected dynamic phenomena. The enablement of spatially explicit artificial intelligence (GeoAI) and machine learning methods is becoming increasingly common in solving problems related to biodiversity and conservation.
Presentations (if applicable) and Session Agenda:
Claire Simpson |
Forecasting Post-Fire Vegetation Recovery Using Deep Learning |
Hazhir Karimi |
Modeling macroscale patterns of forest productivity in United States forests using geographically weighted regression |
Timothy Assal, Kent State University |
Drought-induced variability in the sagebrush ecosystem: a broader view captures lag effects and recovery |
Non-Presenting Participants
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GeoAI and Deep Learning Symposium: Spatial Data Science for Ecosystem Conservation and Biodiversity
Description
Type: Paper,
Date: 3/24/2023
Time: 8:30 AM - 9:50 AM
Room: Capitol Ballroom 3, Hyatt Regency, Fourth Floor
Contact the Primary Organizer
Orhun Aydin Saint Louis University
orhun.aydin@slu.edu
Session Sponsored By
