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Comparing Artificial Intelligence Algorithms and their classification abilities

Organization(s):Institute for Global Environmental Strategies (IGES) GLOBE v-School
Country:United States of America
Student(s):Elle Rosenthal, Amber Jackson, Anika Gullapalli
Grade Level:Secondary School (grades 9-12, ages 14-18)
GLOBE Teacher:Cassie Soeffing
Contributors:Dr. Russanne Low, SME, IGES. Peder Nelson, SME, OSU. Andrew Clark, SME, IGES. Dr. Erika Podest, SME, NASA JPL
Report Type(s):International Virtual Science Symposium Report, Mission Mosquito Report
Protocols:Land Cover Classification, Earth As a System, Mosquitoes
Presentation Video: View Video
Presentation Poster: View Document
Optional Badges:I am a Problem Solver, I am a Data Scientist, I work with a STEM Professional
Language(s):English
Date Submitted:01/17/2024
Our objective is to examine the accuracy of AI land cover classification algorithms for the development of a citizen science app intended to be paired with the GLOBE observer. Artificial intelligence has incredible potential for land cover classification application. It would increase efficiency in land cover monitoring, improve citizen science accessibility, and allow the public to more effectively contribute to land cover classification by removing any discrepancies caused by personal bias. Utilizing AI monitoring of land cover would help to predict droughts, natural disasters, weather patterns, ect. faster. Additionally, it would create an easier process for citizen scientists to contribute to land cover data.


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