Month: February 2021

We received the notification from NASA that our project proposal “Near-Real-Time Forecasting and Change Detection for a Fire-Prone Shrubland Ecosystem” was selected for funding support. This project aims to utilize statistical modeling and GeoAI methods for near-term ecological forecasting to predict natural land surface processes and evaluate near-real-time changes in the state of a hyperdiverse, fire-dependent and seasonally fluctuating open ecosystem: the fynbos of the Cape Floristic Region (CFR) of South Africa.

Our research team consists of:
• Dr. Adam M. Wilson, Principal Investigator, Wilson Lab, Department of Geography, University at Buffalo, State University of New York, United States
• Dr. Yingjie Hu, Co-Investigator, GeoAI Lab, Department of Geography, University at Buffalo, State University of New York, United States
• Dr. Glenn R. Moncrieff, Co-Investigator, Fynbos Node, South African Environmental Observation Network, South Africa
• Dr. Jasper A. Slingsby, Co-Investigator, Fynbos Node, South African Environmental Observation Network, South Africa

This project further enhances our current AI for Earth project funded by Microsoft and led by Dr. Hu.

We will be hiring a Graduate Research Assistant (starting from Fall 2022 or earlier) and a post doc researcher (starting around Summer 2021), both of whom will be co-advised by Dr. Adam Wilson and Dr. Yingjie Hu. By participating in this project, the GRA and post doc researchers will develop expertise on GeoAI, raster data processing, biodiversity, and ecological forecasting. Interested candidates are encouraged to contact Dr. Hu.

The pandemic made it more difficult for many of us to do research. Thanks to the initiative of AAG, faculty members throughout the country were brought together to share their expertise and help students during this challenging time! I (Yingjie) had a great time leading the workshop on “Integrating Machine Learning into Geographic Research” in the past week. While the AAG committee and I initially planned only a 20-person small workshop to ensure personal level interactions, this workshop received 175 registrations from not only students but also researchers all over the world (see the map of the registrants).

Map of the workshop registrants.

This overwhelming interest is a nice surprise to us, but it also means that we would have to reject a large number of students and researchers who are eager to learn, if we were just to admit 20 participants. Meanwhile, having more participants in the workshop will make it more difficult for the students to have personal interaction with the instructors. Eventually, we admitted 21 students as “active participants” who can actively engage in the workshop, while a large number of other registrants were admitted as “observers” who can still call in and listen to the workshop lectures.

The workshop was a great experience, as I can share my knowledge on GIS and Machine Learning with a wide audience while interacting with each individual active participant. Thank you all for your participating, and many thanks to our AAG workshop committee, particularly Julaiti and Coline for their great help and support!