Abstract: In this training session, we will explore the utilization of low-code/no-code visual programming platforms to effectively integrate geospatial analysis with a variety of AI algorithms, including machine learning, deep learning, and Explainable AI. Designed primarily for data science novices, this training enables participants to easily embark on their journey without needing extensive programming expertise. They will learn to harness the platform for advanced spatial analysis and the development of sophisticated AI models.
The NSF Spatiotemporal Innovation Center (STC) is looking for several undergraduate research assistants to serve as NSF REU Fellows from May to September 2024. They will potentially work at the George Mason University site (Fairfax, VA) or the Harvard University site (Cambridge, MA), depending on funding availability from NSF.
This opportunity is supported by the National Science Foundation (NSF) I/UCRC and Research Experiences for Undergraduates (REU) Programs, which enable undergraduate students to obtain research experience and consider a career path in...
The Fisher Prize for excellence in GIS will be given to one Harvard graduate and one Harvard undergraduate student who must be enrolled in the academic year and in good...
Sponsored by the Spatial Data Lab, this hands-on workshop is to promote replicable and expandable spatiotemporal science with advanced methodology and technology. With a focus on geospatial...