Competition Year: 2023-2024
Applicant: Weimin Li
Collaborators: Carlos Flores
Amount Awarded: $24,999.19
Primary College: ENV
Abstract: We are applying for the SPICE grant to establish a streamlined digital environment with software and cloud service and develop a comprehensive instructional framework to support the teaching and learning of Big Data (BD), Artificial Intelligence (AI), and Machine Learning (ML) technologies and their innovative applications in environmental design. Through this proposal, we aim to (1) better prepare students with the latest technologies and tools for the capturing, processing, and analyzing of big geospatial data, such as high-resolution remote sensing images and Light Detection and Ranging (LiDAR) data collected with various sensors mounted on spaceborne, airborne, and uninhabited remote sensing platforms; (2) introduce students to big data in various formats, including texts, sounds, photos, videos, etc., that documents the biophysical and socio-behavior phenomena involved in environmental design decision-making, and (3) expose students to the cutting edge knowledge, skills, and tools of AI and ML and their applications in solving environmental and social problems such as climate change crisis, ecological degradation, and environmental and social injustice through learning, research, and practice. With support from the SPICE grant, we will mentor students in the research and discovery of emerging information challenges in the big data era, facilitate collaboration among students to conduct service-learning projects that involve local, regional, and global communities, provide equal access to centralized computation powers and software environment, and offer inclusive accommodation of students with demands for flexible learning modalities and schedules. In short, our project provides students with cutting-edge information technologies and enables them to generate creative and innovative solutions to critical societal problems through learn-by-doing activities in highly flexible learning modes.
The funding will cover the cost of big geospatial data software applications, such as Trimble eCognition and Pix4D bundle, software development tools, such as those from Jetbrains, and subscription credit for mainstream cloud servers with AI and ML capacities, such as those from Amazon and Microsoft. Most of the funding will pay for perpetual software licenses, while a small portion covers service credits for periodical subscriptions. That said, while the BD-AI-ML program will mainly serve students in the College of Environmental Design, it will be open to students on the entire campus through the GIS minor program or open electives. In this way, thousands of students in different programs and colleges can be benefited from the investment. The BD-AI-ML program and relevant contents will be directly integrated into current courses such as LA3581/5582 Geodesign Fundamentals for Environmental Designers, LA6111L Design for Change studios, LA6441 Plant and Ecology, LA4782 Evolving Issues in Environmental Design, LA5581 Visual Communication for Design, and indirectly involved in many other courses in the College. After the pilot stage, it can become a stand-alone course to teach students about BD-AI-ML in environmental design. It may also lead to a new certificate or degree program in environmental design with big data in the future. The applicants are faculty members with robust digital and geospatial technologies and big data backgrounds and have many years of experience teaching computer technologies. We are confident of achieving great success within the classroom, participating in the Signature Polytechnic Experiences (PolyX), and generating scholarly publications related to BD-AI-ML in environmental design education. In conclusion, the support from the SPICE grant will provide critical software and network infrastructure for this effort and make a big difference in filling in the current gap that students have little access to the latest software and network resources and lack learning experience that directly connects them to AI and ML technologies in the context of environmental design.
