The SERVIR Network, working within five regional hubs around the globe, continues to address environmental issues by enabling the access, implementation, and application of Earth observations. Over the past two years, the SERVIR Network has made an effort to build capacity in utilizing Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) approaches to address these environmental challenges.
The TensorFlow Working Group (TFWG) has been a spin-off of this initiative. The TFWG serves as a capacity-building forum for members from the SERVIR hubs, NASA Applied Sciences Team members, private sector, and university researchers to collaborate and grow abilities to directly apply ML, DL, and AI techniques.
This presentation highlighted capacity-building efforts, collaboration opportunities, and SERVIR's vision for applied Machine Learning service development.
Speaker: Tim Mayer (NASA SERVIR Hindu Kush Himalaya Regional Science Coordination Lead)
Tim Mayer holds a BS in Biology and a MS in Environmental Science. His primary research interests include machine learning applications of remote sensing focusing on landscape ecology and land cover/land use analysis. With the NASA SERVIR Science Coordination Office, Tim contributes to the co-development and generation of applied remote sensing services to address an array of environmental challenges. Additionally, he works to enable the use of Earth observations through building capacity efforts.
