Project Abstract Karst terrains show distinctive surface and subsurface features associated with sinkholes, springs, and caves. Many of the hydrological and hydraulic characteristics associated with these features make groundwater systems in karst areas a significant freshwater resource for human consumption and ecological integrity. Unfortunately, these characteristics also make karst aquifers highly vulnerable to contamination. Karst springs, specifically, provide researchers insight to the interaction between input precipitation, karst aquifers as the transmission medium, and discharge as the output response. A karst catchment area or spring catchment is a mapping unit defined by the surface area and the subsurface drain system that contributes to spring discharge. Therefore, being able to determine this catchment area, and understand how hydrologic conditions in karst environments affect contaminant fate and transport in springs, is crucial to establishing effective source water protection areas (SWPAs) to safeguard groundwater and public health. However, due to the inherent complexity of karst systems, it has been challenging for the PROTECT team to (1) forecast spring response, and (2) infer subsurface properties from surface features in karst environments, since both require the application of advanced computational techniques, such as big data analytics and machine learning (ML) algorithms. Hence, the proposed project for the KC Donnelly Externship award will allow me to train along with Mr. Sean Griffin and his team on the core concepts and models behind geospatial artificial intelligence to automate object detection, object classification, and feature extraction from remotely sensed imagery. Mastering the application of ML algorithms will allow me to discover patterns in my research data, and to construct predictive mathematical models using these discoveries.