Invasive Phragmites threatens biodiversity and has severely impacted wetland ecosystems in southern Ontario. Local organizations in the Niagara peninsula watershed recognize the importance of establishing a coordinated, multi-partner effort for watershed-level Phragmites mapping and management. Effective management requires accurate information on its spatial extent and distribution. Traditional field-based mapping is expensive and time consuming. Navigating remote wetlands and accessing private lands also pose challenges. Although geospatial technologies for mapping Phragmites are well documented, geospatial artificial intelligence (GeoAI) deep learning techniques remain less understood. This study compares several GeoAI deep learning to traditional supervised classification methods for improved Phragmites detection from remote-sensing data. High-resolution (10 cm) orthoimagery was acquired over a test site in Spring 2023, and field data were used to train six classification approaches. The study assessed GeoAI-based classification accuracy relative to conventional methods, aiming to determine the best approach for Phragmites detection. Preliminary results suggest traditional methods may be more accurate, though GeoAI shows promise in mapping various plant species. Additional refinement and review of GeoAI classification techniques are ongoing. Research findings contribute to a broader initiative, led by the Niagara Peninsula Conservation Authority in Ontario, to establish a watershed-wide invasive species strategy. By integrating advanced geospatial technologies with community-driven conservation efforts, this study supports more effective invasive species management, control, and biodiversity protection in one of Canada’s most biodiverse and threatened ecoregions.