Groundwater contamination with naturally-occurring arsenic (As) poses a serious health threat of global proportions. Implementation of focused and sustainable As-mitigation measures is hampered by the inability to pinpoint enigmatic As-hotspots in the vast area of continental alluvial basins with elevated toxicity risk. The catalyser role of invasive vegetation in anoxic fluvial oxbow lakes in combination with microbial metabolism processes are key in mobilizing As from its solid state into the shallow aquifer domain and accumulation in porous and permeable fluvial point-bar sediment. This insight opens up the opportunity for a cross-disciplinary approach to construct predictive machine learning-based object-based geospatial models to locate As-hotspots by the analyses of (1) satellite imagery of alluvial geomorphology, (2) oxbow-lake vegetation density, and (3) ground-truth databases of oxbow-lake/aquifer biogeochemistry and fluvial sedimentology.