From Data Collection to Geospatial Intelligence: Trends, Pitfalls, and a Research Agenda for Next-Generation Mobile Mapping

Mobile mapping systems (MMS) are moving from sensor-centric survey platforms toward intelligencedriven components of the remote sensing ecosystem. This review analyzes rather than catalogs that shift, organizing it around four structural trends: the replacement of per-frame geometric optimization in simultaneous localization and mapping bylearned, feed-forwardgeometry, arrangedalongalatencyfidelity axis; the emergence of tokenization economics—how points become, and are pruned from, attention tokens—as the governing principle of 3D perception efficiency; the migration of radiancefield reconstruction from visualization toward measurement, where explicit representations such as 3D Gaussian Splatting yield updatable digital twins; and the transition from perception to agency, coupling MMS evidence to Earth foundationmodels. Eachtrendistested against the two environments where mobile mapping is most deployed, city and forest, whose contrasting geometry and satellite visibility expose how far each advance generalizes. Seven recurring pitfalls are then consolidated, among them efficiency claims measured on heterogeneous hardware, parameter count used as an efficiency proxy, and architecture rankings assumed to transfer between domains as dissimilar as urban corridors and forest plots. Controlled same-hardware benchmarks supply measured evidence for the efficiency pitfalls, and a research agenda for the photogrammetry and remote sensing communities follows.