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

Mobile-mapping systems (MMSs) are moving from sensor-centric survey platforms toward intelligence-driven components of the remote sensing ecosystem. This critical narrative review analyzes rather than catalogs that shift, organizing it around four structural trends, each stated as a claim and examined against supporting and contradicting evidence: the growing role of learned, feed-forward geometry alongside, not in place of, optimization-based simultaneous localization and mapping, arranged along a latency–fidelity axis; tokenization economics—how points become, and are merged out of, attention tokens—proposed as an interpretive framework for the efficiency of 3D perception; the migration of radiance-field reconstruction from visualization toward measurement, where explicit representations such as 3D Gaussian Splatting yield updatable digital twins whose metric reliability still has to be established against independent reference data; and the transition from perception toward agency, coupling MMS evidence to Earth foundation models. Each trend is tested 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. A controlled same-hardware benchmark of four point-cloud backbones supplies measured evidence for the efficiency pitfalls, and a research agenda for the photogrammetry and remote sensing communities follows. The review has stated limits: the literature was selected purposively rather than systematically, forest-specific evidence is thinner than urban evidence, and the benchmark covers four backbones, two datasets, and one GPU at inference only, so its conclusions are bounded to those configurations.