The technical workflow transforms raw street-level imagery into structured geospatial intelligence through a rigorous, three-stage process of data collection, data extraction, and data integration. It begins with a strategic sensor deployment, utilizing high-resolution cameras mounted on multi-modal transport (preferably a motor bike) to capture sequential 360° imagery at intervals, ensuring comprehensive coverage and optimal feature visibility for computer vision processing. Once uploaded via edge-computing tools, the data enters the extraction phase where utility assets such as power poles and transformers are identified to build functional network graphs, while road geometries and building metadata are digitized to profile carriage-widths and structural attributes. Finally, this data undergoes a dual-layer validation process, cross-referencing computer-vision outputs against satellite imagery before being synchronized with OpenStreetMap via the JOSM editor, ensuring all infrastructure data adheres to global standards for interoperability and long-term utility.

The collaborative framework centers on a "Human Stack" approach that ensures long-term sustainability by embedding mapping expertise within local institutions. This process begins with strategic stakeholder alignment, where needs-assessment workshops with municipal planners and utility providers define mission-critical data attributes and establish clear protocols for open data governance. To transition partners from data consumers to creators, a "Train-the-Trainer" model is implemented, combining hands-on field "mapathons" with technical instruction on open-source tool chains like OSM ID editor, HOT Tasking manager, Mapillary, QGIS and JOSM. By integrating these datasets into existing asset management systems and establishing continuous feedback loops for data validation, the framework fosters deep institutional ownership and ensures that the digital infrastructure remains accurate, scalable, and locally maintained long after the initial project phase.