Mapillary

Mapillary is a crowdsourced street-level imagery platform that collects and hosts geotagged photographs taken by community contributors at ground level, creating a global alternative to Google Street View with more frequent updates and coverage in areas that commercial mapping vehicles have not visited.

For OSINT investigators conducting location verification and geospatial intelligence analysis, Mapillary provides several advantages over Google Street View. First, its imagery is often more recent in specific areas, having been captured recently by community contributors rather than waiting for periodic commercial vehicle sweeps. Second, it covers areas that Street View does not, including pedestrian paths, rural roads, building interiors in some cases, and developing world locations.

Location verification using Mapillary follows the same methodology as Street View analysis but with a different photographic dataset. When a subject image has been geolocated to a specific area but Street View shows no coverage, Mapillary may have contributor-supplied imagery that confirms or contradicts the proposed location.

For urban change detection, Mapillary's crowdsourced model means that community contributors often photograph locations following significant events — construction, demolition, natural disasters, or conflict — providing near-real-time ground-level documentation that commercial services take months or years to update.

Mapillary's API allows programmatic access to imagery, which supports large-scale geospatial analysis workflows where manual browsing would be too time-consuming. Investigators can query imagery within geographic bounding boxes and retrieve the most recent available photos.

The platform's computer vision layer adds automatic detection of street signs, traffic infrastructure, and objects in images, which can assist in location verification by identifying specific visual landmarks programmatically rather than through manual inspection.

Record the coordinates, bearing, image date, and contributor attribution for any Mapillary imagery used in geospatial analysis.