Crowdsourced Street Maps – Mapillary Raises $8M
Mapillary, a Malmo, Switzerland based company that is building a crowdsourced street level photo mapping service has raised $8M in their Series A fundraising round, led by Atomico, with participation by Sequoia Capital, LDV Capital and Playfair Capital. Some have commented that Mapillary wants to compete with Google Street View using crowd sourced, and then geo-located, photos (and presumably video and other assets over time). Mapillary uses Mapbox as its base mapping platform. Mapbox itself sources its underlying street mapping data from OpenStreetMap, as well as satellite imagery, terrain and places information from other commercial sources – you can see the full list here. Very interesting to see that Mapillary has a relationship with ESRI – such that ESRI ArcGIS users can access Mapillary crowd sourced photo data directly via ArcGIS online.
I previously wrote about MapBox and OpenStreetMap in October 2013 when it closed its initial $10M Series A round led by Foundry Group. You can see that initial blog post here. MapBox subsequently raised a $52.6M Series B round, led by DFJ, in June of 2015. I then examined the intersection of crowdsourced data collection and commercial use in the context of the Crunchbase dispute with Pro Populi and contrasted that with the MapBox and OpenStreetMap relationship.
I am fascinated by the opportunities that are unlocked by the continuing improvement in mobile imaging sensors. The devices themselves are becoming robust enough for local computer vision processing (rather than sending data to the cloud) and we are perhaps a generation away (IMHO) from having an entirely different class of sensors to capture data from. That combined with significant improvements in location services makes it possible to explore some very interesting business and data services in the future.
In late 2013 I predicted that, in time, mobile 3D capture devices (and primarily passive ones) would ultimately be used to capture, and tie together a crowd sourced world model of 3D data.
What could ultimately game changing is if we find updated and refined depth sense technology embedded and delivered directly with the next series of smartphones and augmented reality devices. . . In that world, everyone has a 3D depth sensor, everyone is capturing data, and the potentials are limitless for applications which can harvest and act upon that data once captured.
Let the era of crowd sourced world 3D data capture begin!
It makes absolute sense that the place to start along this journey is 2D and video imagery, which can be supplement (and ultimately supplanted) over time by richer sources of data – leveraging an infrastructure that has already been built. We still have thorny and interesting intellectual property implications to consider (Think Before You Scan That Building) – but regardless – bravo Mapillary! Bravo indeed!