San Diego-based Tomnod combines crowdsourcing and machine intelligence to analyze massive datasets, particularly geospatial imagery. Some of their recent applications for crowdsourced imagery analysis include mapping building damage after the Christchurch earthquake and the search for the tomb of Genghis Khan. Most recently, the company partnered with Amnesty International and DigitalGlobe to monitor human rights violations in Syria.
The Genghis Khan work was done through a partnership with the National Geographic Society, gathering 10,000 contributors who made 1.8 million human analytical contributions on high-resolution Geo-Eye satellite imagery. The scale and the scope of this work won them a USGIF Academic Research Award.
Key to Tomnod’s technology are algorithms that rank the reliability and accuracy of individual contributors. With their CrowdRank technology, they can better train contributors as well as improve crowd-based analysis. In addition to their cloud-based toolsets for desktop work, they also have a Rapid On-the-Ground Response System (ROTGRS) that integrates unstructured details with analytics for mobile personnel in the field.
This winning combination of online repository, machine-guided analytics, and on-the-ground tools points to the future of geospatial intelligence gathering. It will be interesting to learn of their next project.