Human Habitat Data in 3D for the Internet

Abstract

In March 2005, at the occasion of his 50th birthday, Bill Gates went public with his “Virtual Earth Vision” for local search in the Internet and stated: "You’ll be walking around in downtown London and be able to see the shops, the stores, see what the traffic is like. Walk in a shop and navigate the merchandise. Not in the flat, 2D interface that we have on the web today, but in a virtual reality walkthrough.” This implies optimism that an enormous advance will be achieved in computing power, communications bandwidth, miniaturization of computing, increase of storage capacity and in the ability to model the human habitat (the Earth) in great detail in 3 dimensions, with photographic realism and at very low cost per data unit. Action followed this declaration by Bill Gates, and the transition of a then-10-year old Microsoft business segment called “Map Point” into a new Virtual Earth Business Unit was kicked off (recently renamed Bing Maps). The Microsoft initiative, along with similar initiatives by other Internet-providers, most visibly Google, can serve as an example and actually also as a driver for the future of computing and of computational thinking. Research in the complete automatic creation of 3D models of urban spaces has become greatly inspired and now is a very active field of innovation. The level of automation in creating 3D city models has benefited from an increase in the redundancy of the source data in the form of highly overlapping imagery either from the air or from the street. We explain in this paper that it is possible to create 3D models of an entire city from aerial photography fully automatically, and thus at a commercially acceptable cost. Using this as a geometric framework, detail can be added from street-level and indoor imagery or laser scanner data. Such data can be produced either systematically or by “us”, the anonymous community of users. The result is a global geo-data base consisting of a combination of aerial data at perhaps 10 to 15 cm pixel size, street side data at perhaps 2 cm and indoor data of important or commercially relevant spaces at 0.5 cm pixel size. This will add up to a data base of thousands of cities, perhaps also of smaller communities, with more than 1 Exabyte to be created and maintained.