Abstract
In this paper, we introduce the concept and an implementation of geospatial 3D image spaces as new type of native urban models. 3D image spaces are based on collections of georeferenced RGB-D imagery. This imagery is typically acquired using multi-view stereo mobile mapping systems capturing dense sequences of street level imagery. Ideally, image depth information is derived using dense image matching. This delivers a very dense depth representation and ensures the spatial and temporal coherence of radiometric and depth data. This results in a high-definition WYSIWYG (“what you see is what you get”) urban model, which is intuitive to interpret and easy to interact with, and which provides powerful augmentation and 3D measuring capabilities. Furthermore, we present a scalable cloud-based framework for generating 3D image spaces of entire cities or states and a client architecture for their web-based exploitation. The model and the framework strongly support the smart city notion of efficiently connecting the urban environment and its processes with experts and citizens alike. In the paper we particularly investigate quality aspects of the urban model, namely the obtainable georeferencing accuracy and the quality of the depth map extraction. We show that our image-based georeferencing approach is capable of improving the original direct georeferencing accuracy by an order of magnitude and that the presented new multi-image matching approach is capable of providing high accuracies along with a significantly improved completeness of the depth maps.
Highlights
The original concept of “smart city” was first postulated in the late 1980s and was focused on the role of information and communication technologies (ICT) with regard to modern urban infrastructures.Since it has evolved into a more general concept relying on the use of ICT to enhance quality and performance of urban services, to reduce costs and resource consumption, and to engage more effectively and actively with its citizens
We show that our image-based georeferencing approach is capable of improving the original direct georeferencing accuracy by an order of magnitude and that the presented new multi-image matching approach is capable of providing high accuracies along with a significantly improved completeness of the depth maps
The goals of the following experiments were (a) to assess the quality of directly georeferenced sensor orientations in a challenging urban environment and (b) to improve the sensor orientation quality using automated image-based georeferencing techniques. These improved sensor orientations were required for the subsequent evaluation of the extracted depth maps (Section 6) and their comparison with reference terrestrial laser scans (TLS) data
Summary
The original concept of “smart city” was first postulated in the late 1980s and was focused on the role of information and communication technologies (ICT) with regard to modern urban infrastructures. Since it has evolved into a (multi-dimensional) more general concept relying on the use of ICT to enhance quality and performance of urban services, to reduce costs and resource consumption, and to engage more effectively and actively with its citizens. A common denominator of all smart city definitions is the employment of ICT concepts and infrastructures allowing people to smartly interact with real-world objects and processes Such ICT solutions, again, require models of the real world in our case urban models, in order to represent, interact with, analyze or simulate the urban environment and processes. Since a large part of urban infrastructure and activity is closely linked to road corridors, streetside urban models are of particular importance in a smart city context
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