Concepts and System Architectures for the Management of Very Large Spatial Raster Objects in a Database Framework

The efficient management of very large spatial raster objects is becoming an important new feature of Geo-Information Systems. This paper investigates and summarises the main requirements that must be fulfilled by a spatial raster management solution. The investigations primarily focus on the management of very large raster mosaics, as a typical example for future requirements, both in terms of data volume and functionality. The aspects investigated include spatial objects access, spatial partitioning and partition indexing, multi-resolution, georeferencing and storage management. The paper then presents two system architectures which approach the problem at different levels of abstraction. The first architecture, GrIdS, is a DBMS application which investigates spatial raster management concepts and techniques available at a middleware layer. The paper discusses some of the key features of the GrIdS project, including a tile-based multi-resolution concept for very large raster mosaics. The section on GrIdS is concluded by the presentation of results which demonstrate the capabilities and limitations of this approach. CONCERT, the second architecture presented, enables the investigation of extensible database concepts and techniques supporting the efficient management of large objects, in particular spatial raster objects

Cloud-Based Geospatial 3D Image Spaces—A Powerful Urban Model for the Smart City

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

Building Change Detection from Historical Aerial Photographs Using Dense Image Matching and Object-Based Image Analysis

A successful application of dense image matching algorithms to historical aerial photographs would offer a great potential for detailed reconstructions of historical landscapes in three dimensions, allowing for the efficient monitoring of various landscape changes over the last 50+ years. In this paper we propose the combination of image-based dense DSM (digital surface model) reconstruction from historical aerial imagery with object-based image analysis for the detection of individual buildings and the subsequent analysis of settlement change. Our proposed methodology is evaluated using historical greyscale and color aerial photographs and numerous reference data sets of Andermatt, a historical town and tourism destination in the Swiss Alps. In our paper, we first investigate the DSM generation performance of different sparse and dense image matching algorithms. They demonstrate the superiority of dense matching algorithms and of the resulting historical DSMs with root mean square error values of 1–1.5 GSD (ground sampling distance) and yield point densities comparable to those of recent airborne LiDAR DSMs. In the second part, we present an object-based building detection workflow mainly based on the historical DSMs and the historical imagery itself. Additional inputs are a current digital terrain model and a cadastral building database. For the case of densely matched DSMs, the evaluation yields building detection rates of 92% for grayscale and 94% for color imagery