A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information

3-Dimensional Building Details from Aerial Photography for Internet Maps

This paper introduces the automated characterization of real estate (real property) for Internet mapping. It proposes a processing framework to achieve this task from vertical aerial photography and associated property information. A demonstration of the feasibility of an automated solution builds on test data from the Austrian City of Graz. Information is extracted from vertical aerial photography and various data products derived from that photography in the form of a true orthophoto, a dense digital surface model and digital terrain model, and a classification of land cover. Maps of cadastral property boundaries aid in defining real properties. Our goal is to develop a table for each property with descriptive numbers about the buildings, their dimensions, number of floors, number of windows, roof shapes, impervious surfaces, garages, sheds, vegetation, presence of a basement floor, and other descriptors of interest for each and every property of a city. From aerial sources, at a pixel size of 10 cm, we show that we have obtained positional accuracies in the range of a single pixel, an accuracy of areas in the 10% range, floor counts at an accuracy of 93% and window counts at 86% accuracy. We also introduce 3D point clouds of facades and their creation from vertical aerial photography, and how these point clouds can support the definition of complex facades

Developing a 3D geometry for Urban energy modelling of Indian cities

The advancement in the field of Urban Building Energy Modelling (UBEM) is assisting urban planners and managers to design and operate cities to meet environmental emission targets. The usefulness of the UBEM depends upon the quality and level of details (LoD) of the inputs to the model. The inadequacy and quality of relevant input data pose challenges. This paper analyses the usefulness of different methodologies for developing a 3D building stock model of Ahmedabad, India, recognizing data gaps and heterogenous development of the city over time. It evaluates the potentials, limitations, and challenges of remote sensing techniques namely (a) Satellite imagery (b) LiDAR and (c) Photogrammetry for this application. Further, the details and benefits of data capturing through UAV assisted Photogrammetry technique for the development of the 3D city model are discussed. The research develops potential techniques for feature detection and model reconstruction using Computer vision on the Photogrammetry reality mesh. Preliminary results indicate that the use of supervised learning for Image based segmentation on the reality mesh detects building footprints with higher accuracy as compared to geometrybased segmentation of the point cloud. This methodology has the potential to detect complex building features and remove redundant objects to develop the semantic model at different LoDs for urban simulations. The framework deployed and demonstrated for the part of Ahmedabad has a potential for scaling up to other parts of the city and other Indian cities having similar urban morphology and no previous data for developing a UBEM