All range and heterogeneous multi-scale 3D city models

Abstract

D City Models (3DCM) are key features into decision making of several urban related problems. Therefore 3DCM are needed by several applications, but the required level-of-detail (LoD) of the model depends on the application. Our goal is to propose a multi-scale 3DCM production and use method. Our approach consists of merging, procedural modeling, graph rewriting techniques, and a generalization technique to handle all different kinds of LoD of a 3DCM. In this way, it allows to handle various heterogeneous LoDs of a complete urban city model. We test our proposal with the 3DCM of the City of Nantes for a rendering application. Our results can also be applied to other LoDs criteria to match other 3DCM-based needs. 3D city models (3DCM) are fully recognized as useful tools in several urban usages, and these models are used in several fields related to urban planning: decision-making processes, analysis of city characteristics, architectural and urban design, simulations of physical phenomena, information or scientific visualization, official communication, and so on. Besides the problem of a unified data format to share the 3DCM between these various applications, each application needs specific geometric data in order to perform its own process, and sometimes a single application needs the same city model at various scales. In order to avoid a costly multiplication of several models of the same city for each specific need, the 3DCM can be adapted to each application by using a multi-scale 3DCM. The production of a multi-scale 3DCM implies a multilevel modeling and a multi-representation of the city model; so different representations of 3D city models at different Levels of Detail (LoDs) are required. There are two ways of obtaining a 3D model at a given LoD: modeling from scratch or deriving from existing models. Modeling depends on data acquisition techniques or production techniques, whereas derivation is confined by the starting models. Photogrammetry and laser scanning technologies are usually used to capture 3D urban data. These techniques are very efficient to obtain detailed models but there is too much data to remove to obtain low precision model. Procedural modeling is used to produce data according to specific rules. Recent research shows it possible to fit rulesets used for procedural modeling to existing datasets (e.g. images and LIDAR data), so procedural modeling is a technique that is now able to model any kind of urban environment, from modeling synthetic cities to represent accurate existing ones. This method can produce 3DCM with details efficiently but lacks to control the amount of data generated. In general, LoD can be manually set at the rules to have local control density. At the other end of the processing scheme, generalization techniques are used to simplify existing data. They allow to simplify the model but precision depends on input data.