Change Detection of Cities

Ledoux

et al. 2015

IJGI

617

380

In the last decades, 3D city models appear to have been predominantly used for visualisation; however, today they are being increasingly employed in a number of domains and for a large range of tasks beyond visualisation. In this paper, we seek to understand and document the state of the art regarding the utilisation of 3D city models across multiple domains based on a comprehensive literature study including hundreds of research papers, technical reports and online resources. A challenge in a study such as ours is that the ways in which 3D city models are used cannot be readily listed due to fuzziness, terminological ambiguity, unclear added-value of 3D geoinformation in some instances, and absence of technical information. To address this challenge, we delineate a hierarchical terminology (spatial operations, use cases, applications), and develop a theoretical reasoning to segment and categorise the diverse uses of 3D city models. Following this framework, we provide a list of identified use cases of 3D city models (with a description of each), and their applications. Our study demonstrates that 3D city models are employed in at least 29 use cases that are a part of more than 100 applications. The classified inventory could be useful for scientists as well as stakeholders in the geospatial industry, such as companies and national mapping agencies, as it may serve as a reference document to better position their operations, design product portfolios, and to better understand the market.

Section: Change Detectionmentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Ledoux

et al. 2015

IJGI

617

380

“…A comparison approach to 3D city models have been initially investigated by (Pédrinis et al, 2014). This approach only focuses on how to semantically mark objects that have been altered through time, by projecting building objects to their footprints and, then, linking the 2D geometries together.…”

Section: D City Models Matchingmentioning

confidence: 99%

A Framework for the Representation of Two Versions of a 3d City Model in 4d Space

Vitalis

Ohori

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

2018

<p><strong>Abstract.</strong> 3D city models are being increasingly adopted by organisations in order to serve application needs related to urban areas. In order to fulfil the different requirements of various applications, the concept of Level of Detail (LoD) has been incorporated in 3D city models specifications, such as CityGML. Therefore, datasets of different LoDs are being created for the same areas by several organisations for their own use cases. Meanwhile, as time progresses newer versions of existing 3D city models are being created by vendors. Nevertheless, the existing mechanisms for representating multi-LoD data has not been adopted by the users and there has been little effort on the implementation of a mechanism to store multiple revisions of a city model. This results in redundancy of information and the existence of multiple datasets inconsistent with each other. Alternatively, a representation of time or scale as additional dimensions to the three spatial ones has been proposed as a better way to store multiple versions of datasets while retaining information related to the corresponding features between datasets. In this paper, we propose a conceptual framework with initial considerations for the implementation of a 4D representation of two states of a 3D city model. This framework defines both the data structure of such an approach, as well as the methodology according to which two existing 3D city models can be compared, associated and stored with their correspondences in 4D. The methodology is defined as six individual steps that have to be undertaken, each with its own individual requirements and goals that have to be challenged. We, also, provide some examples and considerations for the way those steps can be implemented.</p>

“…It has been widely used in geoinformation science and 3D city modelling for diverse purposes (Min et al, 2007), for instance, to assess the quality of GIS data (Girres and Touya, 2010), to assess the performance Figure 3: In our approach and our software implementation, the shadow on the ground is derived as a unionised set (green) of projected polygons (in red; 51 polygons in this LOD3 case) from the CityGML model, and accounting for the footprint. of 3D generalisation (Cignoni et al, 1998), to aid map matching (Mustiere and Devogele, 2008), to analyse movement trajectories (Liu et al, 2012), and to detect changes between two CityGML models (Pédrinis et al, 2015).…”

Section: Selection Of Error Metricsmentioning

confidence: 99%

Does a Finer Level of Detail of a 3D City Model Bring an Improvement for Estimating Shadows?

Biljecki

Ledoux

Advances in 3D Geoinformation

2016

3D city models are characterised by the level of detail (LOD), which indicates their spatio-semantic complexity. Modelling data in finer LODs results in visually appealing models and opens the door for more applications, but that is at the expense of increased costs of acquisition, and larger storage footprint. In this paper we investigate whether the improvement in the LOD of a 3D building model brings more accurate shadow predictions. The result is that in most cases the improvement is negligible. Hence, the higher cost of acquiring 3D models in finer LODs is not always justified. However, the exact performance is influenced by the architecture of a building. The paper also describes challenges in experiments such as this one. For instance, defining error metrics may not always be simple, and the big picture of errors should be considered, as the impact of errors ultimately depends on the intended use case. For example, an error of a certain magnitude in estimating the shadow may not significantly affect visualisation purposes, but the same error may considerably influence the estimation of the photovoltaic potential.