Quality evaluation of 3D city building Models with automatic error diagnosis

Michelin, Jean-Christophe; Tierny, Julien; Tupin, Florence; Mallet, Clément; Paparoditis, Nicolas

doi:10.5194/isprsarchives-xl-7-w2-161-2013

Cited by 8 publications

(16 citation statements)

References 18 publications

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“…As the next stage of the Project, some automatic diagnostics (AD) and classification of errors experiments on this quite representative sample of actual data is being carried out. First step is to test some algorithms considered in [5,6]. After that we'll start to incorporate some ADEs for urban development applications (energy consumption, noise contamination, and air pollution dissemination).…”

Section: Methodsmentioning

confidence: 99%

Smart city: automatic reconstruction of 3D building models to support urban development and planning

Данилина

Slepnev

Chebotarev³

2018

MATEC Web Conf.

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The spread of smart city technologies dictates the need to develop new methods in the field of urban planning and design. The article devotes to the question how to find new approaches to make digital urban project according to the BIMtechnology. We analyze the opportunities of automated design technology BuildingReconstruction 2018 that allows constructing 3D models of landscapes, urban territories and infrastructure objects. Their distinctive feature is their high accuracy of details. We consider methods and tools for creating such models. There is the description of a 3d-model and composing information layers. On the base of the analysis of the model–s capabilities, we offer various directions how to use them for solving urban planning problems addressed to the examples of their existing implementation.

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Section: Methodsmentioning

confidence: 99%

Smart city: automatic reconstruction of 3D building models to support urban development and planning

Данилина

Slepnev

Chebotarev³

2018

MATEC Web Conf.

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“…Raw remote sensing data: models can either be compared to the source that allowed the generation of the models or remote sensing data of superior geometric accuracy: LiDAR point clouds, height maps (i.e., DSMs) (Akca et al, 2010;Lafarge and Mallet, 2012;Li et al, 2016;Zhu et al, 2018) or multi-view Very High Resolution images as in (Boudet et al, 2006;Michelin et al, 2013).…”

Section: Reference Data Typesmentioning

confidence: 99%

A Learning Approach to Evaluate the Quality of 3D City Models

Ennafii¹,

Bris²,

Lafarge³

et al. 2019

photogramm eng remote sensing

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The automatic generation of three-dimensional (3D) building models from geospatial data is now a standard procedure. An abundance of literature covers the last two decades, and several solutions are now available. However, urban areas are very complex environments. Inevitably, practitioners still have to visually assess, at a city-scale, the correctness of these models and detect frequent reconstruction errors. Such a process relies on experts and is highly time-consuming, with approximately two hours/km 2 per expert. This work proposes an approach for automatically evaluating the quality of 3D building models. Potential errors are compiled in a novel hierarchical and versatile taxonomy. This allows, for the first time, to disentangle fidelity and modeling errors, whatever the level of details of the modeled buildings. The quality of models is predicted using the geometric properties of buildings and, when available, Very High Resolution images and Digital Surface Models. A baseline of handcrafted, yet generic, features is fed into a Random Forest classifier. Both multiclass and multilabel cases are considered: due to the interdependence between classes of errors, it is possible to retrieve all errors at the same time while simply predicting correct and erroneous buildings. The proposed framework was tested on three distinct urban areas in France with more than 3000 buildings. 80%–99% F-score values are attained for the most frequent errors. For scalability purposes, the impact of the urban area composition on the error prediction was also studied, in terms of transferability, generalization, and representativeness of the classifiers. It showed the necessity of multimodal remote sensing data and mixing training samples from various cities to ensure a stability of the detection ratios, even with very limited training set sizes.

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“…The building model is therefore assessed owing to a supervised classification that takes these defined errors as labels. In order to represent input models, features are extracted from aerial images or Digital Surface Model (DSM) at very high resolution (20 cm to 25 cm), by 3D segments comparisons or texture correlation ratios [7], [8]. Despite satisfactory results, this approach exhibits two main drawbacks: a taxonomy specific to some urban scenes or modeling methods and a complex feature computation step, preventing upscaling strategies.…”

Section: Related Workmentioning

confidence: 99%

The necessary yet complex evaluation of 3D city models: a semantic approach

Ennafii¹,

Mallet²,

Bris³

et al. 2019

2019 Joint Urban Remote Sensing Event (JURSE)

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The automatic modeling of urban scenes in 3D from geospatial data has been studied for more than thirty years. However, the output models still have to undergo a tedious task of correction at city scale. In this work, we propose an approach for automatically evaluating the quality of 3D building models. A taxonomy of potential errors is first proposed. Handcrafted features are computed, based on the geometric properties of buildings and, when available, Very High Resolution images and depth data. They are fed into a Random Forest classifier for the prediction of the quality of the models. We tested our framework on three distinct urban areas in France. We can satisfactorily detect, on average 96% of the most frequent errors.

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Quality evaluation of 3D city building Models with automatic error diagnosis

Cited by 8 publications

References 18 publications

Smart city: automatic reconstruction of 3D building models to support urban development and planning

Smart city: automatic reconstruction of 3D building models to support urban development and planning

A Learning Approach to Evaluate the Quality of 3D City Models

The necessary yet complex evaluation of 3D city models: a semantic approach

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