A learning approach to evaluate the quality of 3D city models
Résumé
The automatic generation of 3D building models from geospatial data is now a standard procedure.An abundant literature covers the last two decades and several softwares are now available. However,urban areas are very complex environments. Inevitably, practitioners still have to visually assess, atcity-scale, the correctness of these models and detect frequent reconstruction errors. Such a processrelies on experts, and is highly time-consuming with approximately two hours/km2per 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 thefirst time, to disentangle fidelity and modeling errors, whatever the level of details of the modeledbuildings. The quality of models is predicted using the geometric properties of buildings and, whenavailable, Very High Resolution images and Digital Surface Models. A baseline of handcrafted, yetgeneric, features is fed into a Random Forest classifier. Both multi-class and multi-label cases areconsidered: due to the interdependence between classes of errors, it is possible to retrieve all errorsat the same time while simply predicting correct and erroneous buildings. The proposed frameworkwas tested on three distinct urban areas in France with more than 3,000 buildings. 80%-99% F-scorevalues are attained for the most frequent errors. For scalability purposes, the impact of the urbanarea composition on the error prediction was also studied, in terms of transferability, generalization,and representativeness of the classifiers. It shown the necessity of multimodal remote sensing dataand mixing training samples from various cities to ensure a stability of the detection ratios, evenwith very limited training set sizes.
Origine : Fichiers produits par l'(les) auteur(s)