Results of the ISPRS benchmark on urban object detection and 3D building reconstruction

Rottensteiner, Franz; Sohn, Gunho; Gerke, M.; Wegner, Jan Dirk; Breitkopf, Uwe; Jung, Jaewook

doi:10.1016/j.isprsjprs.2013.10.004

Cited by 353 publications

(303 citation statements)

References 29 publications

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“…The examples presented in Figure 12 are the building roofs in DXF files. The organizer of this ISPRS Test Project maintains the web page that show the continuous comparison result of different methods [43], and the detailed results of this ISPRS benchmark on urban object detection and 3D reconstruction can be referred to Rottensteiner et al [44]. The geometric accuracy comparison result of the 3D building reconstruction of test Area 3 is presented in Table 1.…”

Section: Real Data Experimental Resultsmentioning

confidence: 99%

“…As shown in Table 1, among methods that were evaluated by the ISPRS Test Project for the building roof reconstruction [44], our building modeling method is the only method that uses both aerial image and LiDAR point cloud, and the only method that is primitive-based. The average root mean square error in XY plane is 0.6 m, and the error in Z direction is 0.1 m in our modeling result, which indicates that the proposed building modeling method achieves a higher geometric accuracy on both of the plane and elevation accuracy.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

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3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

et al. 2014

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Abstract:In this paper, a primitive-based 3D building roof modeling method, by integrating LiDAR data and aerial imagery, is proposed. The novelty of the proposed modeling method is to represent building roofs by geometric primitives and to construct a cost function by using constraints from both LiDAR data and aerial imagery simultaneously, so that the accuracy potential of the different sensors can be tightly integrated for the building model generation by an integrated primitive's parameter optimization procedure. To verify the proposed modeling method, both simulated data and real data with simple buildings provided by ISPRS (International Society for Photogrammetry and Remote Sensing), were used in this study. The experimental results were evaluated by the ISPRS, which demonstrate the proposed modeling method can integrate LiDAR data and aerial imagery to generate 3D building models with high accuracy in both the horizontal and vertical directions. The experimental results also show that by adding a component, such as a dormer, to the primitive, a variant of the simple primitive is constructed, and the proposed method can generate a building model with some details.

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Section: Real Data Experimental Resultsmentioning

confidence: 99%

Section: Accuracy Assessmentmentioning

confidence: 99%

3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

et al. 2014

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“…With the exception of satellite platforms (Duan & Lafarge, 2016;Toth & Jóźków, 2016), researchers regularly report submeter accuracy of 3D acquisition techniques (Jarząbek-Rychard & Borkowski, 2016;Kabolizade, Ebadi, & Mohammadzadeh, 2012;Mårtensson & Reshetyuk, 2016;Rottensteiner et al, 2014;Wang, Kutterer, & Fang, 2016). Hence, we consider positional accuracy in the range of 0-1 m in 10 cm increments resulting in 11 error classes.…”

Section: Perturbation and Grades Of Accuracymentioning

confidence: 99%

The effect of acquisition error and level of detail on the accuracy of spatial analyses

Biljecki

Heuvelink

Ledoux

et al. 2017

Cartography and Geographic Information Science

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There has been a great deal of research about errors in geographic information and how they affect spatial analyses. A typical GIS process introduces various types of errors at different stages, and such errors usually propagate into errors in the result of a spatial analysis. However, most studies consider only a single error type thus preventing the understanding of the interaction and relative contributions of different types of errors. We focus on the level of detail (LOD) and positional error, and perform a multiple error propagation analysis combining both types of error. We experiment with three spatial analyses (computing gross volume, envelope area, and solar irradiation of buildings) performed with procedurally generated 3D city models to decouple and demonstrate the magnitude of the two types of error, and to show how they individually and jointly propagate to the output of the employed spatial analysis. The most notable result is that in the considered spatial analyses the positional error has a much higher impact than the LOD. As a consequence, we suggest that it is pointless to acquire geoinformation of a fine LOD if the acquisition method is not accurate, and instead we advise focusing on the accuracy of the data. ARTICLE HISTORY

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“…The inclusion of elevation data greatly improves image classification results in urban areas [15,16]. Indeed, a comparison of building extraction methods using aerial imagery indicates that the integration of image-and DSM-based features obtains high accuracies for (large) buildings [17]. However, combining the features derived from both the imagery and the point cloud directly (rather than the DSM) has been shown to prove beneficial for classification problems in the fields of damage assessment [18] and informal settlement mapping [19].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Multiple Kernel Learning for the Classification of UAV Data

2016

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Unmanned Aerial Vehicles (UAVs) are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL) for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM). A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods.

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Results of the ISPRS benchmark on urban object detection and 3D building reconstruction

Cited by 353 publications

References 29 publications

3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

The effect of acquisition error and level of detail on the accuracy of spatial analyses

Optimizing Multiple Kernel Learning for the Classification of UAV Data

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