Classification of airborne laser scanning point clouds based on binomial logistic regression analysis

This article presents a newly developed procedure for the classification of airborne laser scanning (ALS) point clouds, based on binomial logistic regression analysis. By using a feature space containing a large number of adaptable geometrical parameters, this new procedure can be applied to point clouds covering different types of topography and variable point densities. Besides, the procedure can be adapted to different user requirements. A binomial logistic model is estimated for all a priori defined classes, using a training set of manually classified points. For each point, a value is calculated defining the probability that this point belongs to a certain class. The class with the highest probability will be used for the final point classification. Besides, the use of statistical methods enables a thorough model evaluation by the implementation of well-founded inference criteria. If necessary, the interpretation of these inference analyses also enables the possible definition of more sub-classes. The use of a large number of geometrical parameters is an important advantage of this procedure in comparison with current classification algorithms. It allows more user modifications for the large variety of types of ALS point clouds, while still achieving comparable classification results. It is indeed possible to evaluate parameters as degrees of freedom and remove or add parameters as a function of the type of study area. The performance of this procedure is successfully demonstrated by classifying two different ALS point sets from an urban and a rural area. Moreover, the potential of the proposed classification procedure is explored for terrestrial data

Investigation on roof segmentation for 3D building reconstruction from aerial LIDAR point clouds

Three-dimensional (3D) reconstruction techniques are increasingly used to obtain 3D representations of buildings due to the broad range of applications for 3D city models related to sustainability, efficiency and resilience (i.e., energy demand estimation, estimation of the propagation of noise in an urban environment, routing and accessibility, flood or seismic damage assessment). With advancements in airborne laser scanning (ALS), 3D modeling of urban topography has increased its potential to automatize extraction of the characteristics of individual buildings. In 3D building modeling from light detection and ranging (LIDAR) point clouds, one major challenging issue is how to efficiently and accurately segment building regions and extract rooftop features. This study aims to present an investigation and critical comparison of two different fully automatic roof segmentation approaches for 3D building reconstruction. In particular, the paper presents and compares a cluster-based roof segmentation approach that uses (a) a fuzzy c-means clustering method refined through a density clustering and connectivity analysis, and (b) a region growing segmentation approach combined with random sample consensus (RANSAC) method. In addition, a robust 2.5D dual contouring method is utilized to deliver watertight 3D building modeling from the results of each proposed segmentation approach. The benchmark LIDAR point clouds and related reference data (generated by stereo plotting) of 58 buildings over downtown Toronto (Canada), made available to the scientific community by the International Society for Photogrammetry and Remote Sensing (ISPRS), have been used to evaluate the quality of the two proposed segmentation approaches by analysing the geometrical accuracy of the roof polygons. Moreover, the results of both approaches have been evaluated under different operating conditions against the real measurements (based on archive documentation and celerimetric surveys realized by a total station system) of a complex building located in the historical center of Matera (UNESCO world heritage site in southern Italy) that has been manually reconstructed in 3D via traditional Building Information Modeling (BIM) technique. The results demonstrate that both methods reach good performance metrics in terms of geometry accuracy. However, approach (b), based on region growing segmentation, exhibited slightly better performance but required greater computational time than the clustering-based approach

Calibration of full-waveform airborne laser scanning data for 3D object segmentation

Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy. The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%

Roof plane segmentation by combining multiple images and point clouds

A new method for roof plane detection using multiple aerial images and a point cloud is presented. It takes advantage of the fact that segmentation results for different views look different even if the same parameters are used for the original segmentation algorithm. The point cloud can be generated by image matching or by airborne laserscanning. Plane detection starts by a segmentation that is applied to each of the images. The point cloud is used to determine which image segments correspond to planes. The best plane according to a criterion is selected and matched with segments in the other images. Matching of segments requires a DSM generated from the point cloud, and it takes into account the occlusions in each image. This procedure is repeated until no more planes can be found. After that, planar segments are extracted based on region growing in the point cloud in areas of severe under-segmentation, and the multiple-image segmentation procedure is repeated. Finally, neighbouring regions found to be co-planar are merged. First results are presented for test site with up to nine-fold overlap. Our tests show that the method can deliver a good separation of roof planes under difficult circumstances, though the level of detail that can be achieved is limited by the resolution of the point cloud

The Application of LiDAR to Assessment of Rooftop Solar Photovoltaic Deployment Potential in a Municipal District Unit

A methodology is provided for the application of Light Detection and Ranging (LiDAR) to automated solar photovoltaic (PV) deployment analysis on the regional scale. Challenges in urban information extraction and management for solar PV deployment assessment are determined and quantitative solutions are offered. This paper provides the following contributions: (i) a methodology that is consistent with recommendations from existing literature advocating the integration of cross-disciplinary competences in remote sensing (RS), GIS, computer vision and urban environmental studies; (ii) a robust methodology that can work with low-resolution, incomprehensive data and reconstruct vegetation and building separately, but concurrently; (iii) recommendations for future generation of software. A case study is presented as an example of the methodology. Experience from the case study such as the trade-off between time consumption and data quality are discussed to highlight a need for connectivity between demographic information, electrical engineering schemes and GIS and a typical factor of solar useful roofs extracted per method. Finally, conclusions are developed to provide a final methodology to extract the most useful information from the lowest resolution and least comprehensive data to provide solar electric assessments over large areas, which can be adapted anywhere in the world

OBLIQUE PHOTOGRAMMETRY SUPPORTING 3D URBAN RECONSTRUCTION OF COMPLEX SCENARIOS

open9siAccurate 3D city models represent an important source of geospatial information to support various “smart city” applications, such as space management, energy assessment, 3D cartography, noise and pollution mapping as well as disaster management. Even though remarkable progress has been made in recent years, there are still many open issues, especially when it comes to the 3D modelling of complex urban scenarios like historical and densely-built city centres featuring narrow streets and non-conventional building shapes. Most approaches introduce strong building priors/constraints on symmetry and roof typology that penalize urban environments having high variations of roof shapes. Furthermore, although oblique photogrammetry is rapidly maturing, the use of slanted views for façade reconstruction is not completely included in the reconstruction pipeline of state-of-the-art software. This paper aims to investigate state-of-the-art methods for 3D building modelling in complex urban scenarios with the support of oblique airborne images. A reconstruction approach based on roof primitives fitting is tested. Oblique imagery is then exploited to support the manual editing of the generated building models. At the same time, mobile mapping data are collected at cm resolution and then integrated with the aerial ones. All approaches are tested on the historical city centre of Bergamo (Italy).openToschi, I.; Ramos, M. M.; Nocerino, E.; Menna, F.; Remondino, F.; Moe, K.; Poli, D.; Legat, K.; Fassi, F.Toschi, I.; Ramos, M. M.; Nocerino, E.; Menna, F.; Remondino, F.; Moe, K.; Poli, D.; Legat, K.; Fassi, Francesc