Airborne photogrammetry and LIDAR for DSM extraction and 3D change detection over an urban area : a comparative study

A digital surface model (DSM) extracted from stereoscopic aerial images, acquired in March 2000, is compared with a DSM derived from airborne light detection and ranging (lidar) data collected in July 2009. Three densely built-up study areas in the city centre of Ghent, Belgium, are selected, each covering approximately 0.4 km(2). The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t(1) and t(2), are investigated as to what extent 3D (building) changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate 'real' building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t(2) - t(1). Based on the change model, the surface and volume of the building changes can be quantified

Semantic Modeling of Outdoor Scenes for the Creation of Virtual Environments and Simulations

Efforts from both academia and industry have adopted photogrammetric techniques to generate visually compelling 3D models for the creation of virtual environments and simulations. However, such generated meshes do not contain semantic information for distinguishing between objects. To allow both user- and system-level interaction with the meshes, and enhance the visual acuity of the scene, classifying the generated point clouds and associated meshes is a necessary step. This paper presents a point cloud/mesh classification and segmentation framework. The proposed framework provides a novel way of extracting object information – i.e., individual tree locations and related features while considering the data quality issues presented in a photogrammetric-generated point cloud. A case study has been conducted using data that were collected at the University of Southern California to evaluate the proposed framework

Performance Measure That Indicates Geometry Sufficiency of State Highways: Volume II—Clear Zones and Cross-Section Information Extraction

Evaluation method employed for the proposed corridor projects by Indiana Department of Transportation (INDOT) consider road geometry improvements by a generalized categorization. A new method which considers the change in geometry improvements requires additional information regarding cross section elements. Part of this information is readily available but some information like the embankment slopes and obstructions near traveled way needs to be acquired. This study investigates available data sources and methods to obtain cross-section and clear zone information in a feasible way for this purpose. We have employed color infrared (CIR) orthophotos, LiDAR point clouds, digital elevation and surface models for the extraction of the paved surface, average grade, embankment slopes, and obstructions near the traveled way like trees and man-made structures. We propose a framework which first performs a support vector machine (SVM) classification of the paved surface, then determines the medial axis and reconstructs the paved surface. Once the paved surface is obtained, the clear zones are defined and the features within the clear zones are extracted by the classification of LiDAR point clouds. SVM classification of the paved surface from CIR orthophotos in the study area results with a classification accuracy over 90% which suggests the suitability of high resolution CIR images for the classification of paved surface via SVM. A total of 21.3 miles of relevant road network has been extracted. This corresponds to approximately 90% of the actual road network due to missing parts in the paved surface classification results and parts which were removed during cleaning, simplification and generalization process. Branches due to connecting driveways, adjacent parking lots, etc. were also extracted together with the main road alignment as by-product. This information may also be utilized if found necessary with further effort to filter out irrelevant pieces that do not correspond to any actual branches. Based on the extracted centerline and classification results, we have estimated the paved surface as observed on the orthophotos. Based on the estimated paved surface centerline and width, we have generated cross section lines and calculated the side slopes. We have extracted the buildings and trees within the clear-zones that are also defined based on the reconstruction of the paved surface. Among 86 objects detected as buildings, 14% were false positives due to confusion with bridges or trees which present planar structure

Using street based metrics to characterize urban typologies

[EN] Urban spatial structures reflect local particularities produced during the development of a city. High spatial resolution imagery and LiDAR data are currently used to derive numerical attributes to describe in detail intra-urban structures and morphologies. Urban block boundaries have been frequently used to define the units for extracting metrics from remotely sensed data. In this paper, we propose to complement these metrics with a set of novel descriptors of the streets surrounding the urban blocks under consideration. These metrics numerically describe geometrical properties in addition to other distinctive aspects, such as presence and properties of vegetation and the relationship between the streets and buildings. For this purpose, we also introduce a methodology for partitioning the street area related to an urban block into polygons from which the street urban metrics are derived. We achieve the assessment of these metrics through application of a one-way ANOVA procedure, the winnowing technique, and a decision tree classifier. Our results suggest that street metrics, and particularly those describing the street geometry, are suitable for enhancing the discrimination of complex urban typologies and help to reduce the confusion between certain typologies. The overall classification accuracy increased from 72.7% to 81.1% after the addition street of descriptors. The results of this study demonstrate the usefulness of these metrics for describing street properties and complementing information derived from urban blocks to improve the description of urban areas. Street metrics are of particular use for the characterization of urban typologies and to study the dynamics of cities.The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE, and the data made available by the Spanish Instituto Geográfico Nacional (IGN)Hermosilla, T.; Palomar-Vázquez, J.; Balaguer Beser, ÁA.; Balsa Barreiro, J.; Ruiz Fernández, LÁ. (2014). Using street based metrics to characterize urban typologies. Computers, Environment and Urban Systems. 44:68-79. https://doi.org/10.1016/j.compenvurbsys.2013.12.002S68794

Use of multi-angle high-resolution imagery and 3D information for urban land-cover classification: a case study on Istanbul

The BELSPO-MAMUD project focuses on the use of Remote Sensing data for measuring and modelling urban dynamics. Remote sensing is a wonderful tool to produce long time-series of high resolution maps of sealed surface useful for this purpose. In the urban context of Istanbul, a very dynamic city, recent high resolution satellite images and medium resolution images from the past have been exploited to calibrate and validate a regression-based sub-pixel classification method allowing this production. In this context it’s a tricky task for several reasons: prominent occurrence of shadowed and occluded areas and urban canyons, spectral confusions between urban and non-urban materials at ground and roof levels, moderately hilly relief ... To cope with these difficulties the combined use of three types of data may be helpful: diachronic (i), multi-angle and 3D data. A master multispectral and panchromatic QuickBird image and a panchromatic Ikonos stereopair, all acquired in March 2002, were used in combination with a multispectral and panchromatic Ikonos image of May 2005. A DSM was generated from the Ikonos stereopair and building vector file. It was used for orthorectification, building height estimation and classification procedure. The area covered by the high resolution products was divided in 3 partitions and each one was classified independently. This application demonstrates that recent high resolution land-cover classification produced using multi-date, multi-angle and DSM can be used to produce sealed surface maps from longer timeseries of medium resolution images over large urban areas enabling so the analysis of urban dynamics

Methodology and Algorithms for Pedestrian Network Construction

With the advanced capabilities of mobile devices and the success of car navigation systems, interest in pedestrian navigation systems is on the rise. A critical component of any navigation system is a map database which represents a network (e.g., road networks in car navigation systems) and supports key functionality such as map display, geocoding, and routing. Road networks, mainly due to the popularity of car navigation systems, are well defined and publicly available. However, in pedestrian navigation systems, as well as other applications including urban planning and physical activities studies, road networks do not adequately represent the paths that pedestrians usually travel. Currently, there are no techniques to automatically construct pedestrian networks, impeding research and development of applications requiring pedestrian data. This coupled with the increased demand for pedestrian networks is the prime motivation for this dissertation which is focused on development of a methodology and algorithms that can construct pedestrian networks automatically. A methodology, which involves three independent approaches, network buffering (using existing road networks), collaborative mapping (using GPS traces collected by volunteers), and image processing (using high-resolution satellite and laser imageries) was developed. Experiments were conducted to evaluate the pedestrian networks constructed by these approaches with a pedestrian network baseline as a ground truth. The results of the experiments indicate that these three approaches, while differing in complexity and outcome, are viable for automatically constructing pedestrian networks