Laser Scanning Data Processing Research Articles

New Advances in Automated Urban Modelling from Airborne Laser Scanning Data

Traditionally, urban models in many applications such as urban planning, disaster management, and computer games only require visual accuracy. However, more recently, updating urban infrastructure combined with the rise of mega-cities (i.e. those with populations over ten million) has motivated researchers and users to utilize cityscale models for engineering purposes (e.g. tracking pollution monitoring, optimizing solar panel placement), which necessitate high geometric accuracy. Currently, a major bottleneck lies in the cost of generating accurate, geo-spatially referenced models. This paper presents the evolution of some of the efforts to automatically produce such models. Specifically, recent advances in airborne laser scanning can rapidly acquire accurate, spatial data for large geographic areas in hours, but due to the size of the data sets, coupled with difficulties of capturing and portraying complex structures, many post-processing issues have only recently been addressed to a level sufficient to begin to facilitate automation, especially of building surface reconstruction. Automation is a critical step for further processing and utilization of airborne laser scanned data for engineering-based, urban modeling. This paper presents recent development of the methods for building detection and extraction, with an emphasis on patents and other contributions related to automated processing of airborne laser scanning data.

Automatic processing of Terrestrial Laser Scanning data of building façades

Abstract Feature extraction on facades from unstructured point clouds is a challenging work, especially in the presence of noise. Point cloud segmentation is one of the most important steps in this context. In this paper, a new approach for automatic processing of facade laser scanner data is introduced. Scanner orientation is partially known through the inclination sensors of the laser scanner used. Knowing these values allows us to reduce the point cloud data into a profile distribution function. After orientation, this distribution is a series of peaks and valleys suitable for segmentation. Each segmented layer is afterwards processed to find the facade contours. The results obtained prove that the approach may be successfully employed in building segmentation and extraction of planar features. Moreover, the accuracy of contours is very dependent on the resolution of the scan data.

Imaging Embryonic Development in Caenorhabditis elegans

Embryos are remarkable for their combination of pluripotency, three-dimensionality, and swiftness of subcellular and developmental rearrangements. Embryogenesis in the nematode Caenorhabditis elegans is uniquely suited among model systems to high-resolution dynamic imaging. Within a single high-magnification, high-numerical aperture (NA) microscope field, at submicrometer resolution, it is possible to observe several entire animals taking form. The full approximately 14-h course of embryonic cleavage and morphogenesis of this transparent, free-living worm is essentially invariant. Observing specific fluorescently labeled components during embryonic development promises to reveal the roles of organelles and molecules in an extremely diverse and reproducible set of contexts. The C. elegans community has created a growing collection of hundreds of transgenic strains expressing green fluorescent protein (GFP)-labeled versions of distinct endogenously expressed genes. The task of correlating the resulting expression and localization patterns in space and time is simultaneously alluring and technically demanding. This article describes the use of four-dimensional (4D) laser-scanning microscopy and subsequent data processing to record, portray, analyze, and compare the expression of fluorescently tagged gene products during development of the nematode embryo.

Iterative processing of laser scanning data by full waveform analysis

The latest developments in laser scanners allow capturing and digitizing of the full waveform of the backscattered pulse. The waveform can be analyzed for measurement features such as range, reflectance values and spreading of the pulse. These features are used to distinguish between locally planar surface elements and partly penetrable objects caused by partial occlusions. This pre-segmentation and the derived range values are used to automatically generate surface primitives in the form of planes. This allows refining each range value taking the surface geometry in a close neighborhood into account. To refine the modeling of the surface, partly occluded surface areas are extended by prediction of the expected range values. This prediction is further improved by considering the surface slope for the estimated received waveform. Then the point cloud associated with the surface is enhanced by additional range values that were missed in the first processing step due to weak signal response. This procedure is repeated several times until all useful range values are considered to estimate the surface.

Global and local methods for tracking the intersection curve between two surfaces

At present, the modelling of terrain edges from discrete data clouds {x,y,z} is one of the ‘hot topics’ in the processing of laser scanning data. This paper proposes two different methods for the three-dimensional modelling of terrain edges. Common to both methods is the idea to describe the terrain edge as the intersection line of two surface patches zi=z(x,y), i=1,2. The first method is based on numerical integration of a differential equation describing the intersection line. The second method uses the snakes algorithm for the identification of the terrain edge. Both methods are tested for synthetic and real-world data examples, which shows that they are suitable for the practical extraction of edges from laser scanning data.