Since the mid-1990s laser scanning or light detection and ranging (Lidar) has matured into a sophisticated mapping technology routinely used for 3D modelling of cities, power stations and factories; power line mapping; dike and dune inspection for flood prevention; monitoring of forests, open pit mines, construction sites, tunnels and glaciers; and many other applications. A laser scanner emits laser beams for measuring: (1) range – distance from the sensor to the first surface hit by the laser beam; (2) scan angle; and (3) intensity of the return. These measurements combined with GNSS (Global Navigation Satellite System) 3D positioning and the inertial measurements unit (IMU) result in accurate 3D coordinates of object points. To further improve the accuracy and to transform the 3D coordinates to a common reference frame, registration of the diverse scans and ground control points (GCP) are required. Lidar sensors can be mounted on a wide variety of platforms, including vehicles, the human back and manned and unmanned aircraft and helicopters. This chapter first elaborates on basics of laser light and point clouds, then continues with georeferencing, followed by the principles of airborne Lidar and ground-based Lidar and a comparison of their point characteristics.@en

People continue to migrate from rural areas to major cities, driving sustained urban growth and increasing the demand for accurate, detailed and up-to-date 3D city models. The creation of such models is still a cumbersome endeavour but new advancements, such as the combination of three sensor types – nadir camera, oblique cameras and a Lidar unit – in one and the same geodata acquisition system, may bring relief. Aerial surveys conducted in major cities in the UK and Ireland demonstrate the potential of this solution.@en

Built environments developed on compressible soils are susceptible to land deformation. The spatiotemporal monitoring and analysis of these deformations are necessary for sustainable development of cities. Techniques such as Interferometric Synthetic Aperture Radar (InSAR) or predictions based on soil mechanics using in situ characterization, such as Cone Penetration Testing (CPT) can be used for assessing such land deformations. Despite the combined advantages of these two methods, the relationship between them has not yet been investigated. Therefore, the major objective of this study is to reconcile InSAR measurements and CPT measurements using machine learning techniques in an attempt to better predict land deformation.

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The use of an unmanned aerial system (UAS) – cameras and Lidar sensors mounted on an unmanned aerial vehicle (UAV or ‘drone’) – to acquire geodata for mapping purposes has evolved beyond infancy and is now rapidly maturing. How will UAS mapping evolve in foreseeable future? To envisage where exactly UAS technology is heading, it is appropriate to start with the big picture before examining the details.@en

Terrestrial laser scanning is becoming an increasingly preferred surveying technique for the 3D documentation of historical buildings. 3D point clouds provide a wealth of information which advanced 3D mapping software can exploit in a relatively simple way, at least when compared to the tedious surveying techniques of the past. However, effective cooperation between architects and the survey team of experts is necessary in order to get the best out of this geodata acquisition technology. In this article, the authors first provide an overview of equipment based on the size of the site and the surveying aim, and subsequently give four examples of how terrestrial laser scanning can benefit the restoration of damaged historical buildings.@en

Accurate geoinformation about urban areas, public buildings or historical sites is in great demand. It has become astonishingly easy to capture these scenes through cameras or laser scanning or to acquire open data from a diversity of sources. Professionals without a surveying background can do the job, but such professionals require reliable, robust, easy-to-use and affordable processing tools. Today, cloud computing can meet that need, enabling users to work with software packages running on remote servers on either a pay-per-use or a subscription basis. This article presents a cloud computing service dedicated to geoinformation software@en

The battle against poverty in developing countries has always been associated with the issue of land. Since the turn of the millennium, the focus has been on facilitating the official registration of land rights by the poor and the vulnerable. Many countries in sub-Saharan Africa still lack a well-functioning land administration system, notwithstanding many dedicated aid programmes. Such programmes were designed by Western institutions and often copied Western land administration architectures. In this article, the author advocates a bottom-up approach using census-taking as a paradigm. First the capabilities of advanced technologies are identified, then the way aid is brought to the vulnerable is explored and lastly an approach based on conducting population and housing censuses in sub-Saharan Africa is considered.@en

Did you know that you can waste a week or more per year hunting for a parking space in busy cities? Nowadays, smart parking apps offer a much-needed answer to the problem – and mobile mapping point clouds and imagery are core ingredients of the solution. Besides saving time by guiding you straight to an available spot close to your destination, smart parking apps also reduce unnecessary fuel consumption, air pollution and greenhouse gas emissions. One such app is AppyParking by London-based AppyWay.@en

This paper presents our work on automated classification of Mobile Laser Scanning (MLS) point clouds of urban scenes with features derived from cylinders around points of consideration. The core of our method consists of spanning up a cylinder around points and deriving features, such as reflectance, height difference, from the points present within the cylindrical neighbourhood. Crucial in the approach is the selection of features from the points within the cylinder. An overall accuracy could be achieved, exploiting two bench mark data sets (Paris-rue-Madame and IQmulus & TerraMobilita) of 83% and 87% respectively.

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The demand for 3D maps of cities and road networks is steadily increasing
and mobile mapping systems are often the preferred acquisition method
for capturing such scenes. Manual processing of point clouds is labour
intensive and thus time consuming and expensive. This article focuses
on the state of the art of automatic classification and 3D mapping of
road objects from point clouds acquired by mobile mapping systems and
considers the feasibility of exploiting scene knowledge to increase the
robustness of classification.@en

The ‘smart city’ concept entirely relies on a permanent stream of massive amounts of data acquired by a great variety of sensors distributed throughout the city. Smart use of all this data requires integration with 3D city maps for which point clouds, acquired by laser scanning or photogrammetry, are the main sources. The author of this article identifies the abilities of point clouds to support the smart city concept.@en

A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79% for the Paris-rue-Madame point cloud bench mark data set.@en

The demand for 3D maps of cities and road networks is steadily increasing and mobile mapping systems are often the preferred geo-data acquisition method for capturing such scenes. Manual processing of point clouds is labour intensive and thus time consuming and expensive. This article focuses on the state of the art of automatic classification and 3D mapping of road objects from point clouds acquired by mobile mapping systems and considers the feasibility of exploiting scene knowledge to increase the robustness of classification.@en

The demand for mapping intensively used parts of the Earth, including roads and cities, is steadily growing. This is because today’s rate of urbanisation requires detailed and up-to-date geodata in its full three dimensions. Such datasets help city managers to prevent a decline in liveability, to limit water, air and noise pollution, and to improve fair taxation and rapid emergency response. Crucial is the abstraction of the 3D real world, a concept known as level of detail (LoD). Since 2012 Dr Filip Biljecki has been investigating how to improve the level of detail (LoD) in 3D city models. For his efforts – which were published in a hefty book called Level of Detail in 3D City Models – he recently received a PhD degree from Delft University of Technology. In acknowledgement of the high standard of his research, he graduated with the rarely awarded cum laude distinction, providing GIM International with reason enough to interview him.@en

Today, automatic matching of overlapping aerial imagery and airborne Lidar are the main geodata technologies for capturing dense point clouds of the Earth’s surface. The sampled points are used for the generation of bare ground representations which are often augmented with buildings and trees. Airborne Lidar is flourishing as a prevalent geodata acquisition technology and continues to show a fierce rise in terms of advancements and applications. This article discusses the main technological advances of today’s operational systems and surveys the state of the art, developments and trends.@en

This paper focusses on the feasibility of classifiers, developed for classifying multispectral images, for assigning classes to point clouds of urban scenes. The motivation of our research is that dense point clouds require fast classification methods to extract meaningful information within a reasonable amount of time and multispectral classifiers do have this property. We employ two encoding methods acting on one feature: the altitude above street level. We emphasize computation time and therefore we use just one feature in this prelimina@en

The capability of acquiring accurate and dense three-dimensional geospatial information that covers large survey areas rapidly enables airborne light detection and ranging (LiDAR) has become a powerful technology in numerous fields of geospatial applications and analysis. LiDAR data filtering is the first and essential step for digital elevation model generation, land cover classification, and object reconstruction. The morphological filtering approaches have the advantages of simple concepts and easy implementation, which are able to filter non-ground points effectively. However, the filtering quality of morphological approaches is sensitive to the structuring elements that are the key factors for the filtering success of mathematical operations. Aiming to deal with the dependence on the selection of structuring elements, this paper proposes a novel filter of LiDAR point clouds based on geodesic transformations of mathematical morphology. In comparison to traditional morphological transformations, the geodesic transformations only use the elementary structuring element and converge after a finite number of iterations. Therefore, this algorithm makes it unnecessary to select different window sizes or determine the maximum window size, which can enhance the robustness and automation for unknown environments. Experimental results indicate that the new filtering method has promising and competitive performance for diverse landscapes, which can effectively preserve terrain details and filter non-ground points in various complicated environments@en