Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road scanning opportunity. Many methods are available for road pavement, curb and roadside way extraction. Most of them use classical approaches that do not mitigate problems caused by the presence of noise and outliers. In practice, however, laser scanning point clouds are not free from noise and outliers, and it is apparent that the presence of a very small portion of outliers and noise can produce unreliable and non-robust results. A road surface usually consists of three key parts: road pavement, curb and roadside way. This paper investigates the problem of road surface extraction in the presence of noise and outliers, and proposes a robust algorithm for road pavement, curb, road divider/islands, and roadside way extraction using MLS point clouds. The proposed algorithm employs robust statistical approaches to remove the consequences of the presence of noise and outliers. It consists of five sequential steps for road ground and non-ground surface separation, and road related components determination. Demonstration on two different MLS data sets shows that the new algorithm is efficient for road surface extraction and for classifying road pavement, curb, road divider/island and roadside way. The success can be rated in one experiment in this paper, where we extract curb points; the results achieve 97.28%, 100% and 0.986 of precision, recall and Matthews correlation coefficient, respectively.

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Change detection is essential to keep 3D city models up-to-date. LiDAR data with high accuracy are used to create 3D city models. However, updating LiDAR data at state or nation level often takes around a decade. Very high resolution (VHR) stereo images, with often yearly updating rate and dense 3D information, provide an option for validating and updating LiDAR data. However, the 3D information in both data sources has quality problems. LiDAR point clouds are sparse and irregularly spaced, and have mixed returns near building edges, while 3D information extracted from stereo images are affected by shadow and low texture. This research proposes LiDAR-guided dense matching to address these problems explicitly for detecting accurate building changes. Data sparsity and irregular spacing is addressed by densifying LiDAR points in a form of a digital surface model (DSM). Instead of applying interpolation with associated edge problems due to mixed returns, three candidate DSMs are created by linking each DSM pixel to up to three planes as identified in segmented and triangulated LiDAR data. The candidate DSMs limit the disparity search space for dense matching, addressing low texture and shadow problems in images. Through edge-aware dense matching, the detailed building edge information in stereo pairs determine the optimal heights to address LiDAR edge problem. Changes are detected where corresponding pixels from dense matching have large color differences. Due to homogeneous surroundings and shadows, only partial changes are initially detected. A second hierarchical dense matching step is employed to complete changes and update 3D information by propagating initial partial changes iteratively. The proposed method is applied on data from two cities, Amersfoort and Assen, the Netherlands, with around 1200 existing buildings. In both areas, the method successfully verifies unchanged buildings while detecting minimum changes of 2×2×2m³. New and removed building detection in Amersfoort both have a F1 score of over 0.8, both in pixel and object evaluation, while F1 scores in Assen are over 0.9 for both categories. The experiments also show that the proposed method outperforms two well-known change detection methods in terms of verifying unchanged buildings and detecting small changes simultaneously.

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Indoor environments differ from outdoor in many aspects. This, added to the limitations faced by other common standards for urban features reinforced the need of setting a dedicated standard for indoor applications. IndoorGML was born in this context to provide the basic concepts, data models, and standard that meet the requirements of indoor spatial applications. Indoor spatial information can be generally classified into two categories: indoor objects such as architectural components (walls, stairs, slabs) and interior facilities (furniture); indoor spaces such as cavities (rooms and corridors) or virtual subdivision (sensor and legal spaces). Handling both information is necessary to support applications ranging from Indoor location-based services (LBS), indoor route analysis or indoor geo-tagging to building and asset management. In this paper, we present the proposed changes to the second version of IndoorGML, under preparation and intended to provide the necessary support for applications using information from those two categories. IndoorGML 2.0 is open to all applications that rely on indoor spaces and require analysis that can be performed on a network, extracted from those spaces utilizing neighbourhood relationships. It follows a model-driven approach, i.e. all concepts are presented by the Unified Modelling Language, from which technical implementations are derived (GML, JSON, SQL, etc.). We present the proposed changes to the previous version, illustrate a way of representing indoor objects other than spaces and discuss several use cases of the standard.

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During an incident, many people that are located in indoor environments require to follow emergency evacuation procedures. The ‘emergency evacuation’ term has been defined as ‘a critical movement of people from a dangerous area due to the risk or an incident of a tragic event’ (Bonabeau, 2002). An emergency evacuation could be needed in a life or death situation, regardless if it begins with a natural non-intended incident or a terrorist attack. Many researchers have studied the behaviour of the people during the evacuation because of several incidents with panic attacks that have led to injuries including death of people being crushed or trampled down by others. In crisis situation, the perception of the indoor environment, which differs from person to person, play a critical role in the evacuation. Also, the access rights of the indoor spaces are different from those rights (and restrictions) during normal times. They may positively impact the movements of the people during the evacuation by providing suggestions for shorter/better route. This paper addresses the impact of the access rights of the indoor spaces during an emergency evacuation. We employ the conceptual model of LADM-IndoorGML that defines the accessibility of the indoor spaces based on the rights, restrictions, and responsibilities of the user of the indoor space. The access rights of the indoor spaces are affected by the crisis event and this needs to be modelled explicitly (and before crisis situation). Actually, the rights/restrictions persons have on spaces is time dependent: normal operation hours, outside normal operation hours (e.g. during night time in case of a University building) or during crisis times. These actual/valid rights and restrictions affect the movement / accessibility of the users to reach the nearest emergency exits or the safe zone. For this reason, different scenarios have to be developed to study the impact of the accessibilities for different types of users. In this paper we will present the 3D model of an educational building that was built for the purpose of evacuation study. The 3D model is supported by real data for all spaces from the facility management department such as information on departments, sections, groups of users (visitors, employees, and students), and public/private spaces, etc. and a real evacuation exercise. We consider it extremely important to develop our information model based on international standards (LADM/ISO 19152, OGC IndoorGML, ISO 19141, ISO 19107) as we expect that this information will be part of the future ‘building infrastructure’ and applications all over the world can understand and use this data when entering or leaving a certain building both during normal and crisis situations. Different types of applications are anticipated to be based on this information model; e.g. mobile indoor routing app (for normal building users and Emergency Response Team members), crisis evacuation desktop application for command centre, etc.@en