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As urbanisation accelerates, the urban landscape reshapes at a fast pace. Consequently, the urban built environment continuously evolves horizontally as well as vertically. However, more attention in the field of spatial analysis is given to horizontal dynamics, despite the importance of geoinformation in the vertical dimension. 3D modelling methods have gained popularity due to their powerful capability of capturing and analysing geoinformation in the vertical dimension and visualising objects lifelike in the urban built environment. Various urban applications with diverse 3D modelling methods at different research scales and purposes have emerged. However, there is no systematic overview of these different modelling methods. Therefore, it is imperative to provide an up-to-date review of these advances. In this paper, we aim to review urban 3D modelling methods widely used in the prior 5-year period (2015–2020). Our analysis focuses on five attributes, i.e., basic characteristics, data requirements, technical requirements, users requirements and ethical considerations. The discussion presents the current status of 3D modelling methods – a wide range of applications yet with substantial development potential. This paper closes with insights for future work regarding the necessities of 3D data structure support as well as interdisciplinary research, specifically for big data management and integration.

This paper presents our contribution to the development of a standardized 3D input data model for solar photovoltaic potential estimation. Presently, different input data and processing steps influence the calculation for estimating the potential of solar energy in the Netherlands. The variety in characteristics of input data and issues with temporal accuracy extracted from the national registers and databases makes it challenging to obtain a consistent and reliable result. To address this issue, we created a point cloud dataset that integrated from LiDAR point cloud and dense image matching which is complete, recent and positionally accurate. Furthermore, we made a 3D building model from the integrated point cloud and identified the effect of finer resolution in the photovoltaic potential analysis.

With the rapid urbanization and the dynamics in land transactions, Turkish cadastre, as in many other countries, is facing challenges in the daily recording of sales, donations, taxations, and mortgages on land. Especially with the modern constructions and complex interrelationships of rights, restrictions, and responsibilities (RRR), the third dimension should not be forgotten. In this context, the design of a new cadastre data model for Turkey is essential. Inspired by this need, with this study, we aim in proposing a new cadastre model based on international standards such as Land Administration Data Model (LADM) and CityGML. LADM represents the legal and administrative aspects of the cadastral objects but does not show the semantic and 3D geometrical representation of physical cadastral objects which are required for the process of 3D cadastre. Therefore, in the paper, we propose an Application Domain Extension (ADE) for the cadastral objects that expands the integration of LADM and CityGML data model with the legal and administrative concepts defined in the Turkish Law. The study presents a detailed overview of the Turkish legal cadastre system and a proposal for its physical realization based on international standards. In addition, the developed ADE is also valuable for cadastral services undertaken by the General Directorate of Land Registry and Cadastre (GDLRC). It could also be used as a basis of a 3D national data standard for cadastral information systems.

Point clouds serve as the raw material for various models, such as Building Information Models (BIM). In this work, we investigate the reconstruction steps needed to create models that can be utilized directly for agent-based simulations. The input data for the reconstruction is captured with an indoor mobile mapping system. To show the prominence of this idea, we run social distancing and evacuation simulations on the reconstructed models. The simulations are run with multiple agents using a vision-based pedestrian model and A∗-based path finding algorithm. The limitations of this approach are discussed. The video of the simulation is shared with the audience. Link to the video: https://youtu.be/r2D3IxXt7Ls

Dimensionality reduction is the most widely used approach for extracting the most informative low-dimensional features from highdimensional ones. During the last two decades, different techniques (linear and nonlinear) have been proposed by researchers in various fields. However, the main question is now how well a specific technique does this job. In this paper, we introduce a qualitative method to assess the quality of dimensionality reduction. In contrast to numerical assessment, we focus here on visual assessment. We visualize the Minimum Spanning Tree (MST) of neighborhood graphs of data before and after dimensionality reduction in an immersive 3D virtual environment. We employe a mixture of linear and nonlinear dimension reduction techniques to apply to both synthetic and real datasets. The visualization depicts the quality of each technique in term of preserving distances and neighborhoods. The results show that a specific dimension reduction technique exhibits different performance in dealing with different datasets.

The volume of Earth Observation data is increasing immensely in order of several Terabytes a day. Therefore, to explore and investigate the content of this huge amount of data, developing more sophisticated Content-Based Information Retrieval (CBIR) systems are highly demanded. These systems should be able to not only discover unknown structures behind the data, but also provide relevant results to the users’ queries. Since in any retrieval system the images are processed based on a discrete set of their features (i.e., feature descriptors), study and assessment of the structure of feature space, build by different feature descriptors, is of high importance. In this paper, we introduce a clustering-based approach to study the content of image collections. In our approach, we claim that using both internal and external evaluation of clusters for different feature descriptors, helps to understand the structure of feature space. Moreover, the semantic understanding of users about the images also can be assessed. To validate the performance of our approach, we used an annotated Synthetic Aperture Radar (SAR) image collection. Quantitative results besides the visualization of feature space demonstrate the applicability of our approach.