Arttu Julin

Extensive urban expansion has significantly impacted green spaces leading to the degradation of urban vegetation. Hence, monitoring variations in vegetation using remote sensing methods is essential. However, 2D remote sensing methods have drawbacks as they lack vertical structures in urban areas, shadows caused by buildings, cloud cover and require substantial preprocessing to encounter these limitations. This study focuses on identifying and quantifying changes in Malminkartano, Helsinki during the leaf-off and leaf-on seasons for the year 2022. The research utilized terrestrial laser scanning (TLS) and UAV-photogrammetry datasets for change detection in urban vegetation and point cloud-based algorithms for seasonal variations such as C2C, C2M, and M3C2. Notably, many existing methods involve rasterizing point clouds as DSM which results in the loss of significant information. Therefore, this paper investigates the potential of utilized datasets in detecting changes directly on point clouds. However, there are uncertainties associated with point clouds including data registration, point density, weather effects, and misalignment therefore this study aims to take these limitations into account. The results from TLS and UAV-photogrammetry demonstrated competence in identifying the maximum growth of urban vegetation up to 2.0 m and 2.8 m respectively. However, the accuracy assessment of data corresponded to a 4 cm difference in both datasets at a 95% confidence threshold and potential vertical height differences accounted for the difference in change detection. This study underscores data processing uncertainties associated with registration, vertical height, and data noise and proposes the integration of point clouds with different sensors for completeness and improved change detection in urban vegetation.

This study explores the reuse of high-resolution 3D spatial datasets for multiple urban analyses within a game engine environment, aligning with circular economy principles in sustainable urban planning. The work is situated in two residential test areas in Finland, where watershed analysis, lighting simulation, and change detection were conducted using data acquired through drone photogrammetry and terrestrial laser scanning. These datasets were processed and visualized using Unreal Engine 5.5, enabling the interactive, multitemporal exploration of urban phenomena. The results demonstrate how a single photogrammetric dataset—originally captured for visual or structural purposes—can serve a broad range of analytical functions, such as simulating seasonal lighting conditions, modeling stormwater runoff, and visualizing spatial changes over time. The study highlights the importance of capturing data at a resolution that satisfies the most demanding intended use, while allowing simpler analyses to benefit simultaneously. Reflections on game engine capabilities, data quality thresholds, and user interactivity underline the feasibility of integrating such tools into citizen participation, housing company decision making, and urban governance. The findings advocate for a circular data approach in urban planning, reducing redundant fieldwork and supporting sustainable data practices through multi-purpose digital twins and spatial simulations.

Increasing interest has recently been shown towards the digital twins of cities. These urban digital twins utilize 3D city models together with other data sources, such as open data, which can be accessed, e.g., through application programming interfaces (APIs). A large amount of open geospatial data is already available through various open data portals and APIs, such as REST and MQTT. Game engines, on the other hand, have been seen as highly potential platforms for urban digital twins, with recent development increasing the useability of geospatial data in game engines. This study explores the possibilities of game engines for urban digital twins by integrating and visualizing API-based open geospatial datasets with a 3D city model data set in a game engine environment. Three open data integration approaches are demonstrated, and the performance of these approaches is tested for urban digital twins. Based on the results, modern game engines offer a great platform for creating urban digital twin applications; however, some problems and limitations must be overcome before the use of geospatial data in game engines becomes feasible on a larger scale.

The importance of ensuring the adequacy of urban ecosystem services and green infrastructure has been widely highlighted in multidisciplinary research. Meanwhile, the consolidation of cities has been a dominant trend in urban development and has led to the development and implementation of the green factor tool in cities such as Berlin, Melbourne, and Helsinki. In this study, elements of the green factor tool were monitored with laser-scanned and photogrammetrically derived point cloud datasets encompassing a yard in Espoo, Finland. The results show that with the support of 3D point clouds, it is possible to support the monitoring of the local green infrastructure, including elements of smaller size in green areas and yards. However, point clouds generated by distinct means have differing abilities in conveying information on green elements, and canopy covers, for example, might hinder these abilities. Additionally, some green factor elements are more promising for 3D measurement-based monitoring than others, such as those with clear geometrical form. The results encourage the involvement of 3D measuring technologies for monitoring local urban green infrastructure (UGI), also of small scale.

We aim to present a method to measure 3D luminance point clouds by applying the integrated high dynamic range (HDR) panoramic camera system of a terrestrial laser scanning (TLS) instrument for performing luminance measurements simultaneously with laser scanning. We present the luminance calibration of a laser scanner and assess the accuracy, color measurement properties, and dynamic range of luminance measurement achieved in the laboratory environment. In addition, we demonstrate the 3D luminance measuring process through a case study with a luminance-calibrated laser scanner. The presented method can be utilized directly as the luminance data source. A terrestrial laser scanner can be prepared, characterized, and calibrated to apply it to the simultaneous measurement of both geometry and luminance. We discuss the state and limitations of contemporary TLS technology for luminance measuring.

3D city models and their browser-based applications have become an increasingly applied tool in the cities. One of their applications is the analysis views and visibility, applicable to property valuation and evaluation of urban green infrastructure. We present a near real-time semantic view analysis relying on a 3D city model, implemented in a web browser. The analysis is tested in two alternative use cases: property valuation and evaluation of the urban green infrastructure. The results describe the elements visible from a given location, and can also be applied to object type specific analysis, such as green view index estimation, with the main benefit being the freedom of choosing the point-of-view obtained with the 3D model. Several promising development directions can be identified based on the current implementation and experiment results, including the integration of the semantic view analysis with virtual reality immersive visualization or 3D city model application development platforms.

Computer-aided 3D modelling is the standard design method in the architecture, engineering, construction, owner, operator (AECOO) industry and in lighting design. Applying a photogrammetric process, a sequence of images is used to reconstruct the geometry of a space or an object in a 3D model. Likewise, a calibrated digital camera is utilized to measure the surface luminance values of an environment or an object. We propose a workflow in which the geometry and the luminance values are measured simultaneously by combining these two measurement methods. The pipeline has been assessed and validated through the application to a case study, the Aalto Hall (Aalto University, Espoo, Finland), in order to understand its potential.

Terrestrial laser scanning (TLS) enables the efficient production of high-density colored 3D point clouds of real-world environments. An increasing number of applications from visual and automated interpretation to photorealistic 3D visualizations and experiences rely on accurate and reliable color information. However, insufficient attention has been put into evaluating the colorization quality of the 3D point clouds produced applying TLS. We have developed a method for the evaluation of the point cloud colorization quality of TLS systems with integrated imaging sensors. Our method assesses the capability of several tested systems to reproduce colors and details of a scene by measuring objective image quality metrics from 2D images that were rendered from 3D scanned test charts. The results suggest that the detected problems related to color reproduction (i.e., measured differences in color, white balance, and exposure) could be mitigated in data processing while the issues related to detail reproduction (i.e., measured sharpness and noise) are less in the control of a scanner user. Despite being commendable 3D measuring instruments, improving the colorization tools and workflows, and automated image processing pipelines would potentially increase not only the quality and production efficiency but also the applicability of colored 3D point clouds.

Roadside vegetation can affect the performance of installed road lighting. We demonstrate a workflow in which a car-mounted measurement system is used to assess the light-obstructing effect of roadside vegetation. The mobile mapping system (MMS) includes a panoramic camera system, laser scanner, inertial measurement unit, and satellite positioning system. The workflow and the measurement system were applied to a road section of Munkkiniemenranta, Helsinki, Finland, in 2015 and 2019. The relative luminance distribution on a road surface and the obstructing vegetation were measured before and after roadside vegetation pruning applying a luminance-calibrated mobile mapping system. The difference between the two measurements is presented, and the opportunities provided by the mobile 3D luminance measurement system are discussed.