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.

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.

The paper describes an approach to quantify the amount of projective error produced by an offset of projection centres in a panoramic imaging workflow. We have limited this research to such panoramic workflows in which several sub-images using planar image sensor are taken and then stitched together as a large panoramic image mosaic. The aim is to simulate how large the offset can be before it introduces significant error to the dataset. The method uses geometrical analysis to calculate the error in various cases. Constraints for shooting distance, focal length and the depth of the area of interest are taken into account. Considering these constraints, it is possible to safely use even poorly calibrated panoramic camera rig with noticeable offset in projection centre locations. The aim is to create datasets suited for photogrammetric reconstruction. Similar constraints can be used also for finding recommended areas from the image planes for automatic feature matching and thus improve stitching of sub-images into full panoramic mosaics. The results are mainly designed to be used with long focal length cameras where the offset of projection centre of sub-images can seem to be significant but on the other hand the shooting distance is also long. We show that in such situations the error introduced by the offset of the projection centres results only in negligible error when stitching a metric panorama. Even if the main use of the results is with cameras of long focal length, they are feasible for all focal lengths.