The automated 3D modeling of indoor spaces is a rapidly advancing field, in which recent developments have made the modeling process more accessible to consumers by lowering the cost of instruments and offering a highly automated service for 3D model creation. We compared the performance of three low-cost sensor systems; one RGB-D camera, one low-end terrestrial laser scanner (TLS), and one panoramic camera, using a cloud-based processing service to automatically create mesh models and point clouds, evaluating the accuracy of the results against a reference point cloud from a higher-end TLS. While adequately accurate results could be obtained with all three sensor systems, the TLS performed the best both in terms of reconstructing the overall room geometry and smaller details, with the panoramic camera clearly trailing the other systems and the RGB-D offering a middle ground in terms of both cost and quality. The results demonstrate the attractiveness of fully automatic cloud-based indoor 3D modeling for low-cost sensor systems, with the latter providing better model accuracy and completeness, and with all systems offering a rapid rate of data acquisition through an easy-to-use interface.

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.