Conference Agenda

Cities are increasingly growing in size and becoming denser. This situation calls for strategic planning of green infrastructure in the urban planning process. Safeguarding the green infrastructure is important for maintaining urban ecosystem services and increasing the well-being of urban populations. To facilitate appropriate urban planning and enabling cities to grow sustainably, it is important that the geospatial community provides adequate vegetation information. In this study, we investigate the need for vegetation information in urban planning applications such as modelling ecosystem services and noise, as well as performing case studies of using vegetation information in daylight and solar energy simulations. Based on these investigations, we formulate a recommendation of how vegetation information should be included in 3D city models. The study is focused on the development of a Swedish national profile of CityGML, but many of the conclusions are general. The recommendations are, in short, that: (1) the vegetation theme should follow CityGML 3.0 with some additional attributes (e.g., popular name of tree species) added as an application domain extension, (2) no LOD division is required for the vegetation information stored (but rather derived if necessary), (3) the vegetation theme should only contain 3D vegetation objects while the 2D vegetation is part of the land cover theme, and (4) the building specification (and city furniture specification) must include the possibility to store information if the roof and facades (and walls) are covered with vegetation.

Nowadays, our society is in the transit to adopt more sustainable energy sources to reduce our impact in the environment; one alternative is solar energy. However, this is highly affected by the surroundings, which might cause shadowing effects. In this extended abstract, we present our method to perform shadowing calculations in urban areas using semantic 3D city models. Our initial results allow the identification of locations that are shadowed by nearby buildings at a given epoch. For the final version of the paper, we expect to compare our results with existing works regarding timing and accurancy assesment.

Natural disasters, conflicts and vulnerable supply chains are challenging conditions for the humanitarian aid delivery. This study analyses how a multimodal 3D situational awareness map displaying remote sensing and other geo data could support teleoperated truck missions in this difficult environment, e.g. for route planning, terrain analysis or evaluation purposes. A key focus of this work is the additional value of a 3D visualization compared to established 2D mapping applications. Structured interviews were conducted with end users, scientists and engineers to identify their needs and requirements for a 3D situational awareness map. Based on the outcomes an exemplary 3D web application integrating geodata and crisis information from remote sensing, governmental and open sources was designed and implemented for test sites in Bavaria. In situ drone imagery was captured and added to increase the local situational awareness. The live position of the truck was transmitted to the application during operation and displayed as 3D model. The created web-based application was very well received by the end users. Especially the inclusion of drone imagery draped on the derived surface model in combination with the available satellite data provided a high additional value by highlighting steep slopes or other blockages in the truck’s path.

Solid waste management is a crucial challenge for achieving city sustainability [1]. In 2020, it was estimated that around 2.24 billion metric tons of municipal solid waste were generated worldwide [2]. This waste increased later due to medical waste produced during the COVID-19 pandemic [3, 4]. Approximately 33% of the overall waste generated is not n environmentally safe ways [5]. This has several negative impacts, including health risks, sewage system blockages, soil contamination, and potential disease vectors [6–9]. Despite not being included as a primary Sustainable Development Goal (SDG), addressing solid waste management is related to 12 out of 17 SDGs, making it essential to achieve city sustainability [10, 11].

In South Africa, 30.5 million tons of solid waste were generated in 2017, with only 34.5% being recycled and 11% not having adequate final disposal [12, 13]. The country has an estimated generation of 1.48 kg/capita/day of solid waste, which is higher than the Sub-Saharan average and at similar levels to some countries in Europe and Central Asia [5]. One of the primary challenges in South Africa is reducing the waste disposed in landfills [13], which is hindered by littering, illegal dumping, lack of regular collection services, incomplete coverage, and historical spatial and service delivery inequalities [14, 15].

The growing demand for sustainable mobility has led to an increased focus on the development and improvement of bicycle infrastructure, especially within cities. However, evaluating the quality of existing or planned bicycle paths is a complex task mostly done manually. This paper presents a novel approach for automatically evaluating the service quality of bicycle paths using parameters derived from semantic 3D city and streetspace models compliant with the international OGC standard CityGML version 3.0. These models contain detailed 3D information with lane-level accuracy, including precise outlines of individual surfaces. This allows for accurate and high-resolution evaluations of changing bicycle path widths and slopes, as well as information on adjacent surfaces and local disturbances such as bus stops. Additionally, estimated, measured or simulated bicycle traffic volumes are considered. Based on these parameters a method for calculating the Bicycle Levels of Service (BLOS) described in a national technical regulation is adapted and implemented for a microscopic analysis. Results of this analysis are then transferred back to the original semantic 3D city objects, allowing for the attributive description of BLOS values for bicycle paths. In addition, results are visually represented by coloring corresponding bicycle path segments according to evaluation results and integrating the colored objects within a web-based Cesium visualization of a semantic 3D city model.