In the last 15 years semantic 3D city models have seen a steady growth in terms of creation and adoption. Many cities world-wide have now at least one city model which can be used for several applications. Energy- and sustainability-related topics are among those that have experienced a noteworthy increase of interest from the Geomatics community. 3D city models have become a steady component of Urban Energy Modelling, in which bottom-up approaches are developed to assess, for example, the energy efficiency of the building stock and to explore different scenarios of building refurbishment. Within this context, this paper focuses on investigating how much party walls can contribute to the energy demand estimation of a building. For this reason, two approaches to compute party walls are described and compared. The nature and the magnitude of their differences, as well as their possible impact on downstream applications, are considered in order to shed light on whether discrepancies in the amount of computed party wall area might lead to significant differences in terms energy demand of the residential building stock. The case study area is located in the Netherlands and encompasses the municipality of Rijssen-Holten.

This document introduces the process for the creation of a testbed for energy applications based on a semantic 3D city model for the municipality of Rijssen-Holten in The Netherlands. The creation of this dataset requires the consolidation from multiple data sources as well as a lot of manual work so the authors can warranty as much as possible the quality of the dataset so in can be used in several use cases. The data is stored following the OGC standard CityGML v2.0 and contain the geometrical and semantical information of CityObjects from the thematic modules Building, Vegetation and Relief. This data set consolidates the open weather data from the closest weather station to the study area located in Heino in the Netherlands. We discuss the decisions taken during the manual data collection process and we present some use cases that have already consume the dataset at the time of writing this document.

3D city models are frequently used to acquire and store energy-related information of buildings for energy applications. In this context, CityGML is the most common data model, and the Energy ADE, one of its most complex extensions, provides a systematic way of storing detailed energy-related data in XML format. Contrarily, even though CityGML's JSON-based encoding, CityJSON, has an extension mechanism, an energy-related CityJSON Extension is missing. This paper, therefore, presents the first results of the development of a CityJSON Energy Extension and space heating demand calculation is utilized as the use case. The simplified version of the Energy ADE, called the Energy ADE KIT profile, is used to create a semi-direct translation to the CityJSON Energy Extension. This Extension is then validated through the official validator of CityJSON and the use case, and improvements are made considering the validation results. The space heating demand is calculated according to the Dutch standard NTA 8800 for a subset of Rijssen-Holten in the Netherlands although the solar gains calculation requires further review. The results show that the final CityJSON Energy Extension provides full support for space heating demand calculations based on the NTA 8800 and eliminates the deep hierarchical structure of the Energy ADE. A comparison on CityJSON file sizes shows a 25.2 MB increase after the required input data is stored in a CityJSON + Energy Extension file, which is not significant considering the high amount of data stored in the file. Overall, this paper shows that the CityJSON Energy Extension could provide an easy-to-use alternative to the CityGML Energy ADE.

Indoor navigation is a complex task for people and especially for visually impaired ones. This research proposes an indoor navigation system oriented to visually impaired users, integrating the OGC IndoorGML and CityGML standards with Bluetooth Low Energy devices (BLE) by using the RSSI signal loss value and the Weighted Path Loss-techniques to calculate the user's location. This paper describes the design of the system and its implementation as a functional prototype in a mobile web application. Several operational tests were conducted to determine both the accuracy and precision of the user location. The user's positioning results show a root-mean-squared error (RMSE) of 0.88 m in a scenario with obstacles and no height difference of the BLE devices location, and a RMSE of 1.06 m in a scenario with obstacles and height difference. These results confirm the potential of the implemented prototype to grow into a fully operational system.

In this paper, five commonly used software tools to estimate solar radiation in the urban context (GRASS GIS, ArcGIS, SimStadt, CitySim and Ladybug) are run on the same test site and are compared in terms of input data requirements, usability, and accuracy of the results. Spatial and weather data have been collected for an area located in the Brazilian city of São Paulo, in the district of Santana. The test area surrounds a weather station, for which meteorological data of the last 15 years have been collected and used as ground truth when analysing and comparing the simulation results. In terms of spatial data, raster-and vector-based models of the study area have been generated in order to comply with the different input requirements. More specifically, in the case of the vector-based tools (SimStadt, CitySim and Ladybug), a common 3D model based on CityGML and containing buildings, vegetation (trees) and terrain has been generated and used as a common urban model. The paper presents the findings and discusses the results not only from a numerical point of view, but also from the perspective of the overall usability of the software in terms of data requirements, simulation time and task automatisation.

3D city models are playing a growing role worldwide as sources of integrated information upon which different urban applications are developed. In the context of urban planning and design, semantic 3D city models can provide plenty of qualitative and quantitative information about the urban context and of the area(s) to be transformed. This paper takes inspiration and continues a work recently published in which several design parameters and Key Performance Indicators are computed from a semantic 3D city model, and later used in a GIS-supported urban design process to develop a new area. As many of such parameters are derived from the gross volume of the building stock, this paper investigates whether and to which extent different building stock models might affect the estimation of the gross volume. The study is carried out in anticipation of the upcoming LoD2-based, country-wide model of the Netherlands that is being finalised by our team. At the same time, the paper investigates whether and which information can be obtained regarding the quality of the LoD2 model from a comparison with the LoD1 one, with a focus on volume calculation.

The 3D BAG v. 2.0 dataset has been recently released: it is a country-wide dataset containing all buildings in the Netherlands, modelled in multiple LoDs (LoD1.2, LoD1.3 and LoD2.2). In particular, the LoD2.2 allows differentiating between different thematic surfaces composing the building envelope. This paper describes the first steps to test and use the 3D BAG 2.0 to perform energy simulations and characterise the energy performance of the building stock. Two well-known energy simulation software packages have been tested: SimStadt and CitySim Pro. Particular care has been paid to generate a suitable, valid CityGML test dataset, located in the municipality of Rijssen-Holten in the central-eastern part of the Netherlands, that has been then used to test the energy simulation tools. Results from the simulation tools have been then stored into the 3D City Database, additionally extended to deal with the CityGML Energy ADE. The whole workflow has been checked in order to guarantee a lossless dataflow. The paper reports on the proposed workflow, the issues encountered, some solutions implemented, and what the next steps will be.

The rapid and precise detection of diseases and plant disorders is the basis for the adequate and timely design of management strategies. Currently, there are several non-destructive alternatives that allow early detection, highlighting the use of spectral cameras attached to unmanned aerial vehicles (UAVs). The objective of this research was to evaluate the use of multispectral cameras on UAVs to discriminate vascular wilt caused by Verticillium spp., (VW), waterlogging stress (WL), and an unknown alteration (UA) in commercial potato (Solanum tuberosum) variety “Diacol Capiro” crops. Plots were monitored during the crop cycle, performing the visual characterization of the diseases and disorders present. Five spectral band images were acquired using a MicaSense RedEdge spectral camera attached to a Map-T680 hexacopter drone to extract the bands and calculate the vegetation indices that were calibrated and evaluated to determine their ability to discriminate between diseased and healthy plants based on a generalized linear model (GLM) and Kappa index. Additionally, the supervised random forest classification method was implemented, optimized, and evaluated using the accuracy, area under receiver operating characteristic curve (ROC-AUC), kappa index, and inference error based on k-fold cross-validation. After algorithms optimization our results show a classifier accuracy, kappa and ROC-AUC values to VW, WL and UA between 73.5–82.5%, 0.56–0.71, 0.97–0.98, and 35 37.5–51.9%, 0.07–0.06, and 0.88–0.94 for plots 1 and 2, respectively. This study reports an approach to the use of multispectral cameras attached to UAVs as a tool with potential for the detection of diseases and physiological disorders in commercial potato crops.