The high density of the urban fabric poses a real challenge for adequate daylight design in residential buildings. European and national building standards do not provide sufficient guidelines on if and how to consider the urban context at design stage. This study assessed the impact of simulating different urban densities on the indoor daylight performance of typical Dutch apartments. Results showed that not including the surrounding environment when designing a new building leads up to an 85% overestimation of daylight performance, causing an insufficient daylight provision for most apartments built at the lower floors. Furthermore, settling for daylight target values any lower than the minimum standards specified by EN17037 (median illuminance of 300 lx) will lead to insufficient melanopic light levels. In this regard, two new metrics are introduced to compare the non-visual performance between apartments: Melanopic Autonomy and Melanopic Isotropy. These metrics enable the characterisation of non-visual performance of an entire space, rather than of a single occupant position. Last, the analysis explored the relationship between indoor daylight performance and urban density indicators; while the results are limited to the sample considered in this study, a promising relation was noticed for the floor-space index and for the open-space ratio.

Estimating the losses in the immediate aftermath of an earthquake is a key component of seismic response. Seismic rapid-loss estimates provide first responders with a prediction of where and what to prepare for. Improving the precision of quick loss estimates requires an estimate of how a buildings in the affected zone may have reacted to an event. Structural response prediction models are a novel approach to estimating building response from the observed displacement of instrumented buildings. Current SRPMs are built on relatively small databases but offer potential for expansion. There exists no robust building-specific database which could facilitate the construction of these models. As a reaction to this gap, this study applies, abstractly and concretely, the OGC SensorThings data model to building seismograph records. The harmonized records form part of a proposed abstract and concrete Structural Response Prediction Model to make estimates of building-response on other un-instrumented buildings. The utility of a abstracted observation data-model and pipeline is shown, with the potential for unifying existing data-sources. The work shall show that the OGC SensorThings integrates generally well, with some limitations, with the requirements of seismic observation record keeping.

The production of cocoa beans contributes to 7.5 % of European Union (EU) driven deforestation. As a result, the recent European Union Deforestation-free Regulation (EUDR) mandates producers to track cocoa farm extents comprehensively. While Remote Sensing has enormous capacity in dynamic crop monitoring, cocoa crop detection shows challenges due to cocoa complex canopy structure, spectral similarity to forest, variable farming methods, and location in frequently cloudy regions. Previous research on cocoa crop detection has mainly focused on pixel-based classification, disregarding spatial context. In this research we have performed a semantic segmentation approach to incorporate spatial configuration and enhance cocoa crop detection. We have applied Convolutional Neural Network (CNN) for the to semantic segmentation of cocoa parcels, considering both spectral and spatial characteristics. Additionally, we have evaluated the impact of combining Synthetic Aperture RADAR (SAR) and MSI (Multi-Spectral Imagery) data in the training of a CNN to demonstrate the importance of texture, moisture, and canopy characteristics in identifying cocoa canopies. The impact of MSI dataset stack with different SAR polarizations, seasons and temporality has been evaluated. The methodology is tested on Sentinel 1 and 2 data over an area of 100 × 100 km in Ghana for which an extensive ground truth data set of almost 90,000 polygons was available for training and validation. The results show that the addition of single-day and temporal SAR to a single-day MSI image can improve the predictions, reaching an F1 score of 86.62 %. This research demonstrates the influence of SAR measurements, seasons, polarization, and ground truth classes on the semantic segmentation of cocoa.

As urban architectural environments become increasingly complex and densely populated, the demand for precise registration of legal statuses, encompassing both private and public interests, has become more urgent. Traditional 2D cadastral registration systems are increasingly inadequate for addressing the multifaceted and vertical nature of modern urban landscapes. These systems are limited in scope and unable to fully capture the intricacies of multi-level property rights, overlapping parcels, and underground constructions.

This study uses a BIM/IFC model for the building's physical representation. The party and Rights, Restrictions, and Responsibilities (RRRs) data are stored in a DBMS following ISO 19152-2 (Land Administration Domain Model, LADM) All data and the building location are fictitious and represent the most important categories of Land Administration cases. Visualization and interaction is achieved in 3D over the web using Cesium JS, an extensible globe viewer.
Unlike earlier 3D cadastral systems, this research has developed a new 3D Land Administration prototype based on the complete scope of LADM and not just focusing on the 3D spatial information. The objective is to explore improved methods for analyzing and visualizing RRRs in complex buildings. Novel techniques include presenting UML instance-level LADM diagrams for selected parties and/or apartments, showing RRRs and BAUnits linking them to the spatial units. The study further introduces a new method for displaying surrounding buildings at varying Level of Detail (LoD), with closer buildings rendered in higher detail and more distant buildings shown in less detail. This selective detailing enhances both performance and clarity in visualizations.

A key feature of this digital twin system is its real-time update capability. The prototype developed in this study supports the updating of party and rights information in the backend database, accurately reflecting these updates in the public front-end version. This ensures the maintenance and visualization of the most current property rights data. The system also integrates sunlight simulation, which is crucial for urban planning, architectural design, and aiding buyer decision-making. The prototype is (still) online available and via a usability study evaluated.

Transforming the global energy sector from fossil-fuel based to renewable energy sources is crucial to limiting global warming and achieving climate neutrality. The decentralized nature of the renewable energy system allows private households to deploy photovoltaic systems on their rooftops. However, inconsistent data on installed photovoltaic (PV) systems complicate planning for an efficient grid expansion. To address this issue, deep-learning techniques, can support collecting data about PV systems from aerial and satellite imagery. Previous research, however, lacks the consideration for ground truth data-specific characteristics of PV panels. This study aims to implement a semantic segmentation model that detects PV systems in aerial imagery to explore the impact of area-specific characteristics in the training data and CNN hyperparameters on the performance of a CNN. Hence, a U-Net architecture is employed to analyze land use types, rooftop colors, and lower-resolution images. Additionally, the impact of near-infrared data on the detection rate of PV panels is analyzed. The results indicate that a U-Net is suitable for classifying PV panels in high-resolution aerial imagery (10 cm) by reaching F1 scores of up to 91.75% while demonstrating the importance of adapting the training data to area-specific ground truth data concerning urban and architectural properties.

The objective of this paper is to investigate and propose a method for Indoor Localisation based on Isovists, with the aim of extending the fields of Location-based Services and Geomatics. Various methods and combinations incorporating Isovist concepts, Space Syntax, and visibility graphs are examined and assessed. By investigating these approaches, this study aims to create a comprehensive methodology to achieve localisation using Isovists. The main conclusion drawn from this research is that an Indoor Localisation method based on Isovists is not only feasible but can also effectively support Location-based Services. The analysis and evaluation of all the components have been thoroughly conducted, indicating that when properly integrated, they can provide substantial value for LBS applications. As this is a new method for Indoor Localisation, there is significant scope for future work, particularly in terms of connecting it with existing techniques and integrating them into user applications.

Localisation and navigation technologies have vastly evolved during the last years, facilitating users’ guidance in various environments. Unlike outdoor environments where GNSS comprises a universal solution, in indoor environments various localisation techniques have been used, each one with its drawbacks. Thus, this research investigates the reliability of the ceilings towards indoor localisation, by using components that are included in a simple mobile device. The choice of ceilings lies in their advantages, which include the incorporation of various characteristic components, as well as the absence of obstacles between them and the sensor. Indoor localisation is achieved based on LiDAR point clouds and images from RGB sensors of mobile devices. Additionally, this research involves location tracking of different users, to discover different movement patterns in an indoor facility. The proposed methodology revealed the robustness of the Coloured ICP algorithm for in-door localisation based on point clouds, both in terms of time efficiency and quality, while the combination of the SURF feature detector and SIFT descriptor provides the optimal indoor localisation results with image data. The proposed pipeline revealed encouraging results for use in emergencies, based on static data acquisition of a user, while it is also suitable for dynamic applications, in case a sensor is mounted on an automated device for indoor mapping operations. The captured point clouds of the ceilings can also be used as a reference to CAD and BIM models, to help the modelling of the existing utilities and their components in an indoor facility.

With increased urbanization and the impacts of climate change, cities around the world are making resilience-building a priority. Simultaneously, advances in technology have enabled the creation of City Digital Twins (CDTs). Informed by a literature review and interviews with resilience and Digital Twin experts, this paper explores how CDTs might support the development of more resilient urban communities. First, the various definitions of urban resilience, smart cities and CDTs are described. Second, the paper explores how characteristics of CDTs make them uniquely equipped to facilitate (1) a better understanding of complex phenomena, (2) the imagination of possible futures and (3) collaboration between stakeholders. Finally, the technical requirements and challenges of CDT implementation are discussed, including (1) identifying priority hazards and users, (2) collecting and managing data, (3) integrating different models and (4) ensuring usability. The paper concludes by emphasizing the important role of stakeholders in shaping CDTs that can be successfully integrated by the communities they serve.

CAAD Futures is a biennial international conference on Computer-Aided Architectural Design under the umbrella of the CAAD Futures Foundation, and it is active world-wide in advancing and documenting related research. On 5–7 July 2023, the 20th CAAD Futures conference was hosted at Delft University of Technology. The CAAD Futures Foundation was established in 1985, holding the first conference on 18–19 September of that year at the very same University. The return of the conference to Delft for its 20thedition offered a chance to reflect on the past, present and future role of Computation in Architecture and the Built Environment. With reference to the theme of “INTERCONNECTIONS: Co-computing beyond boundaries”, CAAD Futures 2023 reflected on the role of computation to interconnect in and for Architectural Design.