This thesis presents the development of a real-time daylight analysis tool for architectural and urban development using the Unreal Game Engine. The tool offers architects and urban planners a fast and precise way of analysing outdoor daylight conditions in their designs. The Unreal Game Engine provides real-time visualisation and analysis of daylight conditions, which makes it an effective tool for real-time decision-making during the design process. The study compares the light values and the process of extracting these values in Unreal with the validated light model Radiance used in Honeybee & Ladybug in the visual scripting program Grasshopper for Rhino. The thesis compares the values of Unreal & Honeybee/ladybug based on outdoor illuminance values, calculation time and ease of use. The comparison demonstrates the potential of Unreal as a valuable daylight analysis tool, with measurements showing a Mean Absolute Error of 9.78% between Honeybee and Unreal. In terms of computational time, the Unreal application requires only 0.6ms to execute and recalculate the daylight analysis, whereas the complete Honeybee script took an average of 93 minutes to calculate daylight values, and a new angle calculation took approximately 645 seconds. Furthermore, unlike Honeybee, which is sensitive to the complexity of urban or complex geometries and requires challenging adjustments, the Unreal application effortlessly accommodates complex geometry without the need for extensive modification. The thesis concludes that the tool provides a robust and efficient method for analysing daylight conditions in architectural and urban design. The tool's ease of use and real-time visualisation capabilities make it an essential addition to the design workflow. Finally, the thesis presents the tool as a proof of concept for a geospatial urban development platform with built-in geospatial analysis. The research has demonstrated the potential of real-time simulation and analysis using the Unreal Game Engine as a powerful tool for architects and urban planners.

The Earth's surface is a complex landscape that is essential for a wide range of applications, from urban planning to environmental monitoring. Digital models of the Earth's surface are generated through mathematical calculations using elevation data collected from various sources and the Digital Terrain Model (DTM) which captures the bare earth's surface topography in 2.5D. The creation of DTM is an approximation of terrain in unsample locations, by using x-y coordinates and one z value. Traditionally, terrain interpolation uses deterministic or geo-statistical methods to calculate elevation. This research would use random forest regression as an alternative method and to compare the results against traditional interpolation. Comparing different locations against traditional interpolation yields similar results overall. Feature importance, within the the points that are closest to the sampled ICESat-2 data point are more significant than other features used in Random Forest model. The correlation between these datasets and the spatial relationship established would impact on the results of the elevation. The improvement overall of using traditional interpolation compared to random forest regression is limited depending on the location and using model trained with local datasets. For model trained on other geographical locations, which shows similar differences.

The accuracy and comprehensiveness of 3D city models have become increasingly important for applications like monitoring, sustainability evaluation, disaster management, and urban planning. However, creating accurate and complete 3D models is challenging. Traditional methods, such as photogrammetry and Light Detection and Ranging (LiDAR), often face issues like time-consuming processes and data gaps caused by occlusion. Additionally, these methods typically produce discrete models that fail to capture all the information from the original objects, limiting the ability to reconstruct small structures. In this thesis, we explore the potential and characteristics of enhancing 3D models for urban areas using implicit neural representation. This method offers generalizability, ensures no void areas, and provides more detailed information when using multi-modal inputs. The datasets used for model training include Actueel Hoogtebestand Nederland 3 (AHN3), 2019 Luchtfoto Beeldmateriaal, 3D Basisregistratie Adressen en Gebouwen (BAG), and Basisregistratie Grootschalige Topografie (BGT). The city center of Eindhoven is used for training and testing, and the city center of Rotterdam serves as a test dataset. The method involves learning location-dependent latent codes from raw point cloud and orthophoto, and adjusting the probability of space occupancy based on point clouds sampled from the 3D city model. A decoder is used to calculate the probability of existence for any point in the 3D space from the continuous field. Proper sampling allows us to derive a continuous Digital Surface Model (DSM) with unlimited resolution from the neural network. The results show a high degree of accuracy: the generated DSM for the training area in Eindhoven demonstrated a median absolute error of 0.484 m overall. Accuracy for building areas was recorded at 0.798 m, and for terrain, it was 0.302 m. The model also displayed robust generalization capabilities, with an accuracy of 0.336 m in the Eindhoven test area and 0.23 meters in Rotterdam. Additionally, the model's ability to fill voids was confirmed through both visual inspection and quantitative evaluations. This research underscores the potential of implicit neural representation for generating detailed DSMs and effectively filling no-data voids.