In recent years, there has been an exponential growth in the need for 3D spatial information, particularly 3D models representing the geographic information of the urban environment. The existence of a comprehensive integrated 3D model representing the condition of the underground utility networks in accordance with the above-ground city objects is crucial in the ever-increasing infrastructure demands. To acquire such models the related spatial information (geo-information) is required. This information and its quality determine the quality of the models since it describes the functionality, physical properties, semantic details of the urban features as well as the between them relationships (if exist) and interconnections. Currently, there are available various 3D models that, however, are limited in the representation of objects at a city level, with the corresponding underground information about the utility networks supporting cities’ functionality being restricted and/or underdeveloped. Although the modeling of underground utility networks is under development, there are available models that allow for the mapping of the existing information to predefined schema. However, these models have some limitations that restrain the display of the integrated condition with the above-ground city objects as well as they do not support (completely) the interdependencies between networks of different uses.
This thesis focuses on how to develop a three-dimensional model of underground utility networks integrated with above-ground objects, in order to utilize the model in real-world scenarios (disaster management, cost-effective
routes). These networks concern sewage networks (and the stored sub-networks), while the objects are related to the buildings of the study area. For the research, vector datasets were used, related to the existing underground utility networks of a limited area of the Delft University of Technology campus, as well as network elements related to them. For the examination of the proposed model creation, a methodology was developed that could constitute a general approach for relevant applications, considering the condition of the available vector datasets of the similar and/or relevant content.
This methodology is divided into seven stages that concern the preparation of the research and data collection process, the statistical and spatial analysis of their
content, the integration and cleaning approaches to reconstruct invalid information, and the creation of a relational database to store them. Finally, based on the results from these steps model, routing analysis and functionality were implemented to examine the effectiveness of such model in real-world scenarios. According to the results and limitations that arose, suggestions for the harmonization of the data are addressed as well as future application proposals.
Experimentation demonstrates that the developed methodology lead to the creation of a model that, although it represents a simplification of the existing geographical information, it can successfully be used for the implementation of the proposed case studies (disaster management, cost-effective routes). However, taking into account the poor quality of the input data, the model necessitates improvements in order to be used out of the box for other applications, as well as to verify its compliance with available standards supporting the mapping of the including information.

The plastic pollution of aquatic environment is undoubtedly an emerging environmental risk, as it negatively affects ecosystems globally to a great extent. To prevent the plastic soup from growing even further, a Delft-based start-up Noria has developed plastic collectors, to remove plastic from rivers and canals before it reaches the ocean. In order for these devices to give maximum positive effect, they need to be installed in areas where plastic is more likely to accumulate - the plastic hotspots. Taking into consideration various natural attributes that affect the movement of the plastic waste in the water, such as wind direction, water flow, canal geometry, vegetation and man made structures in waterways; potential hotspots can be predicted in a model which would allow more efficient coordination of the cleaning process. Thus, this project aims to locate plastic accumulation zones in the city of Delft in a (semi-) automated manner using open spatial data analysed in GIS and a network simulation model.
The methodology developed in this project results in the visualisation of potential plastic hotspots where Noria’s collectors could be placed in order to remove and recycle the plastic. The potential hotspots suggested by the model were compared with ground truth data collected. The final result yielded only 20% accuracy and therefore did not meet the initial expectation. An evaluation of the shortcomings was made with suggestions for future research.