The production of cocoa beans contributes to 7.5% of European Union (EU) driven deforestation. For this reason, the recent European Union Deforestation-free Regulation (EUDR) requires producers to perform comprehensive tracking of cocoa farm extents. However, cocoa crops present unique detection challenges due to their complex canopy structure, spectral similarity to forest, variable farming methods, and location in frequently cloudy regions. Previous work employs Multispectral Imagery (MSI) and/or Synthetic Aperture Radar (SAR) for pixel-based classification of satellite images. Convolutional Neural Network (CNN)s offer
a promising approach to semantic segmentation of cocoa parcels that considers = both spectral and spatial characteristics. This thesis aims to evaluate the impact of combining SAR and MSI data in the training of a CNN for cocoa detection, in order to demonstrate the importance of texture, moisture and canopy characteristics in identifying cocoa canopies. A U-NET is employed to evaluate
how prediction results are impacted by the stacking of MSI datasets with different SAR polarizations, seasons and temporality. 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 measurement season and polarization, and ground truth classes, on the semantic segmentation of cocoa.

*Public version of internship report*
Urban wildlife plays an invaluable role in cities, including the promotion of overall biodiversity and greenery, pollination and human connection to nature. This internship project involved the development of a prototype that quantifies the potential for local ambassador species to visit a building site based on the amount and connectivity of habitat in the neighbourhood. The workflow integrates vector layers from open datasets and Arup ecologists’ expertise on animal behaviour in order to quantify the cost of moving through an urban environment. Initial results show that the prototype enables the numerical and visual comparison of connectivity for 7 out of the 10 ambassador species, with the Bee demonstrating the highest connectivity, and Toad having the lowest connectivity. This prototype has the potential to support Arup’s work on facilitating more connected urban environments for local wildlife, all the while improving urban ecology overall.

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 interviews with four resilience and digital twin experts, this paper explores how CDTs might support the development of more resilient urban communities. First, the various definitions of 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.

Currently more than 4 billion people live in urban areas around the globe, a trend that is expected to be increased in the upcoming years. While urbanisation provides the space for innovation and new opportunities, in the meantime physical, technical and social challenges are rising and the cities’ vulnerability is increasing. A tool to tackle these issues are Computational Fluid Dynamics
(CFD) simulations, which can provide insight in various topics.

CFD simulations are valuable for modelling complex urban phenomena such as wind flow, microclimates and thermal comfort. A CFD requires as an input a 3D geometric dataset that represents objects in the urban environment which are most commonly buildings and then according to this input the air flow is simulated around it.

When creating geometries automatically for CFD simulations, several clean up tasks must be completed for them to be usable without any issues. One of the problems arising is related to the redundant faces shared between adjacent buildings, which have no purpose for outdoor flow simulations and cause complications when creating the mesh that is needed for the CFD. This
synthesis project focuses on addressing the aforementioned issue by removing the shared faces.

The ultimate goal of this project was to create an open-source product that can efficiently and in an automated way remove the adjacent faces between buildings. The benefits will be imminent during the meshing process, as we strive to reduce the time that consultancies spend fixing the input geometries before running a CFD simulation, along with an overall improved user experience.

This report is organised in four main sections. The first section is the general introduction of the issue that needs to resolved. The second section defines more in depth the problem and sets the research questions, in accordance to that, in the third section the research methodology is developed. In the fourth section the results of both methods are presented. The fifth sectionfocuses on a reflection of the project, while the sixth section presents the final conclusions. Finally, the seventh section contains the specifics of the project management itself.

The project was carried out in cooperation with Dassault Syst`emes and is developed in the context of the GEO1101 course in MSc Geomatics TU Delft. In addition to this report we have created a GitHub repository (https://github.com/Fabisser/facesBgone) that contains the source code of the two methods.