Transforming the global energy sector from fossil-fuel based to renewable energy sources is key to limiting global warming and efficiently achieving climate neutrality. The decentralized nature of the renewable energy system allows private households to install photovoltaic (PV) systems on their rooftops. In this context, planning an efficient grid expansion is becoming increasingly difficult. Therefore, deep learning (DL) techniques, such as convolutional neural networks (CNNs), can support collecting meta data about PV systems from aerial or satellite images, as research in the field of remote sensing has shown. However, previous research lacks the consideration of ground truth data-specific characteristics of PV panels.

This thesis aims to implement a semantic segmentation model that detects PV systems in aerial imagery to emphasize the relevance of area-specific characteristics for the training data and convolutional neural network (CNN) hyperparameters. A CNN with U-Net architecture is employed to analyze the impacts of land use types, rooftop colors, near-infrared (NIR) data, and lower-resolution images on the detection rate of PV panels in aerial imagery. The results indicate that a U-Net is suitable for classifying PV panels in high-resolution aerial images (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 in terms of urban and architectural properties.

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.