Change detection with remote sensing data highlights se- mantic differences in an area between two or more time intervals. It involves the comparison of aerial photographs of the same location taken some time apart. This faci itates mass scale analysis of urban and rural data over time, including population trends, city expansion trends and illegal building detection. State-of-the-art methods for the task are predominantly deep learning networks, following an encoder-decoder architecture. These architectures all share the trait of having a ”fusion” point - a location in the network where inputs transition from being processed independently to becoming correlated. F sions can be classified in three categories: early, middle and late, depending on how deep within the network they occur. This study aims to show how changing the fusion impacts the size, spread and number of changes detected. It is motivated by how the receptive field of feature maps in convolutional neural networks expands in deeper layers, extracting features with different complexities. For this, four fusion architectures on three different datasets are compared: LEVIR-CD, HiUCD and a new, fully-controled dataset, CSCD. In terms of test accuracy and the changes’ size and spread, results are inconclusive. Which fusion achieves the highest performance varies per dataset. Possible reasons why include the complexity of remote sensing data and general differences between areas, but this is a subject of further study. The only conclusive category is the number of changes detected. On aver- age, all architectures overestimate the number of changes in a scene. When the accuracy of architectures is com- parable, however, early fusion overestimates the number of objects changed the most, while middle and late fusion give more realistic estimates. The case study has room for refinement in problem isolation, more data and extending the problem towards more architectures, but is a promising step towards understanding fusion.

The detection of changes in an area over time using remotely sensed data such as images is referred to as change detection. It has a large range of applications. For example, changes in buildings can analysed for urban planning. Many conventional image processing and machine learning-based algorithms have been developed for the purpose of change detection. Conventional non-classification algorithms have advantages in their reduced computational cost. Remotely sensed images vary in their spatial resolution, which is the area a pixel covers on the Earth surface. This work aims to explore how the spatial resolution impacts conventional non-classification pixel-based techniques in the urban change detection context, to provide insight into their performance with regards to detecting urban-related change over different resolutions. A systematic experiment is conducted by considering the LEVIR-CD and OSCD test sets in their initial as well as multiple downsampled resolutions. The change detection algorithms Change Vector Analysis (CVA) and Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) are applied to the data individually. For creating binary change labels on a pixel-level, the Otsu algorithm is applied. A set of performance metrics is calculated, and trends in the metrics values over the resolutions are analysed. The data shows some trends towards improved metric values for lower spatial resolutions. The degree of the trends varies and is dependent on the algorithm and dataset. Overall, further research is necessary to consider influencing factors such as the amount of pixels in images, to refine the processing steps, and to broaden the scope of the experiment.