Building registry updates are essential for urban planning but remain a labor-intensive process. This thesis introduces PolyChange, an adaptation of the PolyBuilding model, to automate mutation delineation by integrating aerial imagery with reference maps to produce precise, vectorized building mutations. PolyChange combines raster-based and direct polygonal representation methods, augmented with angle loss, double-thresholding, and label smoothing to enhance delineation accuracy. Experiments on our synthetic dataset demonstrate that raster-based methods achieve superior delineation, with an AR of 89.3\% and AP of 85.2\%. However, real-world datasets reveal challenges in generalization due to overfitting, semantic vagueness, and annotation inconsistencies, resulting in reduced detection and delineation capabilities, indicated by an AR of 30\% and AP of 13.7\% on the test set. Future work should address these limitations by improving dataset quality, refining model architectures, and leveraging reference maps more effectively to achieve fully automated and accurate building registry updates.

In this paper, we propose a method for eliciting constraints for arbitrary Domain-Specific Languages (DSL) in Program Synthesis search. We argue that we can successfully predict constraints using a form of attribute-based induction. We also provide a novel approach to constraint verification using genetic algorithms to optimize desired results. We implement our approach into GENERALIZE, a novel algorithm for reducing DSL size. GENERALIZE is tested and compared against the default Brute algorithm using 2 different program synthesis domains, robot planning and pixel art. These experiments show that GENERALIZE does not improve performance if good objective functions are available, because of a tendency to get stuck in local heuristic minima. It can increase performance if no such function is available.