The increasing availability and temporal precision of satellite imagery — driven by the rapid expansion of Earth observation constellations — has significantly broadened the scope of change detection models in remote sensing. This progress has fueled applications ranging from disaster response to urban planning. However, detecting subtle and localized changes, such as demolitions of heritage sites, poses unique challenges. High-resolution imagery (0.5m) provides the necessary spatial fidelity, but remains cost-prohibitive for continuous monitoring; low-resolution (10m) imagery lacks critical detail. This thesis investigates whether medium-resolution (3m) satellite imagery can serve as a viable trade-off, enabling scalable, automated change detection through computer vision models. I adapt self-supervised change detection techniques originally developed for low-resolution imagery (e.g., Sentinel-2) to the medium-resolution setting and evaluate their ability to capture semantically meaningful, spatially subtle changes. This is paired with a supervised pipeline using a labeled dataset of medium-resolution satellite images. Through both qualitative and quantitative analyses, I assess model performance in detecting demolition-scale changes under real-world environmental variability. While medium-resolution inputs support the detection of coarse structural shifts such as new roads or land clearance, they consistently fail to capture finer details associated with building demolition — particularly in the presence of seasonal variation. These findings reveal critical resolution-bound limits in current vision pipelines and motivate future research into hybrid-resolution architectures, resolution-aware learning, and intelligent triage strategies for scalable, resource-efficient change monitoring.