Rapid, reliable mapping of earthquake-induced building damage is essential for directing emergency response. This thesis tackles building-level damage detection from pre/post-event very-high-resolution satellite imagery, using the ESA Φ-Lab AI for Earthquake Response Challenge as context. The challenge targets automatic identification of damaged vs. undamaged buildings from image pairs and building polygons, leveraging historical Charter activations and multi-mission VHR data (e.g., Pléiades-1, WorldView-2/3, GeoEye-1, Kompsat-3/3A, Gaofen-2) to simulate real-world conditions. We present an end-to-end pipeline designed for three persistent difficulties: severe class imbalance, image misalignment, and geographic domain shift. Supervision is restricted to building footprints to limit label noise, and a pixel-level weighting term (computed from the ratio of valid negatives to positives) is integrated into the loss to avoid discarding scarce positives.