Urbanisation and land-use change increase the demand for temporally consistent urban maps from high-resolution Earth observation imagery. A key obstacle is <em>label ageing</em>: benchmark annotations are often years older than current true orthophotos (TOP), causing semantic and geometric mismatches (e.g., demolished/new buildings, shifted vegetation boundaries) that degrade supervised learning, calibration, and transfer. This paper presents a probabilistic, quality-aware segmentation framework based on a compact U-Net. Legacy annotations are converted into edge-adaptive soft labels to encode boundary uncertainty. A HELIX-derived per-pixel supervision quality score Q is computed and integrated as a weight in a <em>Q</em>-weighted Kullback–Leibler objective with an agreement-focal component, reducing the influence of unreliable or outdated regions. Global temperature scaling is then applied to obtain calibrated per-class probability fields with comparable confidence magnitudes. Experiments on ISPRS Potsdam and Vaihingen combined with recent (2024) TOPs evaluate temporal transfer (archival supervision vs. updated imagery of the same area) and spatial transfer (cross-city application). Finally, calibrated probability fields are used to derive probabilistic semantic transitions and temporal reliability scores, supporting uncertainty-aware mapping of urban change such as construction, sealing, and vegetation loss.