ACBDT: SAR-Optical Cross-Modal Distillation for Sentinel-1/2 Building-Footprint Mapping in Heterogeneous Yangtze River Delta Cities

Medium-resolution building-footprint mapping is limited by two coupled problems: 10 m optical pixels mix roofs with roads and bare surfaces, and SAR observations are degraded by speckle and viewing geometry. We present ACBDT, a Sentinel-1/2 framework that encodes each modality separately, learns a diffusion-inspired time-step-conditioned fused teacher representation, transforms it through a Cross-Modal Distillation Bridge (CMDB), and refines the output with a Student Refinement Decoder. The time-step variable is used only as a stochastic conditioning index; ACBDT does not implement a forward noising schedule, reverse diffusion, or iterative diffusion sampling. Training and evaluation used 2680 paired 256 × 256 patches over eight Yangtze River Delta cities with a spatially disjoint block partition. In three independent runs on the held-out test partition, ACBDT achieved 85.61 ± 0.32% building IoU, 92.24 ± 0.19% F1, and 83.74 ± 0.34% dataset-level boundary F1, compared with 83.21 ± 0.24% IoU for the strongest baseline, FTransUNet. Repeated-seed ablation showed 79.01 ± 0.42% IoU without CMDB and 84.53 ± 0.20% IoU without time-step conditioning. The separate density diagnostic retained a positive full-minus-optical IoU difference across all five building-density strata. Conclusions are limited to this Yangtze River Delta evaluation; city-held-out and cross-season transfer were not tested.