Spatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors

Building height (BH) and building footprint (BF) jointly describe the vertical and horizontal extent of the built environment and are required inputs for urban climate, disaster-risk, and population-mapping models. The two parameters are coupled through floor-area-ratio (FAR) constraints, yet remote-sensing approaches typically treat them as independent regression targets. We argue that explicitly encoding this cross-task coupling is more impactful than further refining individual encoders, and propose MorphoFormer, a joint BH/BF estimation framework built around two complementary mechanisms: (i) a BF-Guided Task Decoder (BGTD) that gates the height branch via cross-attention on a footprint-derived morphology context, and (ii) a Morphology Consistency Loss (MCL) that supervises a height-from-footprint surrogate against the ground-truth BH, indirectly forcing the BF feature to encode height-correlated structure. The encoder is a single-stage Swin backbone fed by Sentinel-1 SAR, Sentinel-2 multispectral, and DEM inputs, trained and evaluated on a geo-blocked split of 54 cities. Against a Swin-MTL baseline at identical receptive field, MorphoFormer reduces BH test RMSE from 3.39 to 3.15 m (R^2 improves 0.62 -> 0.67) with BF R^2 stable at 0.80. Controlled ablations at identical capacity attribute most of this 0.24 m improvement to the two proposed mechanisms: removing BGTD raises BH RMSE by 0.11 m and removing MCL raises it by 0.11 m, with the residual approximately 0.02 m falling within the noise floor of encoder-side variations. Because both mechanisms act on cross-task representations rather than pixels, the design carries no intrinsic dependence on input resolution.

PhiSat-2 provides multispectral observations at a 4.75m spatial resolution across geographically diverse scenes, offering a new source of remote sensing imagery for monocular building-height estimation. To comprehensively assess its utility, this study introduces the PhiSat-2--Height Dataset (PHDataset) and the Two-Stream Ordinal Network (TSONet). PHDataset integrates global PhiSat-2 imagery with open building-height references and contains 9475 co-registered patch pairs from 26 cities worldwide. In TSONet, multiscale decoupled height representation (MDHR) separates adaptive bin generation from spatial assignment, while footprint-conditioned height reconstruction (FCHR) incorporates footprint guidance at feature and reconstruction levels. Under city-disjoint evaluation, TSONet reduces building RMSE and MAE by 3.64% and 4.49%, respectively, relative to the most accurate comparison method originally designed for building-height estimation, while maintaining similar height-derived footprint IoU. Ablations support the combined design for height accuracy and footprint consistency. PhiSat-2 reduces building RMSE by 3.73% and 6.05% relative to the evaluated Sentinel-2 and Landsat 8 configurations, respectively, with all seven bands providing the lowest building-height errors among its tested band subsets. LiDAR-based testing supports PHDataset-style supervision in a held-out city, while selected patch comparisons show finer urban height patterns than coarser global products. These findings highlight the value of PhiSat-2 spatial-spectral information for urban building height mapping, with PHDataset and TSONet providing a foundation for further development.

High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the advent of deep learning methods using satellite imagery to predict height maps, these approaches often face a trade-off between data accessibility and spatial resolution. To overcome these limitations, we present SERA-H, an end-to-end model combining a super-resolution module (EDSR) and temporal attention encoding (UTAE). Trained under the supervision of high-density LiDAR-derived Canopy Height Models (CHM), our model generates 2.5 m resolution height maps from freely available Sentinel-1 and Sentinel-2 (10 m) time series data. Evaluated against an open-source benchmark dataset in France, SERA-H, with a MAE of 2.6 m and R2 of 0.82, not only outperforms standard Sentinel-1/2 baselines but also approaches the accuracy of methods based on commercial very high-resolution imagery (e.g., SPOT-6/7), while relying solely on freely available data. These results demonstrate that combining high-resolution ALS supervision with the spatio-temporal information embedded in Sentinel time series enables the reconstruction of spatial detail finer than the native resolution of the input imagery. By approaching the accuracy of costly commercial imagery, SERA-H opens the possibility of mapping temperate forests using publicly available data with high revisit frequency.