GaussLift: Renderer-Consistent DSM Export from Satellite Gaussian Fields

3D Gaussian Splatting (3DGS) can reproduce satellite imagery with high photometric fidelity, yet a learned Gaussian field does not uniquely determine a metrically accurate digital surface model (DSM). The ambiguity arises at the representation–measurement interface: primitive centers, projected footprints, and renderer-visible support can induce different elevation surfaces from the same field. We introduce GaussLift, which makes this interface explicit by reusing front-to-back alpha-compositing weights to define a normalized renderer-conditioned altitude observable, then localizing supported samples in world coordinates and realizing them on the DSM grid. The same observable is used for surface supervision and export, without introducing an auxiliary DSM decoder. Fixed-field controls on DFC2019 confirm that changing only the export operator alters both reconstruction error and coverage; under a common-mask comparison across frozen fields, renderer-conditioned altitude attains the lowest MAE and RMSE among the tested readouts. Across four scene-specific reconstructions, the final 12-member map-space ensemble achieves 0.787 m raw MAE, 2.344 m RMSE, and 95.91% coverage. The underlying Gaussian fields remain renderable from 68 held-out views without target-view fitting, reaching 22.562 dB mean PSNR. These results show that surface readout is a consequential component of metric 3D Gaussian reconstruction, complementary to representation learning.