From Pixel Receptive Fields to Ground Spans: GSD-Conditioned Cross-Resolution Feature Alignment for Photovoltaic Detection and Segmentation

Abstract

Photovoltaic (PV) inventories with explicit location and extent are needed for energy accounting, distribution-grid planning, and asset monitoring. However, the ground sampling distance (GSD) of different sources spans nearly an order of magnitude, so a sampling window of fixed pixel size covers 64 times more ground at 0.8 m than at 0.1 m. This physical-scale discrepancy can weaken cross-resolution generalization. To address this, this paper proposes CRFA-PVNet, a physically guided cross-resolution feature alignment network for PV detection and segmentation, which describes the receptive field in ground units rather than in pixels. First, a physical receptive-field formulation quantifies the physical-support gap between GSD conditions, while a normalized discrepancy between spatially aligned fusion features defines the cross-resolution representation error. Second, CRFA formulates fusion-stage feature reassembly as a GSD-conditioned operator: effective GSD modulates the spatial evidence used to generate content-aware local aggregation kernels, making the reassembly weights joint functions of image content and physical sampling scale. Third, a proposal-guided local mask refinement pipeline restricts boundary parsing to oriented proposals and lowers inference cost on embedded hardware. Fourth, a spatially disjoint validation protocol, with the buffer distance set from Moran’s I and semivariogram range, limits spatial information leakage. One jointly trained parameter set serves 0.1, 0.3, and 0.8 m imagery. The largest gains occur on the 0.8 m satellite imagery. OBB mAP50 reaches 0.849, and mask IoU reaches 0.764. These results are consistent with the physical ground-span analysis. On an NVIDIA Jetson TX2, TensorRT FP16 reaches 41.3 FPS with 3.74 GB peak memory and a 0.002 macro accuracy loss.