To address the need for wide-area electromagnetic spectrum sensing of space targets from sparse Low-Earth Orbit constellation observations, this paper proposes SRFlow, a flow-matching generative model. We first construct a high-fidelity dataset covering diverse scenarios via STK-MATLAB co-simulation. By integrating multi-source priors and an iterative measurement injection strategy, SRFlow achieves high-quality reconstruction of full spectrum maps from sparse measurements. Experiments demonstrate that SRFlow significantly outperforms state-of-the-art baselines, including the physics-informed diffusion model RMDM, in both reconstruction accuracy (NMSE/SSIM) and computational efficiency (parameters/inference time), under both known and unknown target-position conditions. Moreover, it trains nearly an order of magnitude faster than diffusion models. This work contributes the first dedicated dataset for space-based spectrum sensing, introduces the accurate and efficient SRFlow model, and establishes a rigorous benchmark for future research.

Radio Environment Map (REM) construction is frequently constrained by sparse and unevenly distributed spectrum measurements. While existing completion methods primarily target Power Spectral Density (PSD) data under random missing patterns, the reconstruction of Reference Signal Received Power (RSRP) maps under structured data loss remains underexplored. This study addresses this gap by proposing a fully convolutional densely connected autoencoder(AE) for RSRP map completion. The encoder stacks dense blocks and transition layers, a bottleneck preserves the latent representation, and the decoder restores spatial resolution through transposed convolution. Both global and local skip connections are incorporated to fuse large-scale structure with fine-grained details. A composite loss function supervises observed and missing regions separately, which preserves the fidelity of known measurements while improving inference over unobserved grid points. Experiments on the public DeepREM dataset under random, spatial, and strip-wise missing patterns show that the method achieves the best or comparable completion accuracy in most tested settings, with the most pronounced performance gains over mainstream baselines under the challenging spatial block-missing case.