The presented research develops and validates practical frameworks for transmitter localization and radio map reconstruction that operate under very sparse sensing in realistic semi-urban environments. There are three critical gaps in spectrum cartography that, if solved, would enable the adoption of digital spectrum twins, truly automated dynamic spectrum access, and fully AI-optimized radio networks. First, a lack of evidence on when higher fidelity propagation models materially improve results over simple path loss models in sparse regimes; second, the prevailing assumption of dense, geographically distributed sensor data, which is often impractical or unavailable in operational deployments; and third, the absence of a quantitative characterization of how realistic uncertainty in the digital description of the environment propagates through ray tracing to affect the channel statistics on which geometry informed propagation models depend. These three gaps in spectrum cartography are spanned by establishing two estimation pipelines for transmitter localization and radio map reconstruction that operate under very sparse sensing in realistic semi-urban environments: a likelihood based method that marginalizes over all candidate transmitter locations to produce a received power field directly from the sensor observations, and a generalized likelihood ratio test (GLRT)-based sparse recovery pipeline that explicitly detects and localizes individual sources through sequential hypothesis testing, beam search, physics based filtering, and combinatorial model selection before proceeding with radio map reconstruction.