Outdoor radio coverage map estimation is an important tool for network planning and resource management in modern Internet of Things (IoT) and cellular systems. A radio map spatially describes radio signal strength distribution and provides network coverage information. A practical problem is to estimate fine-resolution radio maps from sparse radio strength measurements. However, nonuniformly positioned measurements and access constraints pose challenges to accurate radio map estimation (RME) and spectrum planning in many outdoor environments. In this work, we develop a two-phase learning framework for RME by integrating well-known radio propagation model and designing a conditional generative adversarial network (cGAN). We first explore global information to extract radio propagation patterns. Next, we focus on the local features to estimate the shadowing effect on radio maps in order to train and optimize the cGAN. Our experimental results demonstrate the efficacy of the proposed framework for RME based on generative models from sparse observations in outdoor scenarios.