Wide area site models are useful for delineating regions of interest and assisting in tasks like monitoring and change detection. They are also useful in registering a newly acquired image to an existing one of the same site, or to a map. This paper presents an algorithm for building a 2D wide area site model from high resolution, single polarization synthetic aperture radar (SAR) data. A three stage algorithm, involving detection of bright pixels, statistical segmentation of the data into homogeneous regions, and labeling/validation of segmentation results, is used for this task. Constant false alarm rate (CFAR) detectors are used for detecting bright pixels. Under assumptions of a suitable model for the statistical distribution of single polarization intensity or complex data, maximum likelihood labeling is used for initial segmentation. Knowledge of the acquisition parameters and other geometric cues are used to refine the initial segmentation and to extract man-made objects like buildings, and their shadows, as well as roads, from these images. When data from multiple passes of the same site is available, site models yield feature points which can be used to register the different images. In case complete information regarding the radar location, heading, and depression angle are available, the multiple views can be registered prior to site model construction, leading to improved performance. Site models are also useful for SAR data compression, where possible targets, man-made objects, and their neighborhoods are compressed losslessly and the background regions are compressed using lossy schemes.