We propose the use of texture motifs, or characteristic spatially recurrent patterns for modeling and detecting geospatial objects. A method is proposed for learning a texture motif model from object examples and detecting objects based on the learned model. The model is learned in a two-layered framework—the first learns the constituent "texture elements" of the motif and the second, the spatial distribution of the elements. In the experimental session, we first demonstrate the model training and selection methodology for different objects given a limited dataset of each. We then emphasize the utility of such models for detecting the presence or absence of geospatial objects in large aerial image datasets comprising tens of thousands of image tiles.