Modern technology enables organizations to build huge geospatial data repositories. But collecting and storing information is not sufficient if it is not backed-up by accurate and flexible methods of extracting knowledge encapsulated in data. Image analysts use individualized models to represent visual patterns found in images. These models may not coincide with the models created by computer algorithms. To be successful, computer systems need to adapt to the subjective views of image analysts. In this article we introduce a novel method for fast query customization that provides users with individualized computer models to assign semantics to visual patterns in images. These models are evolved according to user input by adjusting possibility functions that mathematically map the assignment of semantics into low-level features. Our approach provides a flexible method for querying image databases using semantics, and potentially provides a knowledge exchange method for the geospatial community.