Advances in technology for digital image capture and storage have caused an information overload problem in the geo-sciences. This has compounded existing image retrieval problems whereby most image matching is performed using content-based image retrieval techniques. The biggest problem in this field is the so-called semantic gap - the mismatch between the capabilities of current CBIR systems and the user needs. One way of addressing this problem is to develop context-based image retrieval methods. Context-based retrieval relies on knowledge about why image contents are important in a particular area and how specific images have been used to address particular tasks. We are developing a case-based knowledge-management retrieval system that employs a task-centric approach to capturing and reusing user context. This is achieved through image annotation and adaptive content presentation. In this paper we present an extension of a previous implementation of our approach and a thorough evaluation of our application.