The New Generation of Automatic Landmark Detection Systems: Challenges and Guidelines

Abstract

Landmarks are salient objects in an environment. They play an important role in navigation by serving as orientation aids and marking decision points. Recently, there have been several efforts to design methods to automatically designate certain buildings with salient features as landmarks. All of these methodologies consist of similar steps: (a) establishing a neighborhood, usually around an intersection, (b) performing statistical or data mining analysis to find the building with outlier characteristics, and (c) establishing this salient building as the local landmark. Although these advances are significant, we believe that there are still several key issues that need to be fully addressed in order to realize the new generation of Automatic Landmark Detection Systems (ALDSs). Currently, the main shortcomings in the domain of ALDSs is the lack of a thorough and systematic study of attributes of objects that are analyzed to select landmarks, and deficient experimental verification of the benefits of ALDSs to the end users. Unless, these shortcomings are thoroughly addressed, the viability, applicability, and usefulness of ALDSs are uncertain. On the other hand, automatic landmark detection has the potential to be a dynamic, fascinating, and interdisciplinary research topic with wide applicability. Therefore, the goal of this paper is to discuss the current shortcomings in the domain of landmark detection, propose some preliminary solutions, and provide general guidelines for implementation of the new generation of ALDSs. Specifically, we discuss and promote the importance of: (a) widening the types of attributes analyzed in the landmark detection process, (b) weighting each attribute relative to its significance, (c) extending the types of objects considered as landmark candidates beyond just buildings, (d) identifying landmarks outside the vicinity of intersections, (e) identifying false landmarks along routes, and (f) using virtual environments for experiments with ALDSs. Throughout the paper, we discuss several demonstrative examples and experiments to clarify and support the ideas and concepts that are being promoted.