Our research brings together topics from a diverse array of subject areas under the unified theme "intelligent representation and processing of geospatial information" . We are particularly interested in understanding the techniques that human agents employ to structure spatial knowledge and use this understanding to provide better methods for interacting with GI systems. Our current research topics are grouped into four main areas:

Recognition and alignment of sketch maps

SIL's research on recognition and alignment of sketch maps is carried within the DFG funded Sketch Mapia project ( SketchMapia is a project that aims to develop a framework for collection, recognition, interpretation, integration and visualization of sketch maps. In the context of Volunteered Geographic Information (VGI), SketchMapia employs sketch maps for contribution of geographic information. It opens more capabilities of Geographic Information Systems (GISs) to the general public. In spite people who have difficulties to draw a map, all others can use the sketching interface that SketchMapia provides to produce user-generated spatial contents. However, due to human cognition, sketch maps are incomplete, distorted, schematized, and therefore not as accurate as metric maps. The Sketch Mapia project develops a qualitative computational model to represent sketch maps in a computer-understandable way. SketchMapia integrates information from various sketch maps and metric maps into one data repository which can be queried by users via a query-by-sketch interface. The main research topics within the Sketch Mapia cover the semantic recognition of sketched objects, cognitive criteria for sketch map alignment and their qualitative formalization as well as computational aspects for sketch map alignment.

For the semantic recognition of sketch map objects, we develop algorithms to extract the location and the meaning of objects within the images. On this level the research covers low level and mid level image processing techniques as well as high level images understanding methods. Sketch maps are often considered as externalizations of cognitive maps. Thus, sketch maps appear to have similar characteristics as cognitive maps: They are schematized and distort directions, distances, size, and shapes. . This research investigates the reliability and accuracy of sketching aspects for aligning sketch maps automatically with the corresponding real-world configurations represented on metric maps. Reliable qualitative sketching aspects indicating orientation, distance, topology, serial or cyclic order are extracted and formalized using existing qualitative calculi. SketchMapia aims at qualitative representations that are robust against schematizations and distortions, and provide a basis for qualitative alignment of spatial objects from sketch maps with those from metric maps. Qualitative alignment of a pair  of sketch maps involves matching the qualitative constrain networks of the pair such that the greatest possible number of  qualitative constraints is satisfied. Sketch maps are usually drawn at a more abstract level than metric maps, often aggregating regions that in metric maps would be seperate and aggregating spatial relations that in metric maps would be more precisely determined. Error-tolerant matching methods must therefore be used to account for differences in levels of abstraction inorder to improve the quality of alignment.

Cognitive Wayfinding Assistance and Spatial Learning

Research on cognitively enabled wayfinding has led to new path planning algorithms computing easyto-follow routes with descriptive instructions. Nevertheless, current research still adheres to the
principles of traditional navigation systems: routes are given as sequences of instructions that users need to execute step-by-step. Instead of forming a logical sequence of instructions embedded in the
overall task, each instruction is isolated and reduced to a minimum of information content. The user’s wayfinding task is cut down to executing predetermined actions at given locations.

This research direction suggests new wayfinding assistance systems that support the acquisition of spatial knowledge and cognitive map-making for advancing the user’s orientation in unfamiliar environments. Wayfinding instructions are to be embedded in the context of the environment and the overall task. Instructions enriched with information that can be related to the user’s cognitive map helps users to get and remain orientated. This makes wayfinding more successful because it enables users to take informed spatial decisions for circumnavigating traffic, taking shortcuts or including spontaneous
detours. Our research determines which context information advances orientation of users and how this information has to be represented.

Representing Spatial Vagueness

Understanding and representing regions which have no well defined boundaries has always been of interest to geographers. Despite being intuitive to humans who use terms to describe vague regions in everyday language, digitally representing such places is a challenge. This is important in order to perform reasoning tasks in geospatial applications, AI or the semantic web. Our research in this direction investigates how existing methods can be classified from the perspective of user requirements. Methods of interest to us include probabilistic and fuzzy methods, rough sets and egg-yolk models, supervaluation semantics, and triangulated irregular networks for delineation of such regions.

In addition to this we investigate where the semantics of such regions can improve the models by presenting users with a view that is tailored to their profile and intended use. We address the problem of how such a contextual view of the vague regions can be developed and  presented to users.

Usability of Mobile and Tangible Devices for Geospatial Information

The research focus on capturing and representing the semantics of spatial information in order to enable effective and accurate information processing. Intelligent methods are required to provide optimal support for users' needs and overcome semantic interoperability problems. Our research is also directed at semantic annotations of spatial data and the development of models to measure semantic similarity of natural language expressions.

Consistent and flawless communication between humans and machines is the precondition for a computer to process instructions correctly. While machines use well-defined languages and formal rules to process information, humans prefer natural-language expressions with vague semantics. We investigate experimentally the meaning of natural-language spatial relations and develop a computational model to specify the semantics and reason on spatial relations. Natural-language relations and cognitively plausible operations shall improve query languages of geographic information systems and increase the usability for humans.