Doctoral thesis defense: Pedro Maroun Eid
Speaker: Pedro Maroun Eid
Supervisor: Dr. S. P. Mudur
Examining Committee: Drs. T. Fevens, R. Ganesan, V. Haarslev, V. Bhavsar, M. Mehmet Ali (Chair)
Title: A Knowledge-based Approach for Creating Detailed Landscape Representations by Fusing GIS Data Collections with Associated Uncertainty Theorem
Date: Wednesday, June 11, 2014
Place: EV 3.309
Geographic Information Systems (GIS) data for a region is of different types and collected from different sources, such as aerial digitized color imagery, elevation data consisting of terrain height at different points in that region, and feature data consisting of geometric information and properties about entities above/below the ground in that region. Merging GIS data and understanding the real world information present explicitly or implicitly in that data is a challenging task. This is often done manually by domain experts because of their superior capability to efficiently recognize patterns, combine, reason, and relate information. For example, a human would create representations of entities by collectively looking at the data layers, noting even elements that are not visible, like a covered overpass or underwater tunnel of a certain width and length. Such detailed representations are needed for use by processes like visualization or 3D modeling in applications used by simulation, earth sciences and gaming communities.
Our main thesis, and a significant research contribution of this work, is that this task of creating detailed representations can be automated to a very large extent using a methodology which first fuses all Geographic Information System (GIS) data sources available into knowledge base (KB) assertions (instances) representing real world objects using a subprocess called GIS2KB. Then using reasoning, implicit information is inferred to define detailed 3D entity representations using a geometry definition engine called KB2Scene. Semantic Web is used as the knowledge inferencing system and is extended with a data extraction framework.This framework enables the extraction of implicit property information using data and image analysis techniques. Data extractors record uncertainty per property. These uncertainty values are used under Zadeh fuzzy semantics to compute a resulting uncertainty for inferred assertional axioms. This is achieved by another major contribution of our research, a unique extension of the KB ABox Realization service using KB explanation services. The uncertainties are then available for user-decision at output. We show that the process of creating 3D visuals from GIS data sources can be more automated, modular, verifiable, and the knowledge base instances available for other applications to use as part of a common knowledge base. We define our method's components, discuss advantages and limitations, and show sample results for the transportation domain.