Name
Abstract | ||
|---|---|---|
The task to update map databases regularly using images calls for automation. Within this process GIS and image data have to be combined. The different nature and content of these information sources prevent a direct comparison. In this paper an approach for the combination of GIS data and aerial images is proposed. We make use of a knowledge base, modelling the objects which are expected to be found. This leads to a refined image interpretation process and enables a revision of the GIS data. The focus is thereby on settlement and industrial areas which are detected automatically from images. The use of a semantic network allows the formulation of such complex objects expected in the image and supports decisions on a high level of abstraction. Reasoning is supported by using the existing GIS information for hypothesis generation. The decisions combine different clues which evolve during the image analysis process. The analysis generates a hierarchical scene description for the area of study and evaluates the correctness of the GIS data. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.inffus.2004.08.001 | Information Fusion |
Keywords | Field | DocType |
Quality control,GIS,Semantic net,Knowledge based image interpretation,Decision level fusion | Data mining,Abstraction,Computer science,Correctness,Interpretation Process,Sensor fusion,Semantic network,Automation,Artificial intelligence,Enterprise GIS,Knowledge base,Machine learning | Journal |
Volume | Issue | ISSN |
6 | 4 | 1566-2535 |
Citations | PageRank | References |
3 | 0.40 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Weis | 1 | 3 | 0.40 |
| Sönke Müller | 2 | 6 | 1.86 |
| C.-E. Liedtke | 3 | 22 | 5.40 |
| M. Pahl | 4 | 3 | 0.40 |