Image-Based Change Detection of Geographic Information Using Spatial Constraints

Abstract

A major challenge in the production and use of geographic information is assessment and control of the quality of the spatial data. The rapid growing number of sources of geospatial data, ranging from high-resolution satellite and airborne sensors, GPS, and derivative geospatial products, pose severe problems for integrating data. Content providers face the problem of continuously ensuring that the information they produce is reliable, accurate and up-to-date. Integrity constraints are able to resolve certain issues in the data, like valid attribute values or relationships between data objects. The main issue is however the consistency of the data with respect to the current “real-world” situation. Today the industry still relies on human operators, who collect and interpret aerial photos and field data to check and correct the current state of the data. Especially for detailed data over large regions, like digital road maps or topographic maps, this is a very labor-intensive and costly process. In addition, human processing is a source of error and inconsistency. Automated detection of change and anomalies in the existing databases using image information can form an essential tool to support quality control and maintenance of spatial information.