Developments in information technology are necessary to support civil infrastructure systems that are large, complex, distributed, dynamic systems including communications, roads, bridges, and distribution of water, gas and electricity. One of the most important data sources for such systems is updated spatial locations, physical conditions, and other attributes. These data describe dynamic status changes of objects caused by, for example, usage and deterioration of materials, repair and relocation, climate conditions, accidents, natural hazards, and other unexpected events. The new technology of mobile mapping systems integrates GPS receivers, INS (inertial navigation system) and stereo CCD (coupled charges device) cameras on a mobile platform for rapid high quality spatial data acquisition. Infrastructure objects appearing in a georeferenced mobile mapping images can be measured on a computer screen and their 3-D ground locations are calculated from measured 2-D image coordinates using a photogrammetric model.
Such a technology is extremely efficient, but it demands a high level of automation and intelligence in data processing for object recognition and location. In this research: 1) a neural network model will be developed to simulate an operator's vision and measurement capability and to automate the procedure of recognition and location of objects; 2) the relationship between known infrastructure objects, their image features, and neural network characteristics will be studied for developing networks capable of recognizing complex objects; and 3) spatial civil infrastructure information databases will be generated automatically from the image sequences. Knowledge algorithms will be developed first for transportation systems, and then will be extended to other civil infrastructure objects.
This research will result in significant progress in spatial information generation to support information technology-based civil infrastructure planning, design, construction, maintenance and repair and management. It will demonstrate an innovative integration of space technology and image processing for civil infrastructure applications. The spatial information databases generated can also be input to a GIS where infrastructure information can be integrated with other information, such as transportation networks, geological models, environmental layers, and others to support complex interdisciplinary decision making processes. ****
