Automatic Recognition and Location of Civil Infrastructure

Objects Using Mobile Mapping Technology

Background

Information technology is increasingly used to support civil infrastructure systems that are large complex heterogeneous, distributed, dynamic systems including communication systems, roads, bridges, traffic control facilities, 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 of infrastructure objects. The new technology of mobile mapping systems integrates GPS receivers, INS (Inertial Navigation System), and stereo CCD (Couple Charged Device) cameras on a mobile platform, such as a van, for rapid high quality spatial data acquisition. Infrastructure objects appearing in 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.

Objectives

• Research and develop a neural network model for simulating operator’s vision and measurement capability and for automating the procedure of recognition and location of the civil infrastructure objects;
• Study the relationship between the known infrastructure objects, their image features, and neural network characteristics for developing networks capable of recognizing complex infrastructure objects; and
• Automatically generate spatial civil infrastructure information databases from the image sequences.

This research will result in a significant progress in spatial information generation to support information technology based civil infrastructure planning, design, construction, maintenance, 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.

Publication

Tu, Z. and R. Li, 2000. Automatic recognition of civil infrastructure objects in mobile mapping imagery using a markov random field model, Proceedings of ISPRS, Vol. XXXIII, Amsterdam.