Introduction Evaluating the condition of transportation infrastructure is an expensive, labor intensive, and time consuming process. Many traditional road evaluation methods utilize measurements taken in situ along with visual examinations and interpretations. The measurement of damage and deterioration is often qualitative and limited to point observations. Remote sensing techniques offer nondestructive methods for road condition assessment with large spatial coverage. These tools provide an opportunity for frequent, comprehensive, and quantitative surveys of transportation infrastructure. Methods The goal of this paper is to provide a bridge between traditional procedures for road evaluation and remote sensing methodologies by creating a comprehensive reference for geotechnical engineers and remote sensing experts alike. Results A comprehensive literature review and survey of current techniques and research methods is provided to facilitate this bridge. A special emphasis is given to the E. Schnebele ( ) · G. Cervone · N. Waters

This paper implements a land use classification for the City of Calgary, Alberta, Canada, using an object-oriented approach for six Landsat TM and ETM+ images and simulates the land use pattern in the future using Markov Chain analysis and Cellular Automata analysis based on the interactions between these land uses and the transportation network. Shannon's Entropy (an urban sprawl index) based on the land use classification results is used to measure urban sprawl. This research proves that an object-oriented approach can produce satisfactory classification results. It reveals the manner in which land use is likely to develop in the future, and demonstrates that urban sprawl continued to grow in Calgary during the years between 1985 and 2001. Such models are useful for providing the building blocks for traditional four-step transportation planning models.

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