Information Extraction of Urban Environments with Remotely Sensed Data

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Remote sensing of urban areas is probably the most challenging compared with other natural landscapes, because of the complexity of land use and land cover patterns, the 3D man-made and natural features and the need for very detailed information. Currently more people live in urban areas than rural areas in the world. Acquiring detailed information in urban areas is important for sustainable urban development and adaptation to global environmental change, for example sea level changes and emergency response to disasters. With the increasing availability of aerial and spaceborne remotely sensed data that record information from different wavelength ranges, with much higher spatial resolution, it is now possible to effectively extract useful land surface information in urban areas. Although remote sensing has been successful in many urban applications, there are still some challenging issues on automatic extraction of difficult to obtain information using remote sensing. New and improved methods and algorithms are required to process and integrate the multi-sensor data to extract the land surface information accurately and automatically. In this proposal, I will focus on filling the gaps in the automatic extraction of land surface information using remote sensing. Specifically, in this proposal, I will develop methodology for three dimensional (3D) urban building modelling based on multi-view image matching; urban road surface condition information extraction using linear feature detection and analysis from aerial images and surface roughness estimation using the airborne lidar; and urban tree species classification by adding structural and shape information to the colour information in order to improve the accuracy. During this grant funding period, five PhDs, six Master's and additional Honours undergraduate students will be trained. The proposed research will contribute new algorithms and ideas to the knowledge of remote sensing, and contribute towards the operational use of remote sensing products in urban environmental applications. Urban planners, municipal government managers and decision makers, urban transportation authorities, urban disaster management teams, urban foresters, hydrologists and ecologists will benefit from the research results of 3D building models, road condition evaluation and urban tree species classification. Researchers in urban climate, hydrology, energy consumption and environmental changes can use the resultant information as inputs to their models. The research will benefit Canada in terms of sustainable urban development, improving urban transportation management, improving the quality of life of Canadians, adapting to global climate change and natural disaster management.