Remotely-sensed cities

New urban applications are emerging for remote sensing, in particular with the use of high-resolution data for measuring, monitoring and analysis. This comes through the use of high spatial resolution imaging, such as for precision mapping of cities; new techniques for population mapping; extracting urban land use features, and evaluating the city energy patterns; and through the use of night-time imagery for determining populations and economic activity, particularly on a global scale. Remotely Sensed Cities helps to redress the balance with remote sensing books, most of which are dedicated to the physical environment. It is designed for upper-level undergraduate and graduate students, along with research scientists and brings together a good deal of topical work applying remote sensing to the understanding of urban features, their behavior and growth.

Chapter 1 Remotely sensed cities: an introduction -- part PART I: High spatial resolution data -- chapter 2 Comparison of simulated IKONOS and SPOT HRV imagery for classifying urban areas -- chapter 3 Resolution convergence: a comparison of aerial photos, LIDAR and IKONOS for monitoring cities -- chapter 4 Determining urban land use through an analysis of the spatial composition of buildings identified in LIDAR and multispectral image data -- chapter 5 The use of wavelets for feature extraction of cities from satellite sensor images -- part PART II: Cities by day -- chapter 6 Refining methods for dasymetric mapping using satellite remote sensing -- chapter 7 Zone-based estimation of population and housing units from satellite-generated land use/land cover maps -- chapter 8 Population estimation at the pixel level: developing the expectation maximization technique -- chapter 9 Urban land use uncertainty: Bayesian approaches to urban image classification -- chapter 10 Population mapping by geodemographics and digital imagery -- chapter 11 GIS and remote sensing in urban heat islands in the Third World -- part PART III: Cities by night -- chapter 12 LandScan: a global population data…