Mapping urban areas by fusing multiple sources of coarse resolution remotely sensed data

The main objective of this research is to improve understanding of the methodological and validation requirements for mapping urban land cover over large areas from coarse resolution remotely sensed data. A technique called boosting is used to improve supervised classification accuracy and provides a means to integrate MODIS data with the DMSP nighttime lights data. Results indicate that fusion of these two data types improves urban classification results by resolving confusion between urban and other classes that occurs when either of the data sets is used alone. Traditional measures of accuracy assessment as well as new, maplet-based methods demonstrate the effectiveness of the methodology for creating maps of cities at continental to global scales.

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