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Abstract: To extract information about the Earth''s surface from Earth Observation data, a key processing step is the separation of pixels representing clear-sky observations of land or water surfaces from observations substantially influenced by clouds. This paper presents an algorithm used for this purpose specifically for data from the AATSR sensor on ENVISAT. The algorithm is based on the structure of the SPARC cloud detection scheme developed at CCRS for AVHRR data, then modified, calibrated and validated for AATSR data. It uses a series of weighted tests to calculate per-pixel cloud presence probability, and also produces an estimate of cloud top height and a cloud shadow flag. Algorithm parameters have been optimized for daytime use in Canada, and evaluation shows good performance with a mean daytime kappa coefficient of 0.76 for the ‘cloud’/‘clear’ classification when compared to independent validation data. Performance is independent of season, and is a dramatic improvement over the existing AATSR L1B cloud flag for Canada. The algorithm will be used at CCRS for processing AATSR data, and will form the basis of similar processing for data from the SLSTR sensors on Sentinel-3. [Copyright &y& Elsevier]

This paper examines the problems of routing freight traffic, scheduling train services and allocating classification work between yards on a rail network. It describes a general optimization model which takes into account the interactions between these activities and develops global strategies for their management over a medium term planning horizon. A heuristic algorithm developed to solve this nonlinear, mixed-integer multicommodity flow problem is also presented with some computational results based on an example at the Canadian National Railroads. [ABSTRACT FROM AUTHOR]