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In this paper, a shadow detection method combining hyperspectral and LIDAR data analysis is presented. First, a rough shadow image is computed through line-of-sight analysis on a Digital Surface Model (DSM), using an estimate of the position of the sun at the time of image acquisition. Then, large shadow and non-shadow areas in that image are detected and used for training a supervised classifier (a Support Vector Machine, SVM) that classifies every pixel in the hyperspectral image as shadow or non- shadow. Finally, small holes are filled through image morphological analysis. The method was tested on data including a 24 band hyperspectral image in the VIS/NIR domain (50 cm spatial resolution) and a DSM of 25 cm resolution. The results were in good accordance with visual interpretation. As the line-of-sight analysis step is only used for training, geometric mismatches (about 2 m) between LIDAR and hyperspectral data did not affect the results significantly, nor did uncertainties regarding the position of the sun.