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This letter addresses the automatic detection of urban area in remotely sensed images. As manual administration is time consuming and unfeasible, researchers have to focus on automated processing techniques, which can handle various image characteristics and huge amount of data. The applied method extracts feature points in the first step, which is followed by the construction of a voting map to represent urban areas. Finally, an adaptive decision making is performed to find urban areas. This letter presents methodological contributions in two key issues to the algorithm: 1) An automatically extracted Harris-based feature point set is introduced for the first step, which is able to represent urban areas more precisely. 2) An improved orientation-sensitive voting technique is proposed, exploiting the orientation information calculated in the local neighborhood of points. Evaluation results show that the proposed contributions increase the detection accuracy of urban areas.