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Abstract. Land-cover classification of Remote Sensing (RS) data in urban area has always been a challenging task due to the complicated relations between different objects. Recently, fusion of aerial imagery and light detection and ranging (LiDAR) data has obtained a great attention in RS communities. Meanwhile, convolutional neural network (CNN) has proven its power in extracting high-level (deep) descriptors to improve RS data classification. In this paper, a CNN-based feature-level frameworkdoi:10.5194/isprs-archives-xlii-4-w18-279-2019 fatcat:w3hzfgmrdnc63m7kvs2zcpm5fq