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Accurate land use and land cover (LULC) mapping based on remote sensing image classification has been a hot topic nowadays. Recently, deep learning, especially convolutional neural network, has achieved promising results in computer vision tasks, which has also been introduced into the field of LULC mapping. Compared with classic machine learning methods, deep learning is capable of extracting the most representative features from remote sensing images, however, its performance is depended on massive labeled data. Considering deep learning has been widely used in LULC classification, the objective was to provide a comprehensive review of deep learning from the following perspectives as sample dataset, model structure and training strategy. Specifically, from the perspective of samples, the most commonly used LULC sample dataset was summarized and their academic influence was analyzed. From the perspective of models, the latest research of deep learning models were reviewed, including convolutional neural network, recurrent neural network, fully convolutional network. From the perspective of training strategies, various training methods that could tackle the data-hunger issue of deep learning were summarized, including active learning, semi-supervised learning, weakly-supervised learning, self-supervised learning, transfer learning. Finally, an outlook of deep learning in LULC mapping was provided, which was still from three perspectives of sample dataset, model structure and training strategy. Through the construction of large-scale LULC sample dataset, improvement of deep learning model structure and the increase of spatial-temporal generalization capability under limited samples, LULC remote sensing classification could yield a better performance and accuracy in future study.