Remote sensing images contain a wealth of spatial information. Efficient scene classification is a necessary precedent step for further application. Despite the great practical value, the mainstream methods using deep convolutional neural networks (CNNs) are generally pretrained on other large datasets (such as ImageNet) and thus fail to capture the specific visual characteristics of remote sensing images. For another, it lacks the generalization ability to new tasks when training a new CNN from scratch with an existing remote sensing dataset. This article addresses the dilemma and uses multiple small-scale datasets to learn a generalized model for efficient scene classification. Since the existing datasets are heterogeneous and cannot be directly combined to train a network, a multitask learning network (MTLN) is developed. The MTLN treats each small-scale dataset as an individual task and uses complementary information contained in multiple tasks to improve generalization. Concretely, the MTLN consists of a shared branch for all tasks and multiple task-specific branches with each for one task. The shared branch extracts shared features for all tasks to achieve information sharing among tasks. The task-specific branch distills the shared features into task-specific features toward the optimal estimation of each specific task. By jointly learning shared features and task-specific features, the MTLN maintains both generalization and discrimination abilities. Two types of MTL scenarios are explored to validate the effectiveness of the proposed method: one is to complete multiple scene classification tasks and the other is to jointly perform scene classification and semantic segmentation. IEEE