The classification of urban land-use information has become the underlying database for a variety of applications including urban planning and administration. The lack of datasets and changeable semantics of land-use make deep learning methods suffer from low precision, which prevent improvements in the effectiveness of using AI methods for applications. In this paper, we first used GIS data to produce a well-tagged and high-resolution urban land-use image dataset. Then, we proposed a combined convolutional neural network named DUA-Net for complex and diverse urban land-use classification. The DUA-Net combined U-Net and Densely connected Atrous Spatial Pyramid Pooling (DenseASPP) to extract Remote Sensing Imagers (RSIs) features in parallel. Then, channel attention was used to efficiently fuse the multi-source semantic information from the output of the double-layer network to learn the association between different land-use types. Finally, land-use classification of high-resolution urban RSIs was achieved. Experiments were performed on the dataset of this paper, the publicly available Vaihingen dataset and Potsdam dataset with overall accuracy levels reaching 75.90%, 89.71% and 89.91%, respectively. The results indicated that the complex land-use types with heterogeneous features were more difficult to extract than the single-feature land-cover types. The proposed DUA-Net method proved suitable for high-precision urban land-use classification, which will be of great value for urban planning and national land resource surveying.

Mining and predicting college students behaviors from fine-grained spatial-temporal campus activity data play key roles in the academic success and personal development of college students. Most of the existing behavior prediction methods use shallow learning algorithms such as statistics, clustering, and correlation analysis approaches, which fail to mine the long-term spatial-temporal dependencies and semantic correlations from these fine-grained campus data. We propose a novel multi-fragment dynamic semantic spatial-temporal graph convolution network, named the MFDS-STGCN, on the basis of a spatial-temporal graph convolutional network (STGCN) for the automatic prediction of college students' behaviors and abnormal behaviors. We construct a dataset including 7.6 million behavioral records derived from approximately 400 students over 140 days to evaluate the effectiveness of the prediction model. Extensive experimental results demonstrate that the proposed method outperforms multiple baseline prediction methods in terms of student behavior prediction and abnormal behavior prediction, with accuracies of 92.60% and 90.84%, respectively. To further enable behavior prediction, we establish an early warning management mechanism. Based on the predictions and analyses of Big Data, education administrators can detect undesirable abnormal behaviors in time and thus implement effective interventions to better guide students' campus lives, ultimately helping them to more effectively develop and grow.