The combination of remote sensing image information extraction and artificial intelligence algorithms is an important technical tool for land use status survey, monitoring and management in land resources and environmental departments.Aiming at the problems of insufficient spatial information localization and inaccurate multi-scale target feature segmentation generated by U-net in remote sensing image extraction, a CA-Res2-Unet model incorporating an attention module into the head of Res2Net to replace the coding part of U-net is proposed, which aims to enhance the spatial localization and multi-scale feature information segmentation capability of U-net.Experiments were carried out on mainstream networks and improved models through the WHDLD public data set and the self-made data set of Shenfu New District. The results show that compared with the basic model, OA, M_IoU and F1 indexes of the experiment on the WHDLD public data set and the self-made data set of Shenfu New District increased by 0.92%, 2.00%, 1.58% and 1.18%, 2.87%, 1.91%, respectively. The visual effect and quantitative indexes of the proposed method are superior to other mainstream semantic segmentation networks, which can provide scientific basis for the investigation of the status quo of regional land use and the decision-making of relevant departments.

The COVID?19 epidemic is facing the influence of a variety of complex practical factors, which makes the development of the epidemic uncertain. In order to overcome the problem of large error in epidemic forecasting results due to the limitations of many ideal assumptions based on the infectious disease compartment model, a time series forecasting model based on deep learning is adopted to predict the epidemic development, and an informer model based on transformer model is established. Attention mechanism and distillation mechanism are applied to the time series forecasting of epidemic data. The threshold autoregressive (TAR) model and a variety of mainstream recurrent neural time series prediction models are used as comparison models. Through simulation experiments, the current number of remaining infections in the epidemic data of China, America and Britain is predicted in the short term, and RMSE and MAE are used as evaluation indicators, and then the best model is selected for medium ? and long?term prediction. The experimental results show that the indicator value of the informer model is optimal in both RMSE and MAE, further indicating that the prediction accuracy of the informer model is higher than that of other comparative models in China, America and Britain. Finally, the Informer model is used for the development of the epidemic in China，America and Britain medium and long?term prediction.