High-resolution remote sensing images (HRRSIs) play an important role in large area and real-time earth observation tasks. However, HRRSIs typically comprise heterogeneous objects of various sizes and complex boundary lines, which pose challenges to HRRSI segmentation. Despite the fact that deep convolutional neural networks dramatically boosted the accuracy, several limitations exist in standard models. Existing methods, mainly concatenate multiscale information to extract the various sizes of objects. However, these methods ignore differentiating information, making it difficult to take advantage of them and completely extract small objects. In addition, there have remained some difficulties in extracting boundary information with positions of uncertainty in previous works. In this article, we propose a novel multiscale feature weighted-aggregating and boundary enhancement network (MFBE-Net) for the segmentation of HRRSIs. ResNet-50, possessing a strong ability to extract features, is employed as the backbone. To fully utilize the information that was extracted, we propose a multiscale feature weighted-aggregating module, which aims to weight-integrate deep features, shallow features, and global information. The boundary enhancement module is designed to solve the blurry boundary information problems and locate its positions. Coordinate attention is also applied in the framework to coherently label size-varied ground objects from different categories and reduce information redundancy. Meanwhile, a mixed loss function is used to supervise the network training process. Finally, MFBE-Net was verified on two public HRRSI datasets, and the experimental results show that the proposed framework outperformed other existing mainstream deep learning methods and could further improve the accuracy of HRRSI segmentation.

Prediction of fine particulate matter with particle size less than 2.5 µm (PM_2.5) is an important component of atmospheric pollution warning and control management. In this study, we propose a deep learning model, namely, a spatiotemporal weighted neural network (STWNN), to address the challenge of poor long-term PM_2.5 prediction in areas with sparse and uneven stations. The model, which is based on convolutional neural network–bidirectional long short-term memory (CNN–Bi-LSTM) and attention mechanisms and uses a geospatial data-driven approach, considers the spatiotemporal heterogeneity effec It is correct.ts of PM_2.5. This approach effectively overcomes instability caused by sparse station data in forecasting daily average PM_2.5 concentrations over the next week. The effectiveness of the STWNN model was evaluated using the Xinjiang Uygur Autonomous Region as the study area. Experimental results demonstrate that the STWNN exhibits higher performance (RMSE = 10.29, MAE = 6.4, R² = 0.96, and IA = 0.81) than other models in overall prediction and seasonal clustering. Furthermore, the SHapley Additive exPlanations (SHAP) method was introduced to calculate the contribution and spatiotemporal variation of feature variables after the STWNN prediction model. The SHAP results indicate that the STWNN has significant potential in improving the performance of long-term PM_2.5 prediction at the regional station level. Analyzing spatiotemporal differences in key feature variables that influence PM_2.5 provides a scientific foundation for long-term pollution control and supports emergency response planning for heavy pollution events.

A comprehensive, accurate, and timely expression of earth observation (EO) tasks is the primary prerequisite for the response to and the emergency monitoring of disasters, especially floods. However, the existing information model does not fully satisfy the demand for a fine-grain observation expression of EO task, which results in the absence of task process management. The current study proposed an EO task representation model based on meta-object facility to address this problem. The model not only describes the static information of a task, but it also defines the dynamics of an observation task by introducing a functional metamodel. This metamodel describes the full life cycle of a task; it comprises five process methods: birth, separation, combination, updating, and extinction. An earth observation task modeling and management prototype system (EO-TMMS) for conducting a remote sensing satellite sensor observation task representation experiment on flooding was developed. In accordance with the results, the proposed model can describe various EO tasks demands and the full life cycle process of an EO task. Compared with other typical observation task information models, the proposed model satisfies the dynamic and fine-grain process representation of EO tasks, which can improve the efficiency of EO sensor utilization.