Self-supervised learning achieves close to supervised learning results on remote sensing image (RSI) scene classification. This is due to the current popular self-supervised learning methods that learn representations by applying different augmentations to images and completing the instance discrimination task which enables convolutional neural networks (CNNs) to learn representations invariant to augmentation. However, RSIs are spatial-temporal heterogeneous, which means that similar features may exhibit different characteristics in different spatial-temporal scenes. Therefore, the performance of CNNs that learn only representations invariant to augmentation still degrades for unseen spatial-temporal scenes due to the lack of spatial-temporal invariant representations. We propose a spatial-temporal invariant contrastive learning (STICL) framework to learn spatial-temporal invariant representations from unlabeled images containing a large number of spatial-temporal scenes. We use optimal transport to transfer an arbitrary unlabeled RSI into multiple other spatial-temporal scenes and then use STICL to make CNNs produce similar representations for the views of the same RSI in different spatial-temporal scenes. We analyze the performance of our proposed STICL on four commonly used RSI scene classification datasets, and the results show that our method achieves better performance on RSIs in unseen spatial-temporal scenes compared to popular self-supervised learning methods. Based on our findings, it can be inferred that spatial-temporal invariance is an indispensable property for a remote sensing model that can be applied to a wider range of remote sensing tasks, which also inspires the study of more general remote sensing models. The source code is available athttps://github.com/GeoX-Lab/G-RSIM/tree/main/T-SC-STICL.

In the age of the Internet of everything, the Edge-Cloud collaborative service support has become a very promising development direction in the application field of the Internet of things. However, when various service components are deployed both on the cloud and the edge, the subsidence and decentralization of computing resources also have a great impact on the performance of composite services. This paper proposes an optimal composite service selection model based on Petri nets. In order to compare the composite service paths, this paper adopts a dynamic evaluation model of composite service quality based on Petri nets. Firstly, all kinds of Petri net models for the implementation structure of composite services are constructed. And then the QoS computing rules corresponding to these structures are given based on the QoS of each service component on the edge of the cloud. Finally, the dynamic execution process of each composite service with a feasible path is simulated through Petri net, and the overall performance of the service is evaluated based on the dynamic simulation of its Petri net model, finally, the optimal service path is selected among the combined services that meet the user's requirements.. Through case analysis and comparison, the feasibility and effectiveness of the method are verified by an example analysis.