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Rapid image acquisition for a region affected by an earthquake is important to manage the rescue operation. The use of an unmanned aerial vehicle (UAV) to rapidly cruise an affected region and obtain very high resolution (VHR) images is highly advantageous. However, haze is a problem for many UAV aerial images, especially when UAVs cross clouds. In this paper, we present a parallel predicting workflow that cooperates with Swin Transformer UNet (ST-UNet) for this task. ST-UNet utilizes the Swin Transformer instead of a convolutional layer (CNN), which greatly enhances the processing speed without accuracy loss. The predicting workflow employs parallel processing and a reasonable data structure to maximize the computing resources for rapid processing. To demonstrate the advantageousness of the proposed workflow, we employed three public remote sensing datasets for evaluation, and the proposed ST-UNet obtained the highest accuracy and speed. Furthermore, the high dehazing performance of ST-UNet was demonstrated using a real post-earthquake scene.

In the past decade, unmanned aircraft systems (UASs) have been widely used in various civilian applications, most of which involve only a single unmanned aerial vehicle (UAV). In the near future, more and more UAS applications will be facilitated by the cooperation of multiple UAVs. In such applications, it is desirable to utilize a general control platform for cooperative UAVs. However, existing open-source control platforms cannot fulfill such a demand because (1) they only support the leader-follower mode, which limits the design options for fleet control, (2) existing platforms can support only certain type of UAVs and thus lack compatibility, and (3) these platforms cannot accurately simulate a flight mission, which may cause a big gap between simulation and real-world flight. To address these issues, we propose a general control and monitoring platform for cooperative UAS, namely, CoUAS , which provides a set of core cooperation services of UAVs, including synchronization, connectivity management, path planning, energy simulation, and so on. To verify the applicability of CoUAS, we design and develop a prototype in which an embedded path planning service is provided to complete any task with the minimum flying time while considering the network connectivity and coverage. Experimental results by both simulation and field test demonstrate that the proposed system is viable.