![]() "And all the CO2 we're putting into the air is causing that temperature – a lot of it goes into the ocean." ![]() "The waters around Florida are over 90 degrees Fahrenheit, which is extremely complicated for marine species like coral reefs, marine plants and marine animals," Carlos Del Castillo, chief of the Ocean Ecology Laboratory at NASA's Goddard Space Flight Center, said. ![]() NASA isn't only focused on managing the crisis in order to protect humanity, but also to aid species on land and in sea. "There has been decade on decade of increasing temperatures – throughout the last four decades." In fact, Gavin suggested 2023 may prove to be the hottest year on record and 2024 will more than likely take that grim title. "The heat waves that we're seeing in the U.S., in Europe and in China are demolishing records left, right and center," Schmidt said. This sentiment grew clear as a variety of experts in marine science, aeronautical engineering and environmental studies spoke during the conference about the immediacy with which climate change must be handled. "You think of NASA as a space agency you think of NASA as an aeronautical agency," NASA administrator Bill Nelson said, "NASA is also a climate agency." That end to end capability allows us the opportunity to deliver actionable science and information so more people can see the Earth as we see it." NASA Earth Science is end-to-end capability from technology all the way through what the observations mean, today and into the future. "This has never been more important or compelling than it is today. Germain, director of NASA's Earth Science Division, said. "Our science isn't done until we've communicated it," Karen St. There was even some preliminary talk about how artificial intelligence and deep learning could aid the agency with getting climate data that's as precise and accurate as possible, but the team emphasized how such mechanisms are still very much in-the-works. The experimental results show that the proposed MDNet has significantly improved the performance of change detection on the three datasets compared with six advanced deep learning models, which will contribute to the development of change detection with VHR remote sensing images.Another recurring theme of the discussion was the importance of generating pristine climate data that's available to the public, researchers and policymakers with the power to make a difference. Finally, the Season-varying Change Detection Dataset is used to verify that the MDNet proposed can detect changes in other scenarios very well. Then, a self-made OMCD dataset was used to achieve an F1-score of 92.8% for the localized and fine-scale change detection in open-pit mines, which is an improvement of 0.7% to 5.4% compared to the benchmark methods. In the experiments, the publicly available open-pit mine change detection (OMCD) dataset was used first to achieve a change detection of open-pit mines over a large area, with an F1-score of 89.2%, increasing by 1.3% to 5.9% compared to the benchmark methods. Therefore, the Multi-level Change Features Fusion Module (MCFFM) is proposed in this study for the effective fusion of multi-level change features. Stacking them directly on channels does not make effective use of change information, thus limiting the performance of MDNet. However, not all high-level features extracted by MDNet contribute to the recognition of image differences, and the multi-level change features suffer from cross-channel heterogeneity. The early-difference network can focus on change information throughout to reduce the spurious changes in the change detection results, and the late-difference network can provide deep features of a single image for reducing rough boundaries and scattered holes in the change detection results, thus improving the accuracy. An early-difference network and a late-difference network are combined by MDNet to extract multi-level change features. To address the problem of limited change detection accuracy in existing single-level difference networks, this study proposes the Multi-level Difference Network (MDNet) for automatic change detection of ground targets from very high-resolution (VHR) remote sensing images. Automatic change detection based on remote sensing is playing an increasingly important role in the national economy construction.
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