Fine-Grained Land Segmentation for Climate Change Impact Assessment: Leveraging the DeepGlobe Dataset with Advanced AI-driven Geospatial Analysis Techniques
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Abstract
As our planet experiences unprecedented heat waves, ice melting, and a steady rise in average temperatures, the unmistakable impact of global climate change is apparent. This environmental crisis has been recognized as the "greatest global health threat of the twenty-first century,"urging the international community to take action. In response to this urgent call, nations worldwide have come together to address the challenges posed by climate change. The Paris Agreement and the Sustainable Development Goals are beacons of hope, signifying the collective commitment to a sustainable future. In this paper, we suggest land segmentation using deep learning to solve modeling and monitoring environmental phenomena as a way in which Artificial Intelligence could benefit the current efforts toward climate change mitigation endeavors. Using the DeepGlobe dataset, we suggest a deep learning-based method for land segmentation in this study to shed light on the impacts of climate change on land cover. We use the DeepGlobe dataset, which comprises high-resolution satellite images classified into several types of land cover. To execute pixel-level land segmentation and automatically extract complicated spatial information from the imagery, our suggested methodology utilizes a deep convolutional neural network architecture. By using the DeepGlobe dataset to train the model, we can take advantage of its broad coverage and variety of land cover classes, improving the model's capacity to generalize across multiple geographical regions. By monitoring and modeling the impacts of global climate change on agriculture, water resources depletion, and coastal erosion. We conclude that by allowing for more specialized and effcient land use management, conservation, and restoration measures, artificial intelligence can help in the fight against climate change.
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