The objective of this study is to develop a global terrain and altitude map by combining a digital twin model and deep learning technique on Florida's coastal area. Utilizing USGS data, we are able to represent diverse landforms while ensuring the accuracy of elevation changes. In order to mitigate projection distortions, we rescaled 5000 map segments worldwide, ensuring that key geographical features are included. We segment the terrain into seven distinct classes: Water, Grassland, Forest, Hills, Desert, Mountain, and Tundra. The map features are enhanced by median filtering and each class is color-coded. Random parameters were introduced in overlapping image sets in order to ensure variety and prevent redundancy. On these seven terrain classes, the U-Net network is used to perform segmentation tasks. In order to monitor the performance of the model, we implemented cross-validation. The model's effectiveness is demonstrated by robust ROC curve analysis and high AUC values, which indicate accurate terrain categorization. Using deep learning methods and satellite imagery from Google Earth, the primary objective is to develop a digital twin of Florida's coastline. The digital twin serves as both a physical and simulation model, accurately resembling real-world locations. In addition to the achievement of detailed terrain mapping, this approach is likely to have significant applications in environmental monitoring and urban planning as well. In terms of reliability and performance, the digital twin model is expected to be a significant advancement in the field of geographical information systems.

本研究的目的是通过结合佛罗里达州沿海地区的数字孪生模型和深度学习技术来开发全球地形和海拔地图。利用美国地质勘探局的数据，我们能够代表不同的地貌，同时确保高程变化的准确性。为了减轻投影失真，我们重新调整了全球 5000 个地图片段的比例，确保包含关键地理特征。我们将地形分为七个不同的类别：水域、草原、森林、丘陵、沙漠、山脉和苔原。地图特征通过中值滤波得到增强，并且每个类别都进行颜色编码。在重叠图像集中引入随机参数，以确保多样性并防止冗余。在这七个地形类上，使用U-Net网络来执行分割任务。为了监控模型的性能，我们实施了交叉验证。稳健的 ROC 曲线分析和高 AUC 值证明了该模型的有效性，这表明地形分类准确。使用深度学习方法和谷歌地球的卫星图像，主要目标是开发佛罗里达州海岸线的数字孪生。数字孪生既充当物理模型又充当仿真模型，准确地模拟现实世界的位置。除了实现详细的地形测绘之外，这种方法还可能在环境监测和城市规划方面具有重要的应用。在可靠性和性能方面，数字孪生模型有望成为地理信息系统领域的重大进步。