Satellite imagery has dramatically revolutionized the field of geography by giving academics, scientists, and policymakers unprecedented global access to spatial data. Manual methods typically require significant time and effort to detect the generic land structure in satellite images. This study can produce a set of applications such as urban planning and development, environmental monitoring, disaster management, etc. Therefore, the research presents a methodology to minimize human labor, reducing the expenses and duration needed to identify the land structure. This article developed a deep learning-based approach to automate the process of classifying geographical land structures. We used a satellite image dataset acquired from MLRSNet. The study compared the performance of three architectures, namely CNN, ResNet-50, and Inception-v3. We used three optimizers with any model: Adam, SGD, and RMSProp. We conduct the training process for a fixed number of epochs, specifically 100 epochs, with a batch size of 64. The ResNet-50 achieved an accuracy of 76.5% with the ADAM optimizer, the Inception-v3 with RMSProp achieved an accuracy of 93.8%, and the proposed approach, CNN with RMSProp optimizer, achieved the highest level of performance and an accuracy of 94.8%. Moreover, a thorough examination of the CNN model demonstrated its exceptional accuracy, recall, and F1 scores for all categories, confirming its resilience and dependability in precisely detecting various terrain formations. The results highlight the potential of deep learning models in scene understanding, as well as their significance in efficiently identifying and categorizing land structures from satellite imagery.

翻译：卫星影像通过为学者、科学家及政策制定者提供前所未有的全球空间数据访问能力，极大地推动了地理学领域的变革。传统人工方法通常需要耗费大量时间与精力来识别卫星图像中的通用地貌结构。本研究可衍生出城市规划与开发、环境监测、灾害管理等一系列应用。为此，本文提出一种旨在最大限度减少人力投入、降低地貌识别成本与耗时的技术方案。本研究开发了一种基于深度学习的自动化地理地貌结构分类方法。我们采用来自MLRSNet的卫星影像数据集，对比评估了三种架构的性能：CNN、ResNet-50与Inception-v3。所有模型均使用三种优化器进行测试：Adam、SGD和RMSProp。训练过程固定为100个epoch，批处理大小为64。其中ResNet-50配合ADAM优化器达到76.5%准确率，Inception-v3配合RMSProp优化器达到93.8%准确率，而本研究提出的CNN配合RMSProp优化器方案取得了最优性能，准确率达94.8%。此外，对CNN模型的深入分析表明，其在所有地貌类别上均展现出卓越的精确率、召回率与F1分数，证实了该模型在精确识别多样化地形结构方面具有鲁棒性与可靠性。研究结果凸显了深度学习模型在场景理解方面的潜力，及其在高效识别与分类卫星影像地貌结构方面的重要价值。