ABSTRACT Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science.

中文翻译：

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从遥感影像自动识别地形特征：一种深度学习方法

摘要地形特征检测是地形分析和景观场景解释中的一项基本任务。发现特定特征（即沙丘、火山口等）的位置以及它如何随时间演变对于了解地貌过程及其对环境、生态系统和人口的影响至关重要。传统的基于归纳的方法因其在概括多样化和复杂的地形特征方面效率低下以及在可扩展处理可用海量地理空间数据方面的性能而受到挑战。本文提出了一种新的深度学习 (DL) 方法，以支持从遥感图像中自动检测地形特征。这项工作的新颖之处在于：（1）一个包含 12,000 张遥感影像（1,000 张原始影像和 11, 000 张来自数据增强的衍生图像），支持数据驱动的模型训练和新发现；(2) 基于深度学习的物体检测网络，通过集成学习和深度卷积神经网络实现高精度物体检测；(3) 微调模型的特征和行为，以识别超参数和其他网络因素的最佳组合。将 DL 引入地理空间应用预计将对智能地形分析、景观场景解释和空间数据科学的成熟做出重大贡献。(3) 微调模型的特征和行为，以识别超参数和其他网络因素的最佳组合。将 DL 引入地理空间应用预计将对智能地形分析、景观场景解释和空间数据科学的成熟做出重大贡献。(3) 微调模型的特征和行为，以识别超参数和其他网络因素的最佳组合。将 DL 引入地理空间应用预计将对智能地形分析、景观场景解释和空间数据科学的成熟做出重大贡献。