Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain.

中文翻译：

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GeoNat v1.0：用于通过人工智能和监督学习进行自然特征映射的数据集

机器学习使“机器”将其暴露于最终产品中，从而推论出控制空间系统（尤其是地形图）的复杂且有时无法识别的规则。通常，这种方法的障碍是获得许多理想结果的标记好的训练示例。大多数类型的自然特征就是这种情况。为了解决这些限制，本研究引入了自然特征数据集GeoNat v1.0，该数据集用于在监督学习范式下支持基于人工智能的地图绘制和自然特征的自动检测。该数据集是通过从美国地质调查局的地理名称信息系统中随机选择点而创建的，包括大约200个示例，每类10个自然特征。使用基于区域的卷积神经网络在对象检测问题中测试了所得数据。对象检测测试得出的平均平均精度为基线结果的62％。本文讨论了在地理空间领域开发培训数据时遇到的主要挑战，例如规模和地理代表性。我们希望所得的数据集可用于各种应用程序，并为地理空间人工智能领域的训练数据收集和标记提供启示。