GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning
GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning
复制标题
GeoNat v1.0：利用人工智能和监督学习进行自然特征映射的数据集
DOI：
10.1111/tgis.12633
复制
发表时间：
2020
影响因子：
2.4
通讯作者：
Sizhe Wang
中科院分区：
文献类型：
--
作者：
S. Arundel;Wenwen Li;Sizhe Wang
机器学习允许“机器”通过将其暴露给最终产品来推导出管理空间系统的复杂且有时无法识别的规则，特别是地形测绘。通常，这种方法的障碍是获得许多良好的和标记的训练示例的期望结果。大多数类型的自然特征都是如此。为了解决这些限制，本研究引入了GeoNat v1.0，这是一个自然特征数据集，用于支持基于人工智能的映射和监督学习范式下的自然特征自动检测。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创建的，其中包括10类自然特征中的每一类约200个示例。使用基于区域的卷积神经网络在对象检测问题中测试所得数据。物体检测测试的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的主要挑战，如规模和地理代表性。我们希望由此产生的数据集将对各种应用程序有用，并为地理空间人工智能领域的训练数据收集和标记提供帮助。
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.