Understanding and exploiting topographical data via standard machine learning techniques is challenging, mainly due to the large dynamic range of values present in elevation data and the lack of direct relationships between anthropogenic phenomena and topography, when considering topographic–geology couplings, for instance. Here we consider the first hurdle, dynamic range, in an effort to apply Convolutional Neural Network (CNN) approaches for prediction of human activity. CNN for learning 3-D elevation data relies on data normalization approaches, which only consider locally available points, thereby discarding contextual information and eliminating global contrast cues. We present a fully invertible and data-driven global partitioning elevation normalization (GPEN) preprocessing technique, which is intended to ameliorate the impact of limited data dynamic range. Global elevation populations are derived and used to formulate a distribution, which is used to adopt a partitioning scheme to remap all values according to global occurrence frequency, while preserving partition contrast. Using USGS 3-D Elevation Project and Microsoft building footprint data, we conduct a binary classification experiment predicting building footprint presence from elevation data, with and without a global remapping using the SegNet convolutional encoder-decoder model. The results of the experiment show more rapid model convergence, reduced regionalization errors, and enhanced classification metrics when compared to standard normalization preprocessing techniques. GPEN demonstrates performance over 10% higher than the next best conventional preprocessing method, with a mean overall accuracy of 94.76%. GPEN may show promise as an alternative normalization for deep learning with topological data.

中文翻译：

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全局分区高程归一化应用于建筑足迹预测

例如，在考虑地形-地质耦合时，通过标准机器学习技术理解和利用地形数据具有挑战性，这主要是由于高程数据中存在的值的大动态范围以及人为现象与地形之间缺乏直接关系。在这里，我们考虑第一个障碍，即动态范围，以应用卷积神经网络 (CNN) 方法来预测人类活动。用于学习 3-D 高程数据的 CNN 依赖于数据归一化方法，该方法仅考虑局部可用点，从而丢弃上下文信息并消除全局对比度提示。我们提出了一种完全可逆和数据驱动的全局分区高程归一化（GPEN）预处理技术，这旨在改善有限数据动态范围的影响。导出全局高程种群并用于制定分布，用于采用分区方案根据全局出现频率重新映射所有值，同时保留分区对比度。使用 USGS 3-D Elevation Project 和 Microsoft 建筑足迹数据，我们进行了一项二元分类实验，通过使用 SegNet 卷积编码器-解码器模型进行或不进行全局重新映射的高程数据预测建筑物足迹的存在。实验结果表明，与标准归一化预处理技术相比，模型收敛速度更快，区域化错误更少，分类指标增强。GPEN 的性能比次佳的传统预处理方法高 10% 以上，平均总体准确率为 94.76%。GPEN 可能显示出作为拓扑数据深度学习的替代规范化的前景。