Maxwell Aaron E, Farhadpour Sarah, Das Srinjoy, Yang Yalin
Department of Geology and Geography, West Virginia University, Morgantown, WV, United States of America.
School of Mathematical and Data Sciences, West Virginia University, Morgantown, WV, United States of America.
PLoS One. 2024 Dec 5;19(12):e0315127. doi: 10.1371/journal.pone.0315127. eCollection 2024.
Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer's accuracies, class-level precisions or user's accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development.
基于卷积神经网络（CNN）的深度学习（DL）方法，因其能够在多个尺度上对空间上下文信息进行建模，已改变了地理空间、地球观测和地球物理数据的分析方式。此类方法特别适用于像素级分类或语义分割任务。已经开发了各种R包来处理和分析地理空间数据。然而，目前在R语言和数据科学环境中还没有用于实现地理空间深度学习的包。本文介绍了geodl R包，它支持将像素级分类应用于广泛的地理空间或地球科学数据，这些数据可以表示为多维数组，其中每个通道或波段持有一个预测变量。geodl基于torch包构建，该包支持使用R和C++语言实现深度学习，而无需安装Python/PyTorch环境。这大大简化了在R中实现深度学习所需的软件环境。使用geodl，可以使用terra包读取和处理具有不同波段数量、空间分辨率和坐标参考系统的基于地理空间栅格的数据，terra包利用C++，并允许处理太大而无法装入内存的栅格网格。训练循环使用luz包实现。geodl包提供了实用函数，用于从基于矢量的地理空间数据创建栅格掩码或标签，以及从较大文件和范围创建图像芯片和相关掩码。它还为地理空间数据定义了一个torch数据集子类，以便与torch数据加载器一起使用。基于UNet的模型提供了各种可选的辅助模块或修改。实现了常见的评估指标（即总体准确率、类级召回率或生产者准确率、类级精度或用户准确率以及类级F1分数），以及统一焦点损失框架的修改版本，该版本允许使用一个一致的实现和一组超参数定义各种损失指标。用户可以使用标准的地理空间和遥感指标及方法评估模型，并使用经过训练的模型对大空间范围进行预测。本文介绍了geodl的工作流程、设计理念和未来发展目标。
