A neural network for cloud masking in WorldView satellite imagery

This repository includes the cloud detection algorithm used in the manuscript 'Topography controls variability in circumpolar permafrost thaw pond expansion' by Abolt et al. It also includes a demonstration of the algorithm's use at a survey area in northern Alaska and a demonstration of training the algorithm. This dataset includes .pt place-text, .py python, .tif image, .ipynb Jupyter notebook, and .txt text files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

本仓库收录了Abolt等学者在论文《地形调控环北极多年冻土融塘扩张的变异性》中所使用的云检测算法。同时，本仓库还提供了该算法在阿拉斯加北部某调查区域的应用演示，以及算法训练流程的演示案例。本数据集包含以下类型文件：.pt格式的位置文本文件、.py格式的Python脚本文件、.tif格式的图像文件、.ipynb格式的Jupyter Notebook，以及.txt格式的纯文本文件。
下一代北极生态系统实验（Next-Generation Ecosystem Experiments: Arctic, 简称NGEE Arctic）是一项旨在通过深化对富碳北极生态系统的预测性认知及其气候反馈机制，以降低地球系统模型不确定性的研究项目。该项目由美国能源部生物与环境研究办公室资助。
NGEE Arctic项目设有两处野外研究站点：其一位于阿拉斯加州乌特恰维克（原名为巴罗）北坡的巴罗环境观测站（Barrow Environmental Observatory, BEO）内的北极多边形苔原沿海区域；其二位于阿拉斯加州诺姆以北的苏厄德半岛不连续多年冻土区的多个区域。
通过观测、实验以及与现有数据集的综合整合分析，NGEE Arctic项目为多尺度建模构建了更为完善的知识库，并推动了美国能源部地球系统模型（能源极端尺度地球系统模型，即Energy Exascale Earth System Model, 简称E3SM）——尤其是其陆面模型组件（ELM, E3SM Land Model）——在泛北极全球尺度上的过程表征精度的提升。