CALFIN: Calving front dataset for East/West Greenland, 1972-2019

We collect our source data from Landsat NIR band images spanning from 1972 to 2019. We provide 1997 manually-masked calving fronts, which we use for training, validating, and testing our automated algorithm. We also provide over 17912 automatically generated fronts, along with estimated mean errors calculated for each basin. This dataset contains a total of 19909 calving fronts for Helheim, Kangerlussuaq, Kong Oscar, Hayes, Rink Isbrae, Upernavik, Jakobshavn, Petermann, Kangiata Nunaata, and 62 other nearby glaciers along East/West Greenland. We process our source data by utilizing deep learning, in the form of the Google DeeplabV3+ Xception derived CALFIN Neural Network. Additional post-processing techniques allow our method to achieve accurate and useful segmentation of raw Landsat subsets into Shapefile outputs.

本数据集的源数据采集自1972年至2019年间的陆地卫星（Landsat）近红外（NIR）波段影像。本数据集提供1997条经人工掩膜标注的冰川崩解前缘数据，用于训练、验证与测试我们的自动化算法。此外，数据集还包含超过17912条自动生成的崩解前缘数据，以及针对各流域计算得到的估计平均误差值。本数据集总计涵盖19909条冰川崩解前缘数据，覆盖黑尔海姆冰川（Helheim）、康克鲁斯瓦格冰川（Kangerlussuaq）、奥斯卡国王冰川（Kong Oscar）、海斯冰川（Hayes）、林克冰架（Rink Isbrae）、乌佩纳维克冰川（Upernavik）、雅各布港冰川（Jakobshavn）、彼德曼冰川（Petermann）、坎吉阿塔·努纳塔冰川（Kangiata Nunaata），以及格陵兰岛东西两侧的62处邻近冰川。本数据集采用基于谷歌（Google）开发的DeeplabV3+ Xception架构衍生的CALFIN神经网络，依托深度学习技术完成源数据处理。辅以额外的后处理技术，本方法可将原始陆地卫星影像子集精准分割为Shapefile格式的输出文件。