卡宁半岛 (俄罗斯西北部) 的第一个逆向解衰退 (RTS) 库存
Ionut Cristi Nicu1, Lena Rubensdotter2,3, Hakan Tanyaș4
1High North Department, Norwegian Institute for Cultural Heritage Research (NIKU), Fram Centre, N-9296, Tromsø, Norway. ionut.cristi.nicu@niku.no.
Scientific data
|July 14, 2025
概括
研究人员创建了一个反向解衰退 (RTS) 的数据集,以训练机器学习模型来监测永久土解及其对北极景观和土著社区的影响.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
背景情况:
- 逆行性融化低谷 (RTS) 是显著的热岩地形,表明永久土的退化.
- 这些特征在北极和高山地区普遍存在,与气候变化直接相关.
- 了解RTS动态对于评估景观变化和对生态系统和人类活动的影响至关重要.
研究的目的:
- 为机器学习模型培训开发回归解衰退 (RTS) 的代表性数据集.
- 为了能够自动检测和分类活跃与非活跃的RTS功能.
- 促进区域范围内监测永久土融化及其后果.
主要方法:
- 900个RTS多边形的手动数字化使用2022年ESRI Wayback卫星图像在俄罗斯西北半岛的卡宁半岛.
- 将数字化RTS根据形态和植被特征分为活跃和不活跃的类别.
- 创建一个精选的数据集,用于训练机器学习算法.
主要成果:
- 创建了一个由900个手动数字化的RTS多边形组成的数据集,其中633个被归类为不活跃的,267个被归类为活跃的.
- 该数据集为开发用于RTS检测和分类的机器学习模型提供了基础.
- 这项工作使得未来能够对永久土退化模式进行区域范围的分析.
结论:
- 开发的数据集是推动自动化RTS监测和永久土研究的宝贵资源.
- 机器学习方法可以显著提高绘制和分类热岩特征的效率.
- 这项研究有助于了解气候变化对敏感的北极环境和土著生计的影响.
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