遇见数据集

Gully Erosion Dataset

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DataCite Commons2025-05-12 更新2025-05-17 收录
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An gully erosion dataset was constructed by using high-resolution optical imagery. The imagery covers key agricultural regions within Lishu County (Siping City), Nong'an County (Changchun City), Qianguo County and Fuyu City (Songyuan City) in Jilin Province, China. Data acquisition occurred during periods of low vegetation cover (April-May and October-November) between 2020 and 2023 to optimize gully visibility. The study areas are predominantly characterized by chernozem, known for their susceptibility to soil erosion. Integrating findings from field surveys and high-resolution remote sensing interpretation, the identified gully erosion features were categorized into three primary types: (1) ephemeral gullies (small-scale, transient channels formed by concentrated overland flow, often recurring seasonally); (2) permanent gullies (deep-seated, stable channels with near-vertical sidewalls and irreversible morphology); and (3) gullies in complex environments (features whose detection is hindered by vegetation, terrain complexity, or anthropogenic disturbance). An initial dataset of gully images was constructed. All images were standardized to 512×512 pixels for uniformity. Using the LabelMe software, annotations were created, marking gully pixels in red and background pixels in black. To enhance dataset scale and diversity for robust model training, data augmentation was performed using spatial-level enhancements (rotation, flipping) and pixel-level enhancements (blurring, noise addition, contrast adjustment) for each gully type.

本沟壑侵蚀数据集通过高分辨率光学影像构建,覆盖中国吉林省四平市梨树县、长春市农安县、松原市前郭县及扶余市的核心农业区域。数据采集于2020年至2023年的低植被覆盖期(4-5月与10-11月),以最大化沟壑的可视性。研究区以黑钙土(chernozem)为主,该类土壤对土壤侵蚀具有较高敏感性。 结合野外调查与高分辨率遥感解译成果,本次识别的沟壑侵蚀特征被划分为三大类别:(1) 暂时性沟壑(ephemeral gullies):由集中地表径流形成的小型临时通道,通常随季节周期性重现;(2) 永久性沟壑(permanent gullies):具有近乎垂直侧壁、形态不可逆的深层稳定通道;(3) 复杂环境沟壑:受植被覆盖、地形复杂性或人为干扰影响,难以准确检测的侵蚀特征。 首先构建了初始沟壑图像数据集,为保证数据统一性,所有图像均被标准化为512×512像素。采用LabelMe软件进行标注:将沟壑像素标记为红色,背景像素标记为黑色。为扩大数据集规模并提升多样性以支撑稳健的模型训练,针对每一类沟壑分别开展数据增强操作,包括空间级增强(旋转、翻转)与像素级增强(模糊处理、添加噪声、对比度调整)。

提供机构:
Mendeley Data
创建时间:
2025-05-12
搜集汇总
背景与挑战
背景概述
该数据集是一个基于高分辨率光学影像构建的沟壑侵蚀数据集,覆盖中国吉林省多个农业区域,数据采集于2020-2023年的低植被覆盖期以优化可见性。数据集将沟壑分为短暂、永久和复杂环境三类,图像统一为512×512像素,并经过标注和数据增强处理,旨在支持稳健的模型训练。
以上内容由遇见数据集搜集并总结生成
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