数据基于多源遥感影像、工程病害实地调查，利用机器学习算法对地形、气象、冻土类型等影响因子进行权重确定，实现了区域尺度冻融灾害的系统识别与制图。数据集重点反映了公路、铁路及原油管道（CRCOP）等线性工程廊道内的灾害集聚特征，揭示了从北部高纬度大片不连续多年冻土区向南部零星岛状多年冻土区过渡过程中，冻害从“成片分布”向“点状/廊道聚集”演变的规律。该数据可为寒区基础设施安全评价及冻土退化环境效应研究提供核心数据支撑。\n
本数据集分类体系（栅格值1-6）严格对应多年冻土区典型工程病害：\n
1：热融湖塘\n
2：路面裂缝\n
3：冻融侵蚀\n
4：路面不均匀沉降\n
5：冰椎\n
6：水毁。", "ds_source": "
综合多源遥感影像（Landsat/Sentinel/高分系列）、实地病害调查点位，通过因子权重，机器学习算法进行冻害分类训练及验证，最后特征提取，得到预测的冻害类型结果，并利用GIS空间分析技术进行区域制图。", "ds_process_way": "
样本采集：在漠河、根河、新林、加格达奇等典型区段采集实地病害样本（GPS定位+现场拍照）。\n
特征建模：引入坡度、植被、地温及工程热扰动因子。\n
识别提取：在大片不连续冻土区侧重识别沉降与热融湖塘；在零星岛状冻土区侧重识别裂缝与侵蚀。\n
精细化修测：利用1:25万地形图及高精度谷歌影像对线性工程沿线的冻害分布进行逻辑修正。", "ds_quality": "
（1）数据采集与源数据：融合了高分辨率多源遥感影像数据与大范围线性工程（公路、铁路、中俄原油管道）的实地病害调查样本，确保了病害识别的真实性。\n
（2）模型方法：采用机器学习算法（如权重确定模型）对海拔、坡度、气温、积雪等多种冻害影响因子进行综合测算，有效降低了传统人工识别的主观性。\n
（3）空间精度：数据以1:25万比例尺进行制图，满足区域尺度及大中型线性工程走廊的灾害评价需求；通过GIS空间分析技术确保了灾害点、带与地形及多年冻土边界的逻辑一致性。\n
（4）验证与校核：重点对漠河枢纽区、根河-伊图里河段、黑河边缘段等典型区域进行了典型性验证，识别结果与实地调查病害吻合度高，能够客观反映暖多年冻土退化背景下的工程响应状态。\n
（5）适用范围：适用于寒区工程地质研究、基础设施风险评估及地学制图等领域。", "ds_acq_start_time": "2023-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "东北多年冻土区", "ds_acq_lon_east": 127.58, "ds_acq_lat_south": 49.11, "ds_acq_lon_west": 117.43, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 118827218, "ds_files_count": 4, "ds_format": "*.tif", "ds_space_res": "100m", "ds_time_res": "2年", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "6799fb86-a763-43d0-b05a-99b8c656693e.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "221ebf56-1b0b-4574-972b-1fb6d3cf1be7", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2026-04-02 20:14:20", "last_updated": "2026-05-12 15:25:34", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7262.2026", "i18n": { "en": { "title": "Distribution data of 100m freeze-thaw disasters in the permafrost region of Northeast China (2023-2024)", "ds_format": "*.tif", "ds_source": "
By integrating multi-source remote sensing images (Landsat/Sentinel/high-resolution series) and on-site disease investigation points, frost damage classification training and validation are carried out through factor weighting and machine learning algorithms. Finally, feature extraction is performed to obtain predicted frost damage types, and GIS spatial analysis technology is used for regional mapping.", "ds_quality": "
(1) Data collection and source data: The fusion of high-resolution multi-source remote sensing image data and large-scale linear engineering (highways, railways, China Russia crude oil pipelines) field disease investigation samples ensures the authenticity of disease identification.\r\n
(2) Model method: Machine learning algorithms (such as weight determination models) are used to comprehensively calculate various freezing damage factors such as altitude, slope, temperature, and snow cover, effectively reducing the subjectivity of traditional manual identification.\r\n
(3) Spatial accuracy: The data is mapped at a scale of 1:250000 to meet the disaster assessment needs of regional scales and large and medium-sized linear engineering corridors; The logical consistency between disaster points, zones, terrain, and permafrost boundaries was ensured through GIS spatial analysis technology.\r\n
(4) Validation and verification: Typical validation was carried out on typical areas such as the Mohe Hub, Genhe Itui River section, and Heihe River edge section. The identification results were highly consistent with the field investigation of diseases, and could objectively reflect the engineering response status under the background of warm permafrost degradation.\r\n
(5) Scope of application: Suitable for engineering geological research, infrastructure risk assessment, and geological mapping in cold regions.", "ds_ref_way": "", "ds_abstract": "
The data is based on multi-source remote sensing images and field investigations of engineering diseases. Machine learning algorithms are used to determine the weights of factors such as terrain, weather, and frozen soil types, achieving systematic identification and mapping of regional scale freeze-thaw disasters. The dataset focuses on reflecting the disaster aggregation characteristics within linear engineering corridors such as highways, railways, and crude oil pipelines (CRCOP), revealing the evolution of frost damage from \"patchy distribution\" to \"point/corridor aggregation\" during the transition from large discontinuous permafrost areas in the north to scattered island shaped permafrost areas in the south. This data can provide core data support for the safety evaluation of infrastructure in cold regions and the study of environmental effects of permafrost degradation.\r\n
The classification system of this dataset (grid values 1-6) strictly corresponds to typical engineering diseases in permafrost regions:\r\n
1: Hot melt lake pond\r\n
2: Road cracks\r\n
3: Freeze-thaw erosion\r\n
4: Uneven settlement of road surface\r\n
5: Ice vertebrae\r\n
6: Water destroyed.", "ds_time_res": "", "ds_acq_place": "Northeast Permafrost Region", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Sample collection: Collect field disease samples (GPS positioning+on-site photography) in typical sections such as Mohe, Genhe, Xinlin, and Jiagedaqi.\r\n
Feature modeling: Introducing slope, vegetation, ground temperature, and engineering thermal disturbance factors.\r\n
Identification and extraction: In large areas of discontinuous permafrost, emphasis is placed on identifying subsidence and thermal melting lakes and ponds; Emphasis is placed on identifying cracks and erosion in sporadic island shaped frozen soil areas.\r\n
Refined measurement: Use 1:250000 topographic maps and high-precision Google imagery to logically correct the distribution of frost damage along linear engineering lines.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "冻融灾害", "工程廊道", "机器学习", "空间分布特", "线性基础设施" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "东北多年冻土区" ], "ds_time_tags": [ 2023, 2024 ], "ds_contributors": [ { "true_name": "金会军", "email": "hjjin@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "唐建军", "email": "jianjuntang@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "王文辉", "email": "wangwenhui@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "金晓颖", "email": "xyj@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "李善珍", "email": "lsz@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "黄帅", "email": "s_hwang@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "陈敦", "email": "chendun@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王宏伟", "email": "wanghw@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "杨岁桥", "email": "yangsuiqiao@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "李祖旺", "email": "lzwang@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "燕翱翔", "email": "yanaoxiang@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "程耀辉", "email": "yhcheng@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "李景涛", "email": "ljtao@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "张泽", "email": "zez@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "王立峰", "email": "9431629@qq.com", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "张虎", "email": "zhanghu@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "刘萌心", "email": "liumengxin@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "张圣嵘", "email": "zhangshengrong@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "杨雪", "email": "yangx014@nenu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "刘子瑞", "email": "lzr@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "岳子颖", "email": "zyyue@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "吴海彬", "email": "haibinwu@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "邢鲁宁", "email": "18363701763@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "陈思宇", "email": "chensy@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "徐景妍", "email": "JingyanXu@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "何峥雲", "email": "yunhardworking@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "米虹岐", "email": "mimhq07@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "彭文昊", "email": "Pengwh@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "梁峻贺", "email": "liangjh@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "史艳玲", "email": "15247298387@163.com", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "周智熠", "email": "v1ncentharrious@outlook.com", "work_for": "东北林业大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "金会军", "email": "hjjin@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "唐建军", "email": "jianjuntang@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" }, { "true_name": "王文辉", "email": "wangwenhui@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" } ], "ds_managers": [ { "true_name": "金会军", "email": "hjjin@nefu.edu.cn", "work_for": "东北林业大学", "country": "中国" } ], "category": "灾害" }