This dataset system displays the spatial layout of frost damage in the permafrost region of Northeast China under human engineering activities such as highways, railways, and crude oil pipelines, revealing the core characteristics of "zonal macroscopic control and corridor microscopic aggregation". The main features include a disaster classification system: based on distribution density and main control factors, the entire area is divided into "high-density engineering disturbance dominant zone", "medium density environment engineering coupling zone", and "low-density seasonal frozen soil background zone". Type identification: Detailed records of typical engineering diseases such as uneven pavement settlement, pavement cracks, hot melt lakes and ponds, freeze-thaw erosion, ice cones, and water damage. Engineering corridor effect: It particularly highlights the disaster aggregation trend along G111, G331, and the China Russia crude oil pipeline (CRCOP), depicting the sharp decrease in frozen soil strength under the "superposition disturbance effect". And micro analysis: including the typical development characteristics of five engineering hubs, namely Mohe, Genhe Ituli River, Xinlin, Heihe, and the southern section of Gagdachi.
The classification system of this dataset (grid values 1-6) corresponds to the types of diseases investigated in the field as follows:
1: Hot melt lake pond.
2: Road cracks.
3: Freeze thaw erosion.
4: Uneven settlement of road surface.
5: Ice cone/ice melting phenomenon.
6: Water destroyed.
Note: The NoData value is 255.
Independently generated, integrating multiple field scientific expedition records and machine learning models to obtain prediction results.
Using machine learning algorithms (random forest) to train and analyze multiple environmental factors (such as ground temperature, vegetation, terrain slope, etc.), and based on the 1:100000 freezing damage data obtained, perform further resolution processing to obtain the data results.
Field investigation: Integrate long-term disease records from typical monitoring areas such as Mohe, Tahe, and Genhe in the Greater Khingan Range and Lesser Khingan Range.
Multi source fusion: integrates the "passive heat collection" characteristics of black asphalt pavement on highways such as G331, G332, G111, as well as the melting circle data generated by the "active heat source" of CRCOP pipeline.
Graded characterization:
High density zone: Lock in the discontinuous permafrost zone in the north, highlighting the continuity of composite diseases.
Medium density zone: Locking the transition zone and island shaped frozen soil area, highlighting patchy and sudden changes.
Low density area: Lock in seasonal frozen soil and the southern boundary, highlighting a single seasonal frost heave cracking.
(1) Accuracy and coverage: Covering the permafrost regions throughout Northeast China. By combining multi-source remote sensing interpretation with field investigations, the authenticity and accuracy of the location of freezing damage points have been ensured.
(2) Disaster mechanism verification: The data not only annotated the location of the disaster, but also systematically summarized the spatial location, disaster process, and characteristics of each type of disaster through mechanism analysis.
(3) Comparative analysis of typical areas: Select typical engineering corridors for enlarged analysis based on different permafrost backgrounds, and verify the sensitivity of data in capturing "superimposed disturbances" and "local thermal responses".
(4) Applicability: This map provides a benchmark reference for distinguishing frost damage in permafrost engineering from seasonal permafrost engineering diseases, and is suitable for maintenance cost accounting, disease control design, and long-term dynamic monitoring of linear engineering in high-altitude regions.
| # | number | name | type |
| 1 | 2022FY100700 | Survey of Permafrost Conditions and Freeze-Thaw Damage in the High-Latitude Regions of Northeast China | Basic Resource Survey Project |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.NIEER.DB7265.2026
10.12072/ncdc.nieer.db7265.2026
