A Comprehensive County-Scale Dataset for Geohazard Risk Assessment of House Construction via Cutting Slope (HCCS)

House construction via cutting slope (HCCS) is an increasingly important driver of geohazards in the granite weathering regions of southeastern China. In these areas, frequent intense rainfall and the rapid growth of self-built rural housing together contribute to recurring landslides at HCCS sites. However, relevant geo-environmental data are often dispersed across survey reports in non-standardized formats, hindering their integration and reuse in fine-scale spatial analyses. We describe a county-wide geospatial dataset for Yanling County, Hunan Province, China, that links slope units, geohazard inventories, and HCCS sites within a unified spatial framework. The dataset includes 3478 slope units, 213 documented geohazard sites (landslides, debris flows, collapses, and ground subsidence), and 12,420 HCCS sites, as well as supporting layers including a digital elevation model, geological structures, river networks, and administrative boundaries. The data were generated from a 1:10,000 geohazard survey using a standardized workflow covering field investigation, database development, and multi-level review, and were further harmonized through unified coordinate and attribute systems. This dataset provides a rare fine-scale representation of the spatial coupling between anthropogenic slope modification for housing and rainfall-induced landslides at the individual-building scale. It offers a reusable data foundation for landslide susceptibility mapping, risk evaluation of HCCS development, and studies of coupled hazard processes in comparable granite mountainous environments.

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Published
2026-10-09
DOI
https://doi.org/10.3390/data11100270
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Landslides and related hazards
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article
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article

A Comprehensive County-Scale Dataset for Geohazard Risk Assessment of House Construction via Cutting Slope (HCCS)

Guangjun Zou, Shuangshuang Wu, haoyang Su, Mingbo Li
Data
Landslides and related hazards
article

A Comprehensive County-Scale Dataset for Geohazard Risk Assessment of House Construction via Cutting Slope (HCCS)

Guangjun Zou, Shuangshuang Wu, haoyang Su, Mingbo Li
article en

Abstract

House construction via cutting slope (HCCS) is an increasingly important driver of geohazards in the granite weathering regions of southeastern China. In these areas, frequent intense rainfall and the rapid growth of self-built rural housing together contribute to recurring landslides at HCCS sites. However, relevant geo-environmental data are often dispersed across survey reports in non-standardized formats, hindering their integration and reuse in fine-scale spatial analyses. We describe a county-wide geospatial dataset for Yanling County, Hunan Province, China, that links slope units, geohazard inventories, and HCCS sites within a unified spatial framework. The dataset includes 3478 slope units, 213 documented geohazard sites (landslides, debris flows, collapses, and ground subsidence), and 12,420 HCCS sites, as well as supporting layers including a digital elevation model, geological structures, river networks, and administrative boundaries. The data were generated from a 1:10,000 geohazard survey using a standardized workflow covering field investigation, database development, and multi-level review, and were further harmonized through unified coordinate and attribute systems. This dataset provides a rare fine-scale representation of the spatial coupling between anthropogenic slope modification for housing and rainfall-induced landslides at the individual-building scale. It offers a reusable data foundation for landslide susceptibility mapping, risk evaluation of HCCS development, and studies of coupled hazard processes in comparable granite mountainous environments.

DataVol. 11(10)
Hohai University (CN)
Openalex Percentile: Top 9%
Landslides and related hazards
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A Comprehensive County-Scale Dataset for Geohazard Risk Assessment of House Construction via Cutting Slope (HCCS) — Guangjun Zou, Shuangshuang Wu, et al. · Data (2026) | TGRS Research Map | TGRS