Dataset of dual-camera close-range photogrammetry for marker-based slope surface monitoring(DCCRP)
Authors/Creators
- Zhang, Sichao (Project leader)1
- Li, Shuangshuang (Contact person)2
- Gong, Wei (Data collector)2
- Tao, Tao (Data collector)2
- Mo, Shuangling (Data collector)2
- Tao, Longqing (Data collector)2
- Li, Ruidong (Distributor)3
- Han, Bingbing (Distributor)3
- Li, Yushan (Project member)4
- Zhang, Yongjun (Distributor)4
- et al. Show all 13 authors
- Zhang, Sichao (Project leader)1
- Li, Shuangshuang (Contact person)2
- Gong, Wei (Data collector)2
- Tao, Tao (Data collector)2
- Mo, Shuangling (Data collector)2
- Tao, Longqing (Data collector)2
- Li, Ruidong (Distributor)3
- Han, Bingbing (Distributor)3
- Li, Yushan (Project member)4
- Zhang, Yongjun (Distributor)4
- Cong, Kai (Distributor)4
- Wei, Jie (Distributor)4
- Han, peng (Project leader)2
Contributors
Contact person:
Description
This dataset incloudes a marker-based dual-camera close-range photogrammetry data for field slope displacement monitoring. The dataset contains high-resolution image sequences, extracted pixel coordinates, and synchronous Real-Time Kinematic (RTK) 3D coordinate ground truth acquired in a complex natural environment. Data collection was performed using a customized system comprising two 4K industrial time-lapse cameras, wind-resistant composite fiberglass markers, and an off-grid solar-powered transmission module. The dataset is structured around two main components: (1) continuous observation data under varying illumination conditions, capturing the complete images and pixel trajectories of 14 markers over a 48-hour period that encompasses midday solar overexposure, afternoon lighting, and nighttime artificial illumination; and (2) simulated displacement data, documenting the controlled movement of 13 markers across five progressive stages. For each displacement stage, synchronized dual-camera images and five repeated high-precision RTK coordinate measurements were recorded. Provided in multiple accessible formats (.jpg, .txt, .csv), this dataset serves as a reliable benchmark for quantitatively evaluating the spatial reconstruction errors of non-contact monitoring systems, testing field image registration algorithms, and developing environmental noise-filtering methods under complex lighting conditions.
Files
01_Lighting_Conditions.zip
Files
(6.8 GB)
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Additional details
Dates
- Submitted
-
2026-05-12