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This dataset accompanies the manuscript: > Jimin Jang, Jun-Hyuk Jang, Tae-Hyuk Kwon(2026) A transferable, deterministic landslide detection > Framework using Sentinel-2 and topographic data for rapid inventory > Construction in forested regions. GIScience & Remote Sensing. It contains ground truth (GT) polygons, algorithmically detected polygons, source data behind selected figures, and the QGIS Python scripts used to produce the detection results for two South Korean testbeds: Yecheon (July 2023) and Gokseong (August 2020). dataset/ ├── README.md ├── GT_dataset/ # ground truth landslide polygons │ ├── GT_dataset_Yecheon.gpkg # 93 polygons (Yecheon, 2023) │ └── GT_dataset_Gokseong.gpkg # 61 polygons (Gokseong, 2020) ├── Detection_result/ # algorithmically detected polygons │ ├── Detection_result_Yecheon.gpkg # 160 polygons (Yecheon, 2023) │ └── Detection_result_Gokseong.gpkg # 96 polygons (Gokseong, 2020) ├── Detection_script/ # QGIS Python scripts that produced the detection results from Sentinel-2 imagery and DEM derivatives │ ├── LDscript_Yecheon.py │ └── LDscript_Gokseong.py └── Figure_data/ # source data behind selected figures ├── Fig5_dNDVI.tif # ΔNDVI raster, Yecheon testbed (Fig. 5a) ├── Fig5_dBSI.tif # ΔBSI raster, Yecheon testbed (Fig. 5b) ├── Fig6_GT_stat.csv # topographic statistics of GT polygons (Fig. 6) └── Fig8_Parameter_calib_optimiz.xlsx # parameter calibration results (Fig. 8)

This dataset accompanies a manuscript currently under peer review and is intended solely to support the evaluation of the associated study. The data are made available to ensure transparency of the associated work. Until the peer-review process is completed, users are respectfully asked not to reproduce, redistribute, or reuse these data without the explicit permission of the authors.

This dataset is primarily used for quantitative analysis of microplastic content in soil by combining DRSN-SpCA with 2T2D spectra. The code is written in Python, and the data is stored in .h5 format. main.py: This is the main function, which includes data import, model training, and result prediction. model_2T2D.py: The DRSN-SpCA model is encapsulated within it. early_stopping.py: This is the early stopping function. function_Function.py: The functions required by main.py are encapsulated within it. The specific data is available via DOI: 10.17632/zhdxvgzkfk.1 (PE_1), DOI: 10.17632/d2xz4sby82.1 (PE_2), DOI: 10.17632/2jf5gwrrg9.1 (PE_3_4), DOI: 10.17632/ksxndsxdgs.1 (PET_1), DOI: 10.17632/yp5kjgv58s.1 (PET_2), DOI: 10.17632/pybh5kgwz8.1 (PET_3_4), DOI: 10.17632/jdtnfgd5v7.1 (PP_1), DOI: 10.17632/spzk3vstdf.1 (PP_2), DOI: 10.17632/4wy2nrjbgc.1 (PP_3_4), DOI: 10.17632/wv8mn99ccb.1 (PS_1), DOI: 10.17632/4jy8k7j7ph.1 (PS_2), DOI: 10.17632/txrxrpks44.1 (PS_3_4).

This dataset is a subset of the “2T2D spectroscopy and deep learning for soil MPs quantification” data, comprising 2T2D spectra of soil and microplastics (MPs) mixtures.

When using the data, users should cite the dataset descriptor (https://doi.org/10.48550/arXiv.2604.16769) (given by the link below), rather than the dataset itself on Mendeley Data. This experimental dataset presents both module-level and cell-level characterization data for lithium-ion battery modules composed of three parallel-connected inhomogeneous cells across a wide range of module-level state of health (M-SoH) and cell-to-cell variation (CtCV). First, 70 cells are aged to establish an inventory with cell-level state of health (C-SoH) ranging approximately from 100% to 80% (80% is considered as the end-of-life for automotive applications). From this inventory, 78 battery modules are then assembled, each exhibiting a distinct M-SoH value (from 100% to 80.98%) and a unique CtCV value (from 0% to 9.31%, defined as population standard deviation of C-SoH within each module). Module-level characterization data are collected under 25°C at 0.5C and 0.25C conditions, enabling extraction of module-level capacities and supporting diagnostic analyses such as incremental capacity analysis and differential voltage analysis. Before a module is assembled and tested, cell-level characterization tests are conducted for every individual cell within that module under 1C conditions, enabling direct quantification of CtCV and providing accurate labels for cell-level capacities and internal resistances. The dataset is organized with both raw time-series data and processed summary information such as C-SoH, M-SoH, and CtCV for all modules. With the paired module-level and cell-level characterization data, this dataset enables understanding and development of advanced degradation monitoring mechanisms for battery modules with parallel-connected cells in the presence of CtCVs.

This dataset is a subset of the “2T2D spectroscopy and deep learning for soil MPs quantification” data, comprising 2T2D spectra of soil and microplastics (MPs) mixtures.

This dataset is a subset of the “2T2D spectroscopy and deep learning for soil MPs quantification” data, comprising 2T2D spectra of soil and microplastics (MPs) mixtures.

This dataset is a subset of the “2T2D spectroscopy and deep learning for soil MPs quantification” data, comprising 2T2D spectra of soil and microplastics (MPs) mixtures.

This dataset is a subset of the “2T2D spectroscopy and deep learning for soil MPs quantification” data, comprising 2T2D spectra of soil and microplastics (MPs) mixtures.

This dataset is a subset of the “2T2D spectroscopy and deep learning for soil MPs quantification” data, comprising 2T2D spectra of soil and microplastics (MPs) mixtures.