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Sen12Landslides: A Spatio-Temporal Dataset for Satellite-Based Landslide and Anomaly Detection

This repository hosts Sen12Landslides, a large-scale, multi-sensor benchmark for spatio-temporal landslide and anomaly detection. It comprises 39,556 NetCDF patches (128×128 px with 10m resolution) spanning 15 time steps, derived from both Sentinel-1 (VV, VH; ascending/descending) and Sentinel-2 (10 spectral bands B02–B12). Every patch includes additionally:

  • A binary landslide mask
  • A high-resolution digital elevation model (DEM)
  • Rich metadata (event dates, trigger type, geolocation etc.)

We also provide two task-specific subsets which can be found in the GitHub Repo:

  • S12LS-LD for supervised landslide detection
  • S12LS-AD for anomaly detection

This release features over 75 000 annotated landslide polygons and ~12 000 multi-temporal image patches across SAR, optical, and elevation modalities, enabling rapid development and benchmarking of geohazard detection models:

Full Dataset

Modality Samples Annotated Ann. Rate
S1-asc 13,306 6,492 48.8%
S1-dsc 12,622 6,347 50.3%
S2 13,628 6,737 49.4%
Aligned 11,719 6,026 51.4%

Task Splits

Modality S12LS-LD S12LS-AD
S1-asc 4,793 (100%) 13,306 (48.8%)
S1-dsc 4,666 (100%) 12,622 (50.3%)
S2 4,988 (100%) 13,628 (49.4%)
Aligned 4,392 (100%) 11,719 (51.4%)

Paper link: https://www.nature.com/articles/s41597-025-06167-2

For any UPDATES, baselines, preprocessing scripts, data loaders or model-training pipelines please visit: GitHub repository

Some of the data used in this dataset was downloaded using terragon, a tool for streamlined Earth observation data acquisition using multiple APIs like Microsoft Planetary Computer, GEE, ASF or CDSE. Please take a look!

Data Versions

Harmonized (recommended)

The data_harmonized/ folder contains radiometrically consistent data that has been pre-processed and bounded for stable model training:

  • Sentinel-1 (Backscatter):
    • VH and VV bands converted from linear power to decibels (dB)
    • Values bounded to [-50, 10] dB to remove extreme noise and specular outliers
  • Sentinel-2 (Reflectance):
    • Bands B02–B12 corrected for the +1000 DN radiometric offset introduced by ESA Baseline 04.00 (January 25, 2022 onward)
    • Values bounded to [0, 10000] DN to ensure physical reflectance consistency
  • DEM (Elevation):
    • Values bounded to [0, 8800] m to maintain a global terrain baseline

MASK, SCL, and spatial_ref are unchanged in both versions.

Raw (original)

The data_raw/ folder preserves the data exactly as published in the original dataset paper, ensuring full reproducibility of reported results:

  • Sentinel-1: Linear power scale (not converted to dB)
  • Sentinel-2: No radiometric offset correction applied
  • DEM/MASK: Unmodified

The conversion functions for both corrections are available in the utils.py file of the GitHub repository.

Data Structure

The dataset is organized into three main folders based on the satellite data source:

├── data_harmonized/        ← recommended for training
│   ├── s1asc/
│   │   ├── s1asc_part01.tar.gz
│   │   └── ...
│   ├── s1dsc/
│   │   ├── s1dsc_part01.tar.gz
│   │   └── ...
│   └── s2/
│       ├── s2_part01.tar.gz
│       └── ...
├── data_raw/               ← original paper version
│   ├── s1asc/
│   │   ├── s1asc_part01.tar.gz
│   │   └── ...
│   ├── s1dsc/
│   │   ├── s1dsc_part01.tar.gz
│   │   └── ...
│   └── s2/
│       ├── s2_part01.tar.gz
│       └── ...
└── inventories.shp.zip

inventories.shp.zip contains the official ground-truth landslide polygons used for annotation, aligned with the image patches in the dataset.

Each .tar.gz file in s1asc/, s1dsc/, or s2/ holds multiple .nc (NetCDF) files. Each file is a 128×128 pixel patch covering a specific location and includes 15 time steps of Sentinel-1 or Sentinel-2 data.

Filenames follow this pattern:

<region>_<sensor>_<id>.nc
e.g. italy_s2_6982.nc

Each file includes multi-temporal imagery, a binary MASK for landslide areas, and metadata like event date and pre/post indices. Sentinel-2 files may also contain DEM and cloud masks (SCL).

The output of such a file looks like the following after calling: xr.open_dataset("Sen12Landslides/data/s2/italy_s2_6982.nc")

<xarray.Dataset> Size: 6MB
Dimensions:      (time: 15, x: 128, y: 128)
Coordinates:
  * x            (x) float64 1kB 7.552e+05 7.552e+05 ... 7.565e+05 7.565e+05
  * y            (y) float64 1kB 4.882e+06 4.882e+06 ... 4.881e+06 4.881e+06
  * time         (time) datetime64[ns] 120B 2022-10-05 2022-10-30 ... 2023-09-10
Data variables: (12/14)
    B02          (time, x, y) int16 492kB ...
    B03          (time, x, y) int16 492kB ...
    B04          (time, x, y) int16 492kB ...
    B05          (time, x, y) int16 492kB ...
    B06          (time, x, y) int16 492kB ...
    B07          (time, x, y) int16 492kB ...
    ...           ...
    B11          (time, x, y) int16 492kB ...
    B12          (time, x, y) int16 492kB ...
    SCL          (time, x, y) int16 492kB ...
    MASK         (time, x, y) uint8 246kB ...
    DEM          (time, x, y) int16 492kB ...
    spatial_ref  int64 8B ...
Attributes:
    ann_id:           41125,41124,37694,37696,41131,37693,37689,37695,37749,3...
    ann_bbox:         (755867.5791119931, 4880640.0, 755900.7341873142, 48806...
    event_date:       2023-05-16
    date_confidence:  1.0
    pre_post_dates:   {'pre': 7, 'post': 8}
    annotated:        True
    satellite:        s2
    center_lat:       4881280.0
    center_lon:       755840.0
    crs:              EPSG:32632

For the corresponding Sentinel-1 data, the overall structure remains the same, but the data variables are adapted to SAR input, containing VV and VH bands instead of optical bands. The metadata attributes are consistent across modalities, with the only change being the satellite attribute set to "s1" instead of "s2".

Download 

pip install --upgrade huggingface_hub

hf auth login  # paste your token from https://huggingface.co/settings/tokens (only first time)

# Download harmonized version
hf download paulhoehn/Sen12Landslides --repo-type dataset --local-dir /path/to/local_folder --include "data_harmonized/**"

# Download raw version (for reproducing original paper results)
hf download paulhoehn/Sen12Landslides --repo-type dataset --local-dir /path/to/local_folder --include "data_raw/**"

# Download everything
hf download paulhoehn/Sen12Landslides --repo-type dataset --local-dir /path/to/local_folder

Baselines

Due to class imbalance (~3% landslides), we provide, additionaly to our macro-avg metrics in the paper, binary metrics on the landslide class for benchmarking against other detection methods.

Note: To compare landslide detection performance, use the binary metrics below rather than the macro-averaged metrics from the paper. Baselines were trained on original raw data. The results for harmonized data may differ slightly, but they are planned to be provided here as well.

Benchmark Results (S12LS-LD)

Benchmark using paper architectures with binary metrics on Sentinel-2 + DEM:

Model Precision Recall F1-Score IoU AP
ConvGRU 0.53 0.67 0.59 0.42 0.61
U-TAE 0.41 0.86 0.55 0.38 0.69
Unet3D 0.52 0.68 0.59 0.42 0.63
U-ConvLSTM 0.55 0.73 0.63 0.46 0.67

Three training runs (seed=42,123,777) were performed for each model on the S12LS-LD split with lit_module=binary for 100 epochs (early stopping enabled). Test metrics were averaged across seeds on the held-out test set. See configs/ for full settings.

Acknowledgement

Sen12Landslides was funded by HELMHOLTZ IMAGING, a platform of the Helmholtz Information & Data Science Incubator [grant number: ZT-1-PF-4-028].

This work was enabled by the computational and data resources provided through the "terrabyte" HPDA project of the German Aerospace Center (DLR) and Leibniz Supercomputing Center (LRZ), where Sentinel-1 NRB data was preprocessed and accessed.

Sentinel-2 Level-2A data were downloaded from Microsoft Planetary Computer and are provided under Copernicus Sentinel license conditions (© European Union 2015–2025, ESA) (https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a).

The DEM data is produced using Copernicus WorldDEM-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.

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