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---
license: cc-by-nc-4.0
task_categories:
- text-to-image
language:
- en
tags:
- climate
- biology
- remote-sensing
- geospatial
- multimodal
- crisis-management
- climate
pretty_name: CrisisLandMark
size_categories:
- 100K<n<1M
---
# CrisisLandMark
**CrisisLandMark** is a large-scale, multimodal corpus for Text-to-Remote-Sensing-Image Retrieval (T2RSIR). It contains over 647,000 Sentinel-1 (SAR) and Sentinel-2 (multispectral optical) images enriched with structured textual and geospatial annotations.
The dataset is designed to move beyond standard RGB imagery, enabling the development of retrieval systems that can leverage the rich physical information from different satellite sensors for applications in Land Use/Land Cover (LULC) mapping and crisis management.
- **Curated by:** Daniele Rege Cambrin
- **License:** Creative Commons Attribution Non Commercial 4.0
- **Repository:** [GitHub](https://github.com/DarthReca/closp)
- **Paper:** [Arxiv](https://arxiv.org/abs/2507.10403)
## Getting Started
To work with the dataset, you will need the `h5py` and `hdf5plugin` packages.
You can try to use the HF dataset implementation, but **I strongly suggest using the GitHub implementation**.
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
The dataset is distributed in HDF5 format. The main file, *crisislandmark.h5*, contains external links to data shards, so all *.h5* files must be kept in the same directory for access. Each key in the HDF5 file corresponds to a unique sample.
Each key contains the following matrices:
| Key | Shape | Data Type | Description |
| :------- | :-------------- | :-------- | :---------------------------------------------------- |
| `image` | `(B, 120, 120)` | `float32` | The image data. B is the number of bands: 2 for Sentinel-1 (VV, VH) or 12 for Sentinel-2. |
| `coords` | `(2, 120, 120)` | `float32` | Contains the x and y coordinates for each pixel of the image. |
and the following attributes:
| Key | Shape | Data Type | Description |
| :---------- | :---- | :---------- | :----------------------------------------------------- |
| `crs` | `(1)` | `float32` | The EPSG code for the Coordinate Reference System. |
| `timestamp` | `(1)` | `float32` | Contains the associated timestamp if available. |
| `labels` | `(L)` | `list[str]` | Contains the list of labels associated with the image. |
The *metadata.parquet* files contain, for each key, the associated split and the labels from the original source (either CLC or DW).
The *queries.jsonl* file contains the queries and their IDs. The qrels for each query can be found in the *qrels* folder.
## Data split
The dataset is divided into two splits based on a stratified multi-label sampling strategy to ensure similar label distribution:
- Training Set: 20% of the data, intended for model training.
- Corpus Set: 80% of the data, intended for retrieval and evaluation.
## Source Data and Annotations
The dataset is built from satellite images sourced from Sentinel-1 and Sentinel-2 missions.
The raw images were drawn from five existing public datasets: re-BEN, CaBuAr, QuakeSet, MMFlood, and Sen12Flood.
All images were processed to a uniform 10-meter spatial resolution and divided into 120x120 pixel patches.
The annotations are created from the following sources:
- Land Use/Land Cover (LULC): Annotations were derived from the CORINE Land Cover (CLC) system for European regions and the global, near-real-time Dynamic World (DW) system for crisis-event images. The script mapping.py in the repository details the mapping between CLC and DW.
- Crisis Events: For images from crisis-focused datasets, original event tags like "wildfire", "flooding", and "earthquake" were retained.
- Geospatial: Every image patch is annotated with its geographic coordinates.
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```bibtex
@misc{cambrin2025texttoremotesensingimageretrievalrgbsources,
title={Text-to-Remote-Sensing-Image Retrieval beyond RGB Sources},
author={Daniele Rege Cambrin and Lorenzo Vaiani and Giuseppe Gallipoli and Luca Cagliero and Paolo Garza},
year={2025},
eprint={2507.10403},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2507.10403},
}
```
## Licensing
The data in this dataset is a compilation of multiple sources, each with its own license. For detailed information on the licensing of each component, please see the [**NOTICE.md**](NOTICE.md) file.
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