ELAI-Dust Storm (ELAI Dust Storm Dataset from MODIS)

Context

As mentioned in the reference paper:

Dust storms are considered a severe meteorological disaster, especially in arid and semi-arid regions, which is characterized by dust aerosol-filled air and strong winds across an extensive area. Every year, a large number of aerosols are released from dust storms into the atmosphere, manipulating a deleterious impact both on the environment and human lives. Even if an increasing emphasis is being placed on dust storms due to the rapid change in global climate in the last fifty years by utilizing the measurements from the moderate-resolution imaging spectroradiometer (MODIS), the possibility of utilizing MODIS true-color composite images for the task has not been sufficiently discussed yet.

This data publication contains MODIS true-color dust images which are collected through an extensive visual inspection procedure to test the above hypothesis. This dataset includes a subset of the full dataset of RGB images each with visually-recognizable dust storm incidents in high latitude, temporally ranging from 2003 to 2019 over land as well as ocean throughout the world. All RGB images are manually annotated for dust storm detection using CVAT tool such that the dust-susceptible pixel area in the image is masked with (255, 255, 255) in RGB space (white) and the nonsusceptible pixel area is masked with (0, 0, 0) in RGB space (black).

Inspiration

• Could the MODIS true-colour satellite images be utilized for detecting dust storms with higher accuracy and segmentation capability?
• What is the role of accurate detection of boundaries in dust storm detection?
• Are machine learning models capable of building a tight correlation between nearby pixels to detect the presence of dust?
• Is it important to build an open dataset for dust storm detection using satellite true-colour images?

Content

This dataset contains 160 satellite true-colour images and their corresponding ground-truth label bitmaps, organized in two folders: images, and annotations. The associated notebook simply presents the image data visualization, statistical data augmentation and a U-Net-based model to detect dust storms in a semantic segment fashion.

Acknowledgements and Citation

The dataset of true-colour dust images, consisting of airborne dust and weaker dust traces, was collected using MODIS database from an extensive visual inspection procedure. The dataset can be used without additional permissions or fees.

If you use these data in a publication, presentation, or other research product please use the following citation:

N. Bandara, “Ensemble deep learning for automated dust storm detection using satellite images,” in 2022 International Research Conference on Smart Computing and Systems Engineering (SCSE), vol. 5. IEEE, 2022, pp. 178–183.

For interested researchers, please note that the paper is openly accessible at conference proceedings and/or here.

Research Ideas

• Would the latest state-of-the-art segmentation models increase the performance of detecting dust storms in satellite true-colour images?
• Few-shot learning for dust storm segmentation and related self-supervised learning techniques
• What is the role of ensemble learning in improving model performance?
• What are the optimum methods for data augmentation for increasing model performance?
• How can this dataset be combined with other datasets of satellite true-colour images for detecting dust storms?
• Methods to combine spatial and temporal information with respect to automated dust detection using satellite images and ground climate data

License

As described here,

``` You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. This license is acceptable for Free Cultural Works. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.