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Ceilometer measurements dataset - MaRDI portal

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Ceilometer measurements dataset

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(Redirected from Dataset:6723929)



DOI10.5281/zenodo.10616434Zenodo10616434MaRDI QIDQ6723929

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 1 February 2024



This dataset contains images obtained by appropriate transformation of cloud detections from a ceilometer located in San Giovanni La Punta (Catania, Italy), from January 2023 to mid-March 2023. We used a Lufft CHM 15k ceilometer that leverages Light Detection and Ranging (LiDAR) technology. The ceilometer took measurements every 15 seconds quantifying the concentration of particles in the atmosphere. By taking advantage of the reflection, it is possible to determine the cloud layer coverage. Once raw data was collected, the variables of interest were scaled using a normalisation factor of the lidar-based ceilometer. All the variables are used to generate backscatter profiles. Specifically, we plot the time on the x-axis and the height of the measured particles (contained in the backscatter coefficient) on the y-axis. The colour of the plot depends on the intensity of the measured particle: intense blue means absence of particulate; red means intense presence of particulate. Numerically, the scale goes from the value 0 to the value of 5 106. We generated a backscatter profile for each day of measurements and each of these were further divided for every hour. In this way, we obtained 24 backscatter profiles for each day of observation. The file uploaded is a file with '. ZIP' extension. Once extracted, there are two folders: train and test. For each folder, there are two classes of images: true and false. We suggest using 'ImageFolder' to use the dataset (see reference: https://pytorch.org/vision/main/generated/torchvision.datasets.ImageFolder.html) For an example of using the dataset, please visit the following authors' repository:https://github.com/alessiochisari/CeilometerDatasetBenchmark The repository contains all the Jupyter Notebooks used by the authors to benchmark five state-of-the-art models on this new dataset. Please cite our work for any use of the dataset or code in this repository using this citation: @inproceedings{chisari2024, author = {Alessio Barbaro Chisari and others}, title = {On the Cloud Detection from Backscattered Images Generated From a Lidar-Based Ceilometer: Current State and Opportunities}, booktitle = {{IEEE} {ICIP} 2024, Abu Dhabi, UAE, October 27-30, 2024}, year = {2024} }






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