Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)

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Creation of a Spatially Validated Satellite Dataset for Cumulonimbus Cloud Detection

Authors
Yenniwarti Rafsyam1, *, Shita Fitria Nurjihan1, Arief Rinaldi1, Anik Tjandra Setiati1, Mary Alysia Suryani1
1Electrical Engineering Department, Politeknik Negeri Jakarta, Depok, Indonesia
*Corresponding author. Email: yenniwarti.rafsyam@elektro.pnj.ac.id
Corresponding Author
Yenniwarti Rafsyam
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-926-1_31How to use a DOI?
Keywords
Aviation Safety; Cloud Detection; Cumulonimbus Clouds; NOAA; YOLOv8
Abstract

Accurate detection of cumulonimbus (CB) clouds is critical for supporting weather monitoring and aviation safety. In this research, a spatially validated dataset was developed using satellite imagery from NOAA-18 and NOAA-19, focusing on the classification of CB and nonCB clouds. Cloud objects were annotated using the LabelImg tool, with each instance labeled in YOLO format and organized into independent training and validation subsets. The YOLOv8 model from the Ultralytics library was then applied, using a pre-trained base model and trained over 200 epochs with an image resolution of 640 pixels. The model achieved strong performance on the validation dataset, with a precision of 0.79, recall of 0.84, and F1-score of 0.81. The mean average precision (mAP) reached 0.917 at IoU 0.5 and 0.655 across the 0.5–0.95 IoU range, indicating robust detection and localization capabilities. These results highlight the effectiveness of combining structured annotation and deep learning techniques for satellite-based cloud detection. The proposed approach offers potential for practical implementation in automated meteorological analysis and early warning systems for flight safety.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)
Series
Advances in Engineering Research
Publication Date
31 December 2025
ISBN
978-94-6463-926-1
ISSN
2352-5401
DOI
10.2991/978-94-6463-926-1_31How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yenniwarti Rafsyam
AU  - Shita Fitria Nurjihan
AU  - Arief Rinaldi
AU  - Anik Tjandra Setiati
AU  - Mary Alysia Suryani
PY  - 2025
DA  - 2025/12/31
TI  - Creation of a Spatially Validated Satellite Dataset for Cumulonimbus Cloud Detection
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2025 (iCAST-ES 2025)
PB  - Atlantis Press
SP  - 266
EP  - 276
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-926-1_31
DO  - 10.2991/978-94-6463-926-1_31
ID  - Rafsyam2025
ER  -