StrikeNet: A Deep Neural Network to Predict Pixel-Sized Lightning Location

Mélanie Bosc, Adrien Chan-Hon-Tong, Aurélie Bouchard, Dominique Béréziat

2025

Abstract

Forecasting the location of electrical activity at a very short time range remains one of the most challenging predictions to make, primarily attributable to the chaotic nature of thunderstorms. Additionally, the punctual nature of lightning further complicates the establishment of reliable forecasts. This article introduces StrikeNet, a specialized Convolutional Neural Network (CNN) model designed for very short-term forecasts of pixel-sized electrical activity locations, utilizing sequences of temporal images as input and only two data types. Employing soft Non-Maximum Suppression (NMS) techniques, incorporating morphological features within residual blocks, and implementing dropout regularization, StrikeNet is specifically designed for detecting and predicting pixel-sized objects in images. This design seamlessly aligns with the task of forecasting imminent electrical activity achieving F1 score about 0.53 for the positive class (lightning) and outperforms the state of the art. Moreover, it can be applied to similar datasets such as the Aerial Elephant Dataset (AED) where it outperforms traditional CNN models.

Paper Citation

in Harvard Style

Bosc M., Chan-Hon-Tong A., Bouchard A. and Béréziat D. (2025). StrikeNet: A Deep Neural Network to Predict Pixel-Sized Lightning Location. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 299-306. DOI: 10.5220/0013110700003912

in Bibtex Style

@conference{visapp25,
author={Mélanie Bosc and Adrien Chan-Hon-Tong and Aurélie Bouchard and Dominique Béréziat},
title={StrikeNet: A Deep Neural Network to Predict Pixel-Sized Lightning Location},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={299-306},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013110700003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - StrikeNet: A Deep Neural Network to Predict Pixel-Sized Lightning Location
SN - 978-989-758-728-3
AU - Bosc M.
AU - Chan-Hon-Tong A.
AU - Bouchard A.
AU - Béréziat D.
PY - 2025
SP - 299
EP - 306
DO - 10.5220/0013110700003912
PB - SciTePress