Deep learning methodologies for chemical dispersion map reconstruction

L. Martellucci; A. Puleio; N. Rutigliano; D. Di Giovanni; P. Gaudio

doi:10.1117/12.2685858

19 October 2023 Deep learning methodologies for chemical dispersion map reconstruction

L. Martellucci, A. Puleio, N. Rutigliano, D. Di Giovanni, P. Gaudio

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 127330G (2023) https://doi.org/10.1117/12.2685858
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Abstract

Environmental monitoring has been receiving increasing interest in recent years, both in research and in industries such as the military field. In the CBRNe event (Chemical, Biological, Radiological, Nuclear and Explosive), detection and monitoring of the target area are generally accomplished with manned devices. Physical exploration of the environment represents an unsafe situation whereas localization and mapping are time-consuming activity that involves some hazard level for the operator in the field. In case of accidental or deliberate release of chemical agents in the environment, the use of low-cost gas sensors developed in a network or mobile platform equipped with portable and reliable sensors provides the ability to acquire data on the event more quickly and safely with respect to manned devices. Localizing the source of a release and mapping its dispersion in the environment are crucial tasks for risk mitigation, even though they remain open problems. The rise of data processing techniques in the last few years such as Artificial Intelligence and Machine Learning methodologies gives the opportunity to develop promising solutions for environmental monitoring. In this work, we propose the application of Artificial Intelligence techniques for the chemical dispersion reconstruction for the data of a distributed sensor network by involving Deep Learning algorithms. The data was generated from a simulation of a gas dispersion in the environment and a reconstruction of the shape of the dispersion at the same resolution of the reference data has been obtained through a modified Deconvolution Neural Network.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

L. Martellucci, A. Puleio, N. Rutigliano, D. Di Giovanni, and P. Gaudio "Deep learning methodologies for chemical dispersion map reconstruction", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 127330G (19 October 2023); https://doi.org/10.1117/12.2685858

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