SE-EDSR: A Deep Learning Method for Gas Distribution Mapping

Zhang, Yajie; Meng, Qing-Hao; Jing, Tao; Li, Yunkai; Song, Tianshu; Zhen-yu, Deng

doi:10.23919/ccc55666.2022.9901638

Cited by 2 publications

(4 citation statements)

References 18 publications

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“…In Gas Distribution Mapping the target is to reconstruct a dense representation of the distribution usually from a limited set of sampling points, that could be considered sparse sampling data. From this point of view, as proposed in Zhang et al 73 , GDM can be considered similar to the SISR problems where a low-resolution matrix describes the input, and the final output is to reconstruct the high-resolution image. By using the learning approach, a dense representation is constructed by considering the spatial correlation of the element in the sparse matrix by introducing deep network models.…”

Section: Methodsmentioning

confidence: 99%

“…In literature, model-based and statistical-based approaches have been widely explored and utilized for various applications, including environmental monitoring, gas distribution mapping, and hazard assessment. These approaches leverage mathematical models or statistical methods to analyse and interpret complex datasets, providing valuable insights into the behaviour and characteristics of environmental phenomena [73][74][75] . An introduction of different methodologies for dispersion mapping is provided in Reggente 75 .…”

Section: Introductionmentioning

confidence: 99%

“…For example, the model-based methods rely on the prior gas dispersion model, but the turbulent dispersion model cannot be directly used for parameter estimation due to its complexity 76 . The statistical-based methods at the same time suffer from hand-design parameters, such as in the KDM where such parameters have a significant impact on the mapping results 38,73,77 .…”

Section: Introductionmentioning

confidence: 99%

“…To overcome the dependence of the model-based and statistical-based methods on predefined parameters that influence reconstruction performances, a new approach based on deep learning has been proposed in the paper as the initial study of the applicability of such methodologies. The first attempt to introduce such methodology in the field of GDM was proposed by Zhang et al 73 , starting from the similarity between the process of building dense information from a sparse sampling of GDM and Single Image Super-Resolution (SISR). In general, SISR targets to recover high-resolution images from lowresolution images by using intrinsic relationships between pixels.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning methodologies for chemical dispersion map reconstruction

Martellucci,

Puleio,

Rutigliano

et al. 2023

Image and Signal Processing for Remote Sensing XXIX

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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.

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Section: Methodsmentioning

confidence: 99%