Recreating Lunar Environments by Fusion of Multimodal Data Using Machine Learning Models
The latest satellite infrastructure for data processing, transmission and reception can certainly be improved by upgrading tools used to deal with very large amounts of data from every different sensor incorporated within the space missions. In order to develop a better technique to process data, in...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/27/1/54 |
| _version_ | 1797611926355705856 |
|---|---|
| author | Ana C. Castillo Jesus A. Marroquin-Escobedo Santiago Gonzalez-Irigoyen Marlene Martinez-Santoyo Rafaela Villalpando-Hernandez Cesar Vargas-Rosales |
| author_facet | Ana C. Castillo Jesus A. Marroquin-Escobedo Santiago Gonzalez-Irigoyen Marlene Martinez-Santoyo Rafaela Villalpando-Hernandez Cesar Vargas-Rosales |
| author_sort | Ana C. Castillo |
| collection | DOAJ |
| description | The latest satellite infrastructure for data processing, transmission and reception can certainly be improved by upgrading tools used to deal with very large amounts of data from every different sensor incorporated within the space missions. In order to develop a better technique to process data, in this paper we will take an insight into multimodal data fusion using machine learning algorithms. This paper discusses how machine learning models are used to recreate environments from heterogeneous, multi-modal data sets. In particular, for those models based on neural networks, the most important difficulty is the vast number of training objects of the connected neural network based on Convolutional Neural Networks (CNN) to avoid overfitting and underfitting of the models. |
| first_indexed | 2024-03-11T06:35:31Z |
| format | Article |
| id | doaj.art-092f26f35f074d7581442d108bbd4164 |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-03-11T06:35:31Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-092f26f35f074d7581442d108bbd41642023-11-17T10:55:05ZengMDPI AGEngineering Proceedings2673-45912022-11-012715410.3390/ecsa-9-13326Recreating Lunar Environments by Fusion of Multimodal Data Using Machine Learning ModelsAna C. Castillo0Jesus A. Marroquin-Escobedo1Santiago Gonzalez-Irigoyen2Marlene Martinez-Santoyo3Rafaela Villalpando-Hernandez4Cesar Vargas-Rosales5School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnológico de Monterrey, Monterrey 64849, MexicoThe latest satellite infrastructure for data processing, transmission and reception can certainly be improved by upgrading tools used to deal with very large amounts of data from every different sensor incorporated within the space missions. In order to develop a better technique to process data, in this paper we will take an insight into multimodal data fusion using machine learning algorithms. This paper discusses how machine learning models are used to recreate environments from heterogeneous, multi-modal data sets. In particular, for those models based on neural networks, the most important difficulty is the vast number of training objects of the connected neural network based on Convolutional Neural Networks (CNN) to avoid overfitting and underfitting of the models.https://www.mdpi.com/2673-4591/27/1/54data fusionmultimodal datamachine learningsensor fusionlunar mission data |
| spellingShingle | Ana C. Castillo Jesus A. Marroquin-Escobedo Santiago Gonzalez-Irigoyen Marlene Martinez-Santoyo Rafaela Villalpando-Hernandez Cesar Vargas-Rosales Recreating Lunar Environments by Fusion of Multimodal Data Using Machine Learning Models Engineering Proceedings data fusion multimodal data machine learning sensor fusion lunar mission data |
| title | Recreating Lunar Environments by Fusion of Multimodal Data Using Machine Learning Models |
| title_full | Recreating Lunar Environments by Fusion of Multimodal Data Using Machine Learning Models |
| title_fullStr | Recreating Lunar Environments by Fusion of Multimodal Data Using Machine Learning Models |
| title_full_unstemmed | Recreating Lunar Environments by Fusion of Multimodal Data Using Machine Learning Models |
| title_short | Recreating Lunar Environments by Fusion of Multimodal Data Using Machine Learning Models |
| title_sort | recreating lunar environments by fusion of multimodal data using machine learning models |
| topic | data fusion multimodal data machine learning sensor fusion lunar mission data |
| url | https://www.mdpi.com/2673-4591/27/1/54 |
| work_keys_str_mv | AT anaccastillo recreatinglunarenvironmentsbyfusionofmultimodaldatausingmachinelearningmodels AT jesusamarroquinescobedo recreatinglunarenvironmentsbyfusionofmultimodaldatausingmachinelearningmodels AT santiagogonzalezirigoyen recreatinglunarenvironmentsbyfusionofmultimodaldatausingmachinelearningmodels AT marlenemartinezsantoyo recreatinglunarenvironmentsbyfusionofmultimodaldatausingmachinelearningmodels AT rafaelavillalpandohernandez recreatinglunarenvironmentsbyfusionofmultimodaldatausingmachinelearningmodels AT cesarvargasrosales recreatinglunarenvironmentsbyfusionofmultimodaldatausingmachinelearningmodels |