The procedure of categorizing images from remote sensing is also another application of machine learning not just ground-based platforms (for instance satellites), aerial platforms become platforms sometimes in aviation either. They erase the counterparts that were based on individual categories and are portrayed on a specific part of the image. Geospatial Supply of gravel mainly is used for producing railway track, road and concrete surface. Data by analyzing their buildup, dams, bridges, extraordinary open spaces, reservoirs and canals. It targets to be specific and exact as possible in a different specific area of the land. Aspects of the enlarged portrait or distinctions weaved into the completed arts. This might have aspects such as mapping of the trees, plants, rivers, cities, farms and woodlands, and other items. Geospatial image classification is necessary for the identification and real-time analysis of different hazards and unrests. Provide numerous applications, including waste management, water resources, air quality, and traffic control in the urban contexts. Planning, monitoring the environment, land cover, mapping, as well as post-disaster recovery. Management team, traffic control, and situation assessments. In the past, human experts situated in a selected area classified geographical images by means of manual processing. One that involved the allocation of too much time. As this is one of the two broad categories, how to get rid of it is consequently. Applying machine learning and deep learning methods we analyze and interpret the data in order to reduce the time required to provide feedback which allows the system to reach a higher accuracy. The procedure will also be more reliable and the outcome will hopefully be more efficient CNNs are one of the deep learning subclasses in which the network learns and improves without the need for human intervention. It extracts features from images. They are main for the performance and metrics to help the organization to decide on whether they have accomplished their goals, using visual imagery.
| Published in | American Journal of Science, Engineering and Technology (Volume 9, Issue 3) |
| DOI | 10.11648/j.ajset.20240903.11 |
| Page(s) | 150-162 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
CNN, Machine Learning, Deep Learning, ResNet50, EfficientNetv2
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APA Style
Bobba, S. (2024). Leveraging Pre-trained Deep Learning Models for Remote Sensing Image Classification: A Case Study with ResNet50 and EfficientNet. American Journal of Science, Engineering and Technology, 9(3), 150-162. https://doi.org/10.11648/j.ajset.20240903.11
ACS Style
Bobba, S. Leveraging Pre-trained Deep Learning Models for Remote Sensing Image Classification: A Case Study with ResNet50 and EfficientNet. Am. J. Sci. Eng. Technol. 2024, 9(3), 150-162. doi: 10.11648/j.ajset.20240903.11
AMA Style
Bobba S. Leveraging Pre-trained Deep Learning Models for Remote Sensing Image Classification: A Case Study with ResNet50 and EfficientNet. Am J Sci Eng Technol. 2024;9(3):150-162. doi: 10.11648/j.ajset.20240903.11
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Download@article{10.11648/j.ajset.20240903.11,
author = {Srivani Bobba},
title = {Leveraging Pre-trained Deep Learning Models for Remote Sensing Image Classification: A Case Study with ResNet50 and EfficientNet
},
journal = {American Journal of Science, Engineering and Technology},
volume = {9},
number = {3},
pages = {150-162},
doi = {10.11648/j.ajset.20240903.11},
url = {https://doi.org/10.11648/j.ajset.20240903.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajset.20240903.11},
abstract = {The procedure of categorizing images from remote sensing is also another application of machine learning not just ground-based platforms (for instance satellites), aerial platforms become platforms sometimes in aviation either. They erase the counterparts that were based on individual categories and are portrayed on a specific part of the image. Geospatial Supply of gravel mainly is used for producing railway track, road and concrete surface. Data by analyzing their buildup, dams, bridges, extraordinary open spaces, reservoirs and canals. It targets to be specific and exact as possible in a different specific area of the land. Aspects of the enlarged portrait or distinctions weaved into the completed arts. This might have aspects such as mapping of the trees, plants, rivers, cities, farms and woodlands, and other items. Geospatial image classification is necessary for the identification and real-time analysis of different hazards and unrests. Provide numerous applications, including waste management, water resources, air quality, and traffic control in the urban contexts. Planning, monitoring the environment, land cover, mapping, as well as post-disaster recovery. Management team, traffic control, and situation assessments. In the past, human experts situated in a selected area classified geographical images by means of manual processing. One that involved the allocation of too much time. As this is one of the two broad categories, how to get rid of it is consequently. Applying machine learning and deep learning methods we analyze and interpret the data in order to reduce the time required to provide feedback which allows the system to reach a higher accuracy. The procedure will also be more reliable and the outcome will hopefully be more efficient CNNs are one of the deep learning subclasses in which the network learns and improves without the need for human intervention. It extracts features from images. They are main for the performance and metrics to help the organization to decide on whether they have accomplished their goals, using visual imagery.
},
year = {2024}
}
TY - JOUR T1 - Leveraging Pre-trained Deep Learning Models for Remote Sensing Image Classification: A Case Study with ResNet50 and EfficientNet AU - Srivani Bobba Y1 - 2024/08/15 PY - 2024 N1 - https://doi.org/10.11648/j.ajset.20240903.11 DO - 10.11648/j.ajset.20240903.11 T2 - American Journal of Science, Engineering and Technology JF - American Journal of Science, Engineering and Technology JO - American Journal of Science, Engineering and Technology SP - 150 EP - 162 PB - Science Publishing Group SN - 2578-8353 UR - https://doi.org/10.11648/j.ajset.20240903.11 AB - The procedure of categorizing images from remote sensing is also another application of machine learning not just ground-based platforms (for instance satellites), aerial platforms become platforms sometimes in aviation either. They erase the counterparts that were based on individual categories and are portrayed on a specific part of the image. Geospatial Supply of gravel mainly is used for producing railway track, road and concrete surface. Data by analyzing their buildup, dams, bridges, extraordinary open spaces, reservoirs and canals. It targets to be specific and exact as possible in a different specific area of the land. Aspects of the enlarged portrait or distinctions weaved into the completed arts. This might have aspects such as mapping of the trees, plants, rivers, cities, farms and woodlands, and other items. Geospatial image classification is necessary for the identification and real-time analysis of different hazards and unrests. Provide numerous applications, including waste management, water resources, air quality, and traffic control in the urban contexts. Planning, monitoring the environment, land cover, mapping, as well as post-disaster recovery. Management team, traffic control, and situation assessments. In the past, human experts situated in a selected area classified geographical images by means of manual processing. One that involved the allocation of too much time. As this is one of the two broad categories, how to get rid of it is consequently. Applying machine learning and deep learning methods we analyze and interpret the data in order to reduce the time required to provide feedback which allows the system to reach a higher accuracy. The procedure will also be more reliable and the outcome will hopefully be more efficient CNNs are one of the deep learning subclasses in which the network learns and improves without the need for human intervention. It extracts features from images. They are main for the performance and metrics to help the organization to decide on whether they have accomplished their goals, using visual imagery. VL - 9 IS - 3 ER -
Department of Information Technology, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India
