Multi-level Feature Extraction for Automated Land Cover Classification using Deep CNN with Long Short-Term Memory Network

Patel, Sanskruti; Ganatra, Nilay; Patel, Rachana

doi:10.1109/icoei53556.2022.9777148

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The PANCSLTN could address the issue of backdated decay mistakes through memory blocks and displays better predictive capability for time sequences, including long-time dependency. Patel et al [13] modelled a new network utilizing deep CNN with long short term memory (LSTM) that derives the features from satellite imageries for land cover classification. The CNN was utilized for deriving the features from these images, and the LSTM network was utilized to support the classification and sequence prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Ahmed¹,

Al-Zebari²,

Zebari³

et al. 2023

Computers, Materials &Amp; Continua

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Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. At the initial stage, the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder (CNN-AE) model for productive and accurate traffic flow. Next, the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization (BADSO) algorithm. The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

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Section: Literature Reviewmentioning

confidence: 99%

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Ahmed¹,

Al-Zebari²,

Zebari³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Leveraging pre-trained models from optical datasets, such as ImageNet [19], can facilitate training new RS models using smaller labeled datasets. Various approaches have leveraged pretrained models alongside EuroSAT and have demonstrated encouraging results [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning

Kunwar,

Ferdush

2024

RIG

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As the global population continues to expand, the demand for natural resources increases. Unfortunately, human activities account for 23% of greenhouse gas emissions. On a positive note, remote sensing technologies have emerged as a valuable tool in managing our environment. These technologies allow us to monitor land use, plan urban areas, and drive advancements in areas such as agriculture, climate change mitigation, disaster recovery, and environmental monitoring. Recent advances in Artificial Intelligence (AI), computer vision, and earth observation data have enabled unprecedented accuracy in land use mapping. By using transfer learning and fine-tuning with red-green-blue (RGB) bands, we achieved an impressive 99.19% accuracy in land use analysis. Such findings can be used to inform conservation and urban planning policies.

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Effective segmentation of land-use and land-cover from hyperspectral remote sensing image

Yele,

Alegavi,

Sedamkar

2024

Int. j. inf. tecnol.

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Multi-level Feature Extraction for Automated Land Cover Classification using Deep CNN with Long Short-Term Memory Network

Cited by 4 publications

References 13 publications

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Mapping of Land Use and Land Cover (LULC) Using EuroSAT and Transfer Learning

Effective segmentation of land-use and land-cover from hyperspectral remote sensing image

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