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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Aerospace Engineering Journal of Geosciences and Environmental Studies
Sapkota, Sanjog Chhetri
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Aerospace Engineering Astronomy Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Journal : Aerospace Engineering

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Deep Learning-Based Classification of Remote Sensing Images: Challenges, Techniques, and Future Directions in Global Sustainability Paneru, Biplov; Paneru, Bishwash; Sapkota, Sanjog Chhetri
Aerospace Engineering Vol. 1 No. 3 (2024): July
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i3.2772

Abstract

With its high accuracy and efficiency, deep learning has greatly improved the classification of remote sensing (RS) photos. In order to categorize RS photos, this research analyzes the effectiveness of three cutting-edge deep learning models: ResNet-50, EfficientNetB2, and MobileNetV2. The models' accuracy on training and validation data were noted after they were trained and assessed on a dataset containing a variety of situations. Our findings illustrate each model's advantages and disadvantages and shed light on how well suited each is for various RS image categorization applications. The ResNet-50 model performed well in our study, achieving 74.41% training accuracy and 75.00% validation accuracy. With a training accuracy of 74.66% and a higher validation accuracy of 80.33%, the EfficientNetB2 model performed marginally better, demonstrating its strong generalization capabilities. On the other hand, the MobileNetV2 model had severe overfitting, as evidenced by its validation accuracy of 22.79%, which was much lower than its extraordinary high training accuracy of 99.21%. In order to achieve balanced performance between training and validation datasets in remote sensing image classification tasks, these results emphasize the significance of model architecture and regularization strategies. The proposed model can be utilized for sustainable remote sensing based applications in global water, environment and air health.
Water Sustainability Enhancement with UAV and AIoT: An Integrated Technology for Water Quality and Flood Hazard Monitoring using the Internet of Drones Paneru, Biplov; Paneru, Bishwash; Sapkota, Sanjog Chhetri; Shah, Krishna Bikram; Poudel, Yam Krishna
Aerospace Engineering Vol. 1 No. 4 (2024): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i4.2773

Abstract

Globally, there are challenges in minimizing the effects of water pollution and global warming everywhere in the world. In order to map the flood conditions, we want to apply a sensor network connected to a Esp32 and Tensorflow lite integrated system for drone-based water surface waste collection. Finally, a GSM sim 800L Module incorporated is used to send notifications to the user about the monitored conditions, such as trash level and other data. An ultrasonic sensor is utilized to detect the water level. The outcome shows that there is a high chance of tracking water levels and monitoring floods. By using this innovative technology, users can receive warnings and be warned remotely. The Inception-v3 model on clean and unclean water images obtained 97% accuracy on testing USING Inception-v3 and using the proposed circuit diagram a prototype is developed for possible deployment in such water resource region for possible operation and application is presented in the paper.
Co-Authors Paneru, Biplov Paneru, Bishwash Poudel, Yam Krishna Shah, Krishna Bikram