2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 1 , PP: 250-259, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
M. Prema Kumar 1 * , P. Chinnasamy 2 , B. Bala Abirami 3 , Juvvala Sailaja 4 , S. Bhuvana 5 , Sai Krishna Vunnam 6
Doi: https://doi.org/10.54216/JISIoT.180119
Advancements in Unmanned Aerial Vehicles (UAVs), popularly identified as drones, offer unprecedented opportunities to improve various applications of Extensive Internet of Things (IoT). In this framework, Deep Learning (DL) techniques are considered a practical alternative for improving the real-time obstacle detection and avoidance performance of fully autonomous UAVs. This research propose novel technique in urban environment climate change detection utilizing UAV image based on cloud IoT with deep learning model. Here the UAV images has been collected through cloud IoT module and prepared for dataset. This dataset with UAV images has been processed for filtering and contour reduction by normalization. Then processed image features are extracted utilizing graph cut fuzzy convolutional ResNet attention neural network with moath firefly sparrow colony optimization model. The simulation results has been analyzed for various UAV dataset in terms of training accuracy, average precision, recall, QoS, scalability. Proposed technique Average precision of 97%, QOS of 92%, SCALABILITY of 96%, training accuracy of 98%, RECALL of 95%.
Urban environment , Climate change detection , UAV image , Cloud IoT , Deep learning model
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