Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3949 2019 2019 Professor, Dept. of ECE, Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhra Pradesh, India M. M. Associate Professor, Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Srivilliputtur, India P. Chinnasamy Assistant Professor, Department of Artificial intelligence and Data Science, Panimalar Engineering College, Chennai, India B. Bala Abirami Assistant Professor, Department of Information Technology, Aditya University, surampalem, Andhra Andhra Pradesh, India Juvvala Sailaja Research Scholar, Department of Computer Science and Engineering, Dr.MGR Educational and Research, India S. Bhuvana Associate Professor, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India Sai Krishna Vunnam 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%. 2026 2026 250 259 10.54216/JISIoT.180119 https://www.americaspg.com/articleinfo/18/show/3949