Climate Change Prediction in Urban Environment Using UAV Imaging Based on Cloud IoT and Deep Learning Techniques

 

 

 

M. Prema Kumar1,*, P. Chinnasamy2, B. Bala Abirami3, Juvvala Sailaja4, S. Bhuvana5, Sai Krishna Vunnam6

 

1Professor, Dept. of ECE, Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhra Pradesh, India

 

2Associate Professor, Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Srivilliputtur, India

 

3Assistant Professor, Department of Artificial intelligence and Data Science, Panimalar Engineering College, Chennai, India

 

4Assistant Professor, Department of Information Technology, Aditya University, surampalem, Andhra Andhra Pradesh, India

 

5Research Scholar, Department of Computer Science and Engineering, Dr.MGR Educational and Research, India

 

6Associate Professor, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India

 

Emails: medapatipremakumar@gmail.com; chinnasamyponnusamy@gmail.com; bala.bami@gmail.com; sailu.sailaja130@gmail.com; institute,bhupreethi@gmail.com; vskrishna@mlrit.ac.in

 

Text Box: Abstract 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%.

 

Received: February 18, 2025 Revised: June 02, 2025 Accepted: July 05, 2025

 

Keywords: Urban environment; Climate change detection; UAV image; Cloud IoT; Deep learning model