The paper "Efficient CNN-based disaster events classification using UAV-aided images for emergency response application" addresses the challenge of rapid and accurate disaster classification in remote and inaccessible areas. The authors propose a deep learning model that leverages feature concatenation to classify natural disasters using images captured by UAVs. The model is designed to be efficient, accurate, and computationally affordable, aiming to improve the response time and effectiveness of disaster management teams. The paper highlights the use of UAVs for monitoring and surveillance in disaster-stricken areas, where traditional methods often fall short due to poor connectivity and physical access. The proposed model is evaluated on a dataset and compared with existing CNN models, demonstrating superior performance in terms of accuracy and precision. The research also discusses related works, including the use of transfer learning, semantic segmentation, and object detection techniques, and provides a detailed experimental analysis and comparative results.The paper "Efficient CNN-based disaster events classification using UAV-aided images for emergency response application" addresses the challenge of rapid and accurate disaster classification in remote and inaccessible areas. The authors propose a deep learning model that leverages feature concatenation to classify natural disasters using images captured by UAVs. The model is designed to be efficient, accurate, and computationally affordable, aiming to improve the response time and effectiveness of disaster management teams. The paper highlights the use of UAVs for monitoring and surveillance in disaster-stricken areas, where traditional methods often fall short due to poor connectivity and physical access. The proposed model is evaluated on a dataset and compared with existing CNN models, demonstrating superior performance in terms of accuracy and precision. The research also discusses related works, including the use of transfer learning, semantic segmentation, and object detection techniques, and provides a detailed experimental analysis and comparative results.