This paper investigates using neural networks to enable autonomous drone navigation in urban settings. The study's primary goals were to examine how neural networks might improve autonomous drone sensing, planning, and decision-making and to analyze new developments and potential future paths in this area. A thorough analysis of the body of research and secondary data sources—such as scholarly publications, conference proceedings, and internet databases—was conducted regarding methodology. The most important discoveries are the notable developments in object detection and scene understanding that neural networks permit in drone vision, as well as the adaptive planning and decision-making skills made possible by trajectory prediction models and reinforcement learning. Emerging technologies like multimodal sensor fusion and continuous learning are also highlighted in the paper, along with the policy implications for data privacy, accessibility, ethics, and safety. While highlighting the significance of addressing essential policy considerations for ethical and sustainable deployment, these findings highlight the transformative potential of neural networks for autonomous drone navigation in urban contexts.
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