Enhancing Autonomous Drone Semantic Segmentation for Safe Landing

Ensuring safe and accurate drone landings is a critical challenge in the development of autonomous drone systems. This study presents a two-fold approach combining granular semantic segmentation with high-level binary safety classification of aerial imagery. Our method first performs detailed segmentation of environmental elements, then categorizes these segments into "safe to land" and "unsafe to land" zones, providing a more comprehensive and practical solution for autonomous landing decisions. We employed three state-of-the-art deep learning algorithms to perform semantic segmentation on a newly-released dataset of aerial footage from residential neighborhoods. Our models achieved a multiclass Mean Intersection over Union (mIoU) score of 0.82 for detailed segmentation and a binary mIoU score of 0.90 for the critical safe/unsafe landing classification. Among the models tested, U-Net with MobileNetV3 encoder stood out by its better generalization performance, faster training times, and a more compact model size. This study highlights the potential for real-world drone applications, emphasizing its suitability for ensuring safe landings through enhanced environmental mapping and classification.