Intelligent Identification of Continuously Illuminated Regions at Lunar South Pole Based on Deep Learning

Taking the connecting ridge between Shackleton and de Gerlache craters as the research area, based on real-time illumination simulation data from November 1, 2026, to February 28, 2027, a dynamic illumination dataset with a spatial resolution of 20 m/pixel and a temporal resolution of 1 hour was constructed. A deep-learning framework is proposed to recognize regions with continuous 3-day illumination, in which an improved VGG network extracts illumination-friendly regions from each temporal frame, a bidirectional GRU network captures temporal illumination characteristics, and a consistent temporal-spatial attention mechanism highlights key spatiotemporal illumination features. An output head network integrates these features to generate target regions. Based on the extracted regions and an eight-direction rover mobility model, a Sun-synchronous A* path planning algorithm is further optimized to enable illumination-aware navigation. Simulation results demonstrate that the proposed method accurately recognizes 3-day consecutive illumination-friendly regions in the 20 m/pixel dynamic dataset and effectively supports efficient rover path planning in well-illuminated areas of the lunar south pole.