让机器人提前猜出未知区域的结构和功能，提升导航效率。

In target-driven navigation and autonomous exploration, reasonable prediction of unknown regions is crucial for efficient navigation and environment understanding. Existing methods mostly focus on single objects or geometric occupancy maps, lacking the ability to model room-level semantic structures. We propose SemSight, a probabilistic bird's-eye-view prediction model for multi-level scene semantics. The model jointly infers structural layouts, global scene context, and target area distributions, completing semantic maps of unexplored areas while estimating probability maps for target categories. To train SemSight, we simulate frontier-driven exploration on 2,000 indoor layout graphs, constructing a diverse dataset of 40,000 sequential egocentric observations paired with complete semantic maps. We adopt an encoder-decoder network as the core architecture and introduce a mask-constrained supervision strategy. This strategy applies a binary mask of unexplored areas so that supervision focuses only on unknown regions, forcing the model to infer semantic structures from the observed context. Experimental results show that SemSight improves prediction performance for key functional categories in unexplored regions and outperforms non-mask-supervised approaches on metrics such as Structural Consistency (SC) and Region Recognition Accuracy (PA). It also enhances navigation efficiency in closed-loop simulations, reducing the number of search steps when guiding robots toward target areas.