Needles in a Raystack: Ultra-Sparse LiDAR Occupancy Detection for Bat Tracks

The paper presents a lightweight 3D U‑Net designed to detect ultra‑sparse LiDAR occupancy of bat flight paths in nocturnal field recordings. By preserving temporal resolution and combining weighted binary cross‑entropy with Dice loss, the model overcomes the class imbalance that hampers standard reconstruction methods. Experiments on real LiDAR data, cross‑checked with acoustic monitoring, show that the U‑Net successfully recovers coherent occupancy patterns along bat trajectories, offering a practical foundation for large‑scale validation, clustering of flight tracks, and integration into biodiversity‑aware turbine curtailment strategies.

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