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

2026-09-17

AI Summary

研究针对夜间蝙蝠监测难题，提出一种轻量级3D U-Net模型，结合特定损失函数处理稀疏LiDAR数据中的前景检测问题。

Abstract

Monitoring flying animals is important for understanding and protecting biodiversity, but nocturnal species such as bats are difficult to observe in the field. Using LiDAR, bat movements at night result in ultra-sparse 3D spatio-temporal data in which standard reconstruction losses tend to predict only background and miss real flight paths. We study this problem as voxel-wise occupancy detection in sensor-centric LiDAR raystacks. A lightweight 3D U-Net is proposed that preserves temporal resolution, uses skip connections for spatial detail, and combines weighted binary cross-entropy with Dice loss to handle the strong class imbalance. In real LiDAR recordings of bats over open fields, cross-checked with acoustic monitoring, a reconstruction-based 3D convolutional autoencoder baseline fails to recover foreground trajectories. In contrast, the proposed U-Net recovers sparse foreground occupancy in diagnostic experiments and produces coherent occupancy patterns along bat flight trajectories, providing a practical basis for validation-scale experiments, later clustering of flight tracks, and future integration of bat activity information into biodiversity-aware turbine curtailment strategies.

Problem

• LiDAR
• occupancy detection
• ultra-sparse data
• bat monitoring

Innovation

• Ultra-Sparse LiDAR
• 3D U-Net
• Occupancy Detection
• Weighted Binary Cross-Entropy
• Dice Loss