From Forest to Urban: Data Efficient Tree Segmentation with Self-Supervised Pretraining on Height-Based Voronoi Maps
Published in , 2025
Abstract:
We present a data-efficient approach for tree segmentation that leverages self-supervised pretraining on height-based Voronoi maps. Our method significantly improves performance across diverse environments—from forested regions to dense urban settings—while reducing reliance on large annotated datasets.