Multimodal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds

TL;DR

This paper explores deep learning fusion of 2D orthophotos and 3D ALS point clouds to reliably assess forest biodiversity potential, demonstrating improved accuracy through multimodal data integration.

Contribution

It introduces the BioVista dataset and compares various deep learning fusion methods, achieving high accuracy in biodiversity assessment from multimodal forest data.

Findings

Orthophotos achieve 76.7% accuracy

ALS point clouds achieve 75.8% accuracy

End-to-end fusion reaches 82.0% accuracy in separating forest potential levels

Abstract

Assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos and 3D airborne laser scanning (ALS) point clouds can reliable assess the biodiversity potential of forests. We introduce the BioVista dataset, comprising 44378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multimodal fusion approaches. Using deep neural networks (ResNet for orthophotos and PointVector for ALS point clouds), we investigate each data modality's ability to assess forest biodiversity potential, achieving overall accuracies of 76.7% and 75.8%, respectively. We explore various 2D and 3D fusion approaches: confidence-based ensembling, feature-level concatenation, and end-to-end training, with the latter achieving an overall accuracies of 82.0% when separating low- and high potential forest areas. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in the assessment of forest biodiversity potential.