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3D Deep Network Predicts Thickness of Forest Layers

example of 3D Deep Network Models Layers
3D Deep Network Models Layers

The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m2 with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep network architecture predicting for the first time both 3D point-wise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://github.com/ekalinicheva/multi_layer_vegetation.

From a paper by Kalinicheva et al.

1. Introduction

The retrieval and analysis of the multiple layers of dense vegetation is an essential step for many forestry and ecology applications, such as forest management [20], biodiversity and habitat analysis [14, 31], biomass estimation [11, 29], or forest fire modeling [30, 32, 35]. Thanks to the constant advances in remote sensing technology, we can now gather large amounts of precise geometric and radiometric data on vast forested environments. However, standard satellite or aerial images are only suitable for the analysis of the top canopy layer [37]. Conversely, terrestrial laser scanning (TLS) captures detailed information on the bottom layers with slightly decreasing point density toward the upper vegetation layers. Unfortunately, TLS data acquisition over large areas (> 1 ha) is unpractical due to the high amount of resource involved [7]. In contrast, Airborne Laser Scanners (ALS), and especially Unmanned Aerial Vehicles equipped with Laser Scanners (UAV-LS), can capture 3D point clouds over larger areas with sufficient point density [6, 7, 27].

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