Detailed forest inventories are critical for sustainable and flexible
management of forest resources, to conserve various ecosystem services. Modern
airborne laser scanners deliver high-density point clouds with great potential
for fine-scale forest inventory and analysis, but automatically partitioning
those point clouds into meaningful entities like individual trees or tree
components remains a challenge. The present study aims to fill this gap and
introduces a deep learning framework that is able to perform such a
segmentation across diverse forest types and geographic regions. From the
segmented data, we then derive relevant biophysical parameters of individual
trees as well as stands. The system has been tested on FOR-Instance, a dataset
of point clouds that have been acquired in five different countries using
surveying drones. The segmentation back-end achieves over 85% F-score for
individual trees, respectively over 73% mean IoU across five semantic
categories: ground, low vegetation, stems, live branches and dead branches.
Building on the segmentation results our pipeline then densely calculates
biophysical features of each individual tree (height, crown diameter, crown
volume, DBH, and location) and properties per stand (digital terrain model and
stand density). Especially crown-related features are in most cases retrieved
with high accuracy, whereas the estimates for DBH and location are less
reliable, due to the airborne scanning setup.

通过介绍一种深度学习框架，本研究旨在自动地将高密度点云分割为有意义的树木实体，以及树组件，并从分割数据中获得相关的生物物理参数。通过测试，该系统在个体树木和五个语义类别的分割上取得了较高的准确性，在树冠相关特征方面表现尤其突出，而对于树直径和位置的估计相对不太可靠，这是由于航空扫描设置的原因。

自动森林清查: 3D 深度学习对高密度航空 LiDAR 点云的分析

Automated forest inventory: analysis of high-density airborne LiDAR  point clouds with 3D deep learning

In this paper we present BioFaceNet, a deep CNN that learns to decompose a
single face image into biophysical parameters maps, diffuse and specular
shading maps as well as estimating the spectral power distribution of the scene
illuminant and the spectral sensitivity of the camera. The network comprises a
fully convolutional encoder for estimating the spatial maps with a fully
connected branch for estimating the vector quantities. The network is trained
using a self-supervised appearance loss computed via a model-based decoder. The
task is highly underconstrained so we impose a number of model-based priors.
Skin spectral reflectance is restricted to a biophysical model, we impose a
statistical prior on camera spectral sensitivities, a physical constraint on
illumination spectra, a sparsity prior on specular reflections and direct
supervision on diffuse shading using a rough shape proxy. We show convincing
qualitative results on in-the-wild data and introduce a benchmark for
quantitative evaluation on this new task.

本文介绍了一种名为 BioFaceNet 的深度 CNN，它能学习将单一的人脸图像分解成生物物理参数地图、漫反射和镜面遮蔽地图，并估计场景照明光谱功率分布和相机光谱灵敏度。