We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for three vegetation strata corresponding to lower, medium, and higher cover. Our weakly-supervised training scheme allows our network to only be supervised with vegetation occupancy values aggregated over cylindrical plots containing thousands of points. Such ground truth is easier to produce than pixel-wise or point-wise annotations. Our method outperforms handcrafted and deep learning baselines in terms of precision by up to 30%, while simultaneously providing visual and interpretable predictions. We provide an open-source implementation along with a dataset of 199 agricultural plots to train and evaluate weakly supervised occupancy regression algorithms.
Computer Vision Lab, ETH Zurich（苏黎世联邦理工学院计算机视觉实验室）
Airborne acquisition and on-road mobile mapping provide complementary 3D information of an urban landscape: the former acquires roof structures, ground, and vegetation at a large scale, but lacks the facade and street-side details, while the latter is incomplete for higher floors and often totally misses out on pedestrian-only areas or undriven districts. In this work, we introduce an approach that efficiently unifies a detailed street-side Structure-from-Motion (SfM) or Multi-View Stereo (MVS) point cloud and a coarser but more complete point cloud from airborne acquisition in a joint surface…
Université Gustave Eiffel, IGN-ENSG, LASTIG（古斯塔夫·埃菲尔大学，法国国家地理与林业信息研究所-国家地理科学学院，地理信息与空间分析实验室）；INRAE, UMR 1202 BIOGECO, Université de Bordeaux（法国国家农业食品与环境研究院，BIOGECO联合研究单位，波尔多大学）；EA G&E Bordeaux INP, Université Bordeaux Montaigne（波尔多综合理工学院G&E研究组，波尔多蒙田大学）
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from 3D point clouds captured from an aerial platform. Our model predicts rasterized occupancy maps for three vegetation strata: lower, medium, and higher strata. Our training scheme allows our network to only being supervized with values aggregated over cylindrical plots, which are easier to produce than pixel-wise or point-wise annotations. Our method outperforms handcrafted and deep learning baselines in terms of precision while simultaneously providing visual and interpretable predictions. We prov…
This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the approach collects crown information such as steepness and height on-the-fly to delineate crown boundaries, and most importantly, does not require a priori assumptions of crown shape and size. The approach segments trees iteratively starting from the tallest within a given area to the smallest until all trees have been segmented. To evaluate its performance, the approach was applied to the University of Kentucky Robinson Forest, a deciduous closed…
Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in natural deciduous forests however remained a challenge because of the complex and multi-layered canopy. In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density o…
Geosciences Rennes, Université Rennes 1, CNRS（法国国家科学研究中心雷恩第一大学雷恩地球科学研究所）；University of Canterbury（坎特伯雷大学）
3D point clouds of natural environments relevant to problems in geomorphology often require classification of the data into elementary relevant classes. A typical example is the separation of riparian vegetation from ground in fluvial environments, the distinction between fresh surfaces and rockfall in cliff environments, or more generally the classification of surfaces according to their morphology. Natural surfaces are heterogeneous and their distinctive properties are seldom defined at a unique scale, prompting the use of multi-scale criteria to achieve a high degree of classification succe…
Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlappi…
University of Copenhagen（哥本哈根大学）；University of Münster（明斯特大学）
Quantification of forest biomass stocks and their dynamics is important for implementing effective climate change mitigation measures. The knowledge is needed, e.g., for local forest management, studying the processes driving af-, re-, and deforestation, and can improve the accuracy of carbon-accounting. Remote sensing using airborne LiDAR can be used to perform these measurements of vegetation structure at large scale. We present deep learning systems for predicting wood volume, above-ground biomass (AGB), and subsequently above-ground carbon stocks directly from airborne LiDAR point clouds. …
LiDAR data acquisition and processing for ecology applications
INESC-ID Lisboa（INESC-ID里斯本）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）；Centre for Ecology, Evolution and Environmental Changes CHANGE - Institute for Global Change and Sustainability（生态、进化与环境变化中心——全球变化与可持续性研究所）；Faculdade de Ciências（科学学院）
Unsupervised semantic segmentation of urban high-density multispectral point clouds
Finnish Geospatial Research Institute FGI, the National Land Survey of Finland（芬兰地理空间研究所（FGI，芬兰国家土地测量局））；Department of Built Environment（建筑环境系）；School of Engineering（工程学院）；Aalto University（阿尔托大学）
本文提出无监督地面感知深度聚类方法 GroupSP，通过超点分组与伪标签迭代训练，实现高密度多光谱 ALS 城市点云的七类语义分割，并以极少标注取得高精度。
Benchmarking Deep Learning Models for Aerial LiDAR Point Cloud Semantic Segmentation under Real Acquisition Conditions: A Case Study in Navarre
Department of Statistics, Computer Science and Mathematics and Institute of Smart Cities (ISC), Public University of Navarre (UPNA)（统计、计算机科学与数学系和智能城市研究所（ISC），纳瓦拉公共大学（UPNA））；Tracasa Instrumental
York University（约克大学）；Lassonde School of Engineering（拉森德工程学院）
Due to the aged nature of much of the utility network infrastructure, developing a robust and trustworthy computer vision system capable of inspecting it with minimal human intervention has attracted considerable research attention. The airborne laser terrain mapping (ALTM) system quickly becomes the central data collection system among the numerous available sensors. Its ability to penetrate foliage with high-powered energy provides wide coverage and achieves survey-grade ranging accuracy. However, the post-data acquisition process for classifying the ALTM's dense and irregular point clouds i…
Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era
École française d’Extrême-Orient (EFEO)（法国远东学院）；LIGM, École des Ponts, CNRS, UGE（巴黎路桥学校信息与建模实验室、法国国家科学研究中心、巴黎东部大学）；Université Paris 1 Panthéon-Sorbonne（巴黎第一大学先贤祠-索邦大学）
Multispectral LiDAR data for extracting tree points in urban and suburban areas
Department of Geodesy and Geoinformation, TU Wien（大地测量与地理信息系，维也纳技术大学）；D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK)（三维光学计量（3DOM）单元，布鲁诺·凯斯勒基金会）；Department of Photogrammetry and Remote Sensing, Finnish Geospatial Research Institute, National Land Survey of Finland（摄影测量与遥感系，芬兰地理研究 institute，芬兰国家土地测绘局）
