SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

• URL: http://arxiv.org/abs/2401.15739v1
• Date: Sun, 28 Jan 2024 19:47:17 GMT
• Title: SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data
• Authors: Maciej Wielgosz, Stefano Puliti, Binbin Xiang, Konrad Schindler, Rasmus Astrup
• Abstract summary: This research advances individual tree crown (ITC) segmentation in lidar data, using a deep learning model applicable to various laser scanning types. It addresses the challenge of transferability across different data characteristics in 3D forest scene analysis. The model, based on PointGroup architecture, is a 3D CNN with separate heads for semantic and instance segmentation.
• Score: 15.438892555484616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research advances individual tree crown (ITC) segmentation in lidar data, using a deep learning model applicable to various laser scanning types: airborne (ULS), terrestrial (TLS), and mobile (MLS). It addresses the challenge of transferability across different data characteristics in 3D forest scene analysis. The study evaluates the model's performance based on platform (ULS, MLS) and data density, testing five scenarios with varying input data, including sparse versions, to gauge adaptability and canopy layer efficacy. The model, based on PointGroup architecture, is a 3D CNN with separate heads for semantic and instance segmentation, validated on diverse point cloud datasets. Results show point cloud sparsification enhances performance, aiding sparse data handling and improving detection in dense forests. The model performs well with >50 points per sq. m densities but less so at 10 points per sq. m due to higher omission rates. It outperforms existing methods (e.g., Point2Tree, TLS2trees) in detection, omission, commission rates, and F1 score, setting new benchmarks on LAUTx, Wytham Woods, and TreeLearn datasets. In conclusion, this study shows the feasibility of a sensor-agnostic model for diverse lidar data, surpassing sensor-specific approaches and setting new standards in tree segmentation, particularly in complex forests. This contributes to future ecological modeling and forest management advancements.

Related papers

• Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation [6.635604919499181]
  We introduce a new large aerial dataset for forest inspection. It contains both real-world and virtual recordings of natural environments. We develop a framework to assess the deforestation degree of an area.
  arXiv  Detail & Related papers  (2024-03-11T11:26:44Z)
• Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning [16.071397465972893]
  ForAINet is able to perform a segmentation across diverse forest types and geographic regions. System has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones.
  arXiv  Detail & Related papers  (2023-12-22T21:54:35Z)
• Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation [67.07112533415116]
  We present a novel framework that adapts various foundational models for the 3D point cloud segmentation task. Our approach involves making initial predictions of 2D semantic masks using different large vision models. To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting.
  arXiv  Detail & Related papers  (2023-11-03T15:41:15Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees [0.06597195879147556]
  FOR-instance dataset comprises five curated and ML-ready UAV-based laser scanning data collections. The dataset is divided into development and test subsets, enabling method advancement and evaluation. The inclusion of diameter at breast height data expands its utility to the measurement of a classic tree variable.
  arXiv  Detail & Related papers  (2023-09-03T22:08:29Z)
• Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats [80.12253291709673]
  We propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model.
  arXiv  Detail & Related papers  (2022-12-29T22:22:49Z)
• Tree Detection and Diameter Estimation Based on Deep Learning [0.0]
  Tree perception is an essential building block toward autonomous forestry operations. Deep neural network models trained on our datasets achieve a precision of 90.4% for tree detection. Results offer promising avenues toward autonomous tree felling operations.
  arXiv  Detail & Related papers  (2022-10-31T15:51:32Z)
• Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [52.464516118826765]
  We introduce RandLA-Net, an efficient and lightweight neural architecture to infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches.
  arXiv  Detail & Related papers  (2021-07-06T05:08:34Z)
• Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene [76.4183572058063]
  We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks. The dataset has been point-wisely annotated with both hierarchical and instance-based labels. We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
  arXiv  Detail & Related papers  (2020-08-11T19:10:32Z)
• A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds [1.6249267147413524]
  We present an algorithm that operates on 3D-point clouds and estimates the underlying DTM for the scene using an end-to-end approach. Our model learns neighborhood information and seamlessly integrates this with point-wise and block-wise global features.
  arXiv  Detail & Related papers  (2020-05-21T15:54:55Z)
• Stance Detection Benchmark: How Robust Is Your Stance Detection? [65.91772010586605]
  Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim. We introduce a StD benchmark that learns from ten StD datasets of various domains in a multi-dataset learning setting. Within this benchmark setup, we are able to present new state-of-the-art results on five of the datasets.
  arXiv  Detail & Related papers  (2020-01-06T13:37:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.