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Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Related Experiment Video

Updated: Aug 19, 2025

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

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Published on: August 29, 2019

LiDAR and Deep Learning-Based Standing Tree Detection for Firebreaks Applications.

Zhiyong Liu¹, Xi Wang², Jiankai Zhu¹

¹School of Technology, Beijing Forestry University, Beijing 100083, China.

Sensors (Basel, Switzerland)

|November 26, 2022

Summary

This summary is machine-generated.

This study introduces an AI vision system for intelligent belt openers to aid forest fire prevention. PointRCNN demonstrated superior tree detection accuracy for creating firebreaks.

Keywords:

LiDAR deep learning firebreaks opening object detection

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Area of Science:

Forestry Science
Artificial Intelligence
Computer Vision

Background:

Effective forest fire prevention is crucial for ecological protection, necessitating efficient methods for creating firebreaks.
Intelligent automation, powered by artificial intelligence, offers a promising approach to enhance firebreak creation processes.

Purpose of the Study:

To introduce an innovative vision system for an intelligent belt opener to monitor environments during firebreak creation.
To evaluate the performance of deep learning networks in providing precise geometric and location information of trees using LIDAR data.

Main Methods:

Investigated four deep learning networks: PointRCNN, PointPillars, SECOND, and PV-RCNN.
Trained networks on a custom stand tree detection dataset derived from the KITTI point cloud dataset.
Utilized LIDAR data combined with deep learning for precise tree localization.

Main Results:

PointRCNN achieved the highest detection accuracy among the evaluated networks.
PV-RCNN and PointPillars also demonstrated strong performance in tree detection.
SECOND exhibited lower accuracy but identified the largest number of targets.

Conclusions:

The developed vision system effectively provides precise tree information for intelligent firebreak creation.
Deep learning networks, particularly PointRCNN, show significant potential for enhancing forest fire prevention strategies through automated firebreak systems.