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Intelligence

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The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...

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Related Experiment Video

Updated: Feb 4, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Artificial Intelligence and Terrestrial Point Clouds for Forest Monitoring.

Maksymilian Kulicki^1,2, Carlos Cabo³, Tomasz Trzciński^1,4,5

¹IDEAS NCBR, ul. Chmielna 69, 00-801 Warsaw, Poland.

Current Forestry Reports

|February 2, 2026

Summary

Artificial intelligence (AI), especially deep learning (DL), significantly improves forest monitoring with LiDAR data. Advancements in AI for forestry require benchmark datasets and open data sharing for reproducible research.

Keywords:

Deep learning Forest inventory LiDAR Machine learning Open data Precision forestry TLS Tree characteristics

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Last Updated: Feb 4, 2026

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Area of Science:

Forestry Science
Computer Science
Remote Sensing

Background:

Ground-based LiDAR point clouds offer detailed 3D forest data.
Traditional methods struggle with the complexity of LiDAR data for forest inventory.
Artificial intelligence (AI), particularly deep learning (DL), shows promise for analyzing this data.

Purpose of the Study:

To review the integration of AI and DL with ground-based LiDAR for forest monitoring.
To identify current trends, advancements, and future research directions.
To highlight the potential of AI in enhancing forest management and conservation.

Main Methods:

Review of recent studies on AI/DL applications in terrestrial LiDAR data analysis.
Focus on techniques like semantic segmentation, individual tree segmentation, and species classification.
Discussion of challenges and proposed solutions, including benchmark datasets and synthetic data.

Main Results:

DL models outperform traditional machine learning for forest inventory tasks using LiDAR.
Key advancements in vegetation structure labeling, tree segmentation, and species classification.
Challenges include lack of standardized metrics, data/code sharing, and need for reference data.

Conclusions:

AI, especially DL, is transformative for accurate and efficient forest monitoring with LiDAR.
Critical needs include benchmark datasets, open-access policies, and exploration of novel DL architectures.
These advancements are vital for reproducibility, comparative studies, and improved forest management.