Deep Learning for Forest Monitoring: A Systematic Review of 186 Studies

A systematic review of 186 peer-reviewed studies from 2011 to 2026 evaluates the transition of deep learning architectures into actionable ecological technologies. The review, authored by Gabriel Osei Forkuo, focuses on architectures like 3D-Convolutional Neural Networks and Vision Transformers (ViTs) for forest monitoring and management. It was last updated on May 7, 2026, and is shared under a CC-BY-4.0 license.

Use Cases

• Benchmarking the performance of different deep learning architectures for forest analysis based on the reviewed studies.
• Identifying trends in the application of computer vision to ecological management from 2011 to 2026.
• Surveying the evolution of technologies like Vision Transformers (ViTs) for remote sensing tasks in forestry.

Strengths

• Systematic review of 186 peer-reviewed studies provides a substantial evidence base.
• Covers a 15-year time range from 2011 to 2026, offering a longitudinal perspective on technological evolution.
• Explicitly evaluates specific architectures like 3D-CNNs and Vision Transformers (ViTs).

Limitations

• The dataset is small at 3.0 MB, which may limit the amount of primary data included.
• Row count and column-level documentation are unknown, requiring manual inspection after download.
• Description metadata is limited; actual data quality and structure must be verified.

Provenance

Source

figshare

Collection Method

Systematic literature review.

Time Range

2011–2026

Freshness

Last updated 2026-05-07 07:55:51