<b>Benchmarking Deep Learning Architectures for Forest Monitoring and Management: A Systematic Review</b>

This paper presents a systematic review of 186 peer-reviewed articles (2011–2026) to evaluate how Deep Learning (DL) and Computer Vision (CV) are transitioning from observational tools to actionable ecotechnologies for forest restoration. By automating the extraction of multi-modal structural and spectral data, advanced architectures—such as 3D Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs)—are directly empowering evidence-based ecological engineering tasks, including climate-resilient carbon accounting, the tracking of biodiversity shifts during habitat recovery, and early-stage disease mitigation.