Artificial Intelligence and Terrestrial Point Clouds for Forest Monitoring
The review underscores the transformative role of AI, particularly DL, in enhancing the accuracy and efficiency of forest monitoring using ground-based 3D point clouds. To advance the field, there is a critical need for comprehensive benchmark datasets, open-access policies for data and code, and the exploration of novel DL architectures and learning paradigms. These steps are essential for improving research reproducibility, facilitating comparative studies, and unlocking new insights into forest management and conservation.
Authors
- Krzysztof Stereńczak (ORCID: https://orcid.org/0000-0002-9556-0144)
- Maksymilian Kulicki (ORCID: https://orcid.org/0000-0001-7138-9592)
- Janusz Będkowski (ORCID: https://orcid.org/0000-0003-2630-1947)
- Carlos Çabo (ORCID: https://orcid.org/0000-0002-0387-9052)
- T. P. Trzcinski (ORCID: https://orcid.org/0000-0002-1486-8906)
Institutions
- Warsaw University of Technology (PL)
- Universidad de Oviedo (ES)
- Showa Corporation (JP)
- Forest Research Institute (HU)
- Institute of Fundamental Technological Research (PL)
- Narodowy Instytut Leków (PL)
Publication Details
- Journal
- Current Forestry Reports
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1007/s40725-024-00234-4
- Citations
- 17
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 3.50
Funders
- Sight Research UK
- Natural Environment Research Council