Computer Application and Deep Learning in Forestry
A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".
Deadline for manuscript submissions: closed (12 September 2024) | Viewed by 12171
Special Issue Editors
Interests: artificial intelligence; visualization simulation and virtual reality for forestry
Special Issues, Collections and Topics in MDPI journals
Interests: computer graphics; computer vision; virtual reality
Special Issues, Collections and Topics in MDPI journals
Interests: pattern recognition, machine learning, and their applications in forestry; remote sensing image classification; tiny object detection recognition; robust feature extraction; distance metric learning; multi-view learning; artificial intelligence and forestry (forest fire prevention, vegetation classification, monitoring and prediction of combustible impact factors, etc.)
Special Issues, Collections and Topics in MDPI journals
Interests: quantitative remote sensing in forestry; application of LiDAR in forestry; digital forest resource monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: forest monitoring; forest scattering mechanisms at microwave bands; crop growth monitoring and identification; forest height inversion using PolInSAR technology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Technologies such as deep learning (DL) can reproduce expert observations on every single tree in hundreds or thousands of hectares. In recent years, DL techniques have been applied in the field of forestry for aspects such as tree detection, tree species classification, and forest disturbance detection.
Considering the importance and critical requirement for computer application and DL in the monitoring of forests, this Special Issue focuses on collecting new insights, novel approaches and the most recent advances in the field of computer technology and DL application in forestry. We also welcome papers on tree detection, tree species classification, and forest disturbance detection using DL methods.
Prof. Dr. Huaiqing Zhang
Prof. Dr. Meili Wang
Prof. Dr. Qiaolin Ye
Prof. Dr. Hua Sun
Prof. Dr. Wangfei Zhang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- deep learning
- algorithm
- tree detection
- tree species classification
- forest disturbance detection
- remote sensing
- satellite image analysis
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