Panoptic Segmentation of Tree Scenes from Mobile LiDAR Data

Dear Colleagues,

The spatial structure of forests is related to the management, optimization, and allocation of vegetation resources. The scientific evaluation of green quantity effectiveness has led to a trend toward precise research on forest spatial structure. Through intelligent information science technology, obtaining accurate tree instances and crown information to assess the green amount has become an interesting, yet complex, research topic.

LiDAR point clouds have better penetration than simple remote sensing images, with millimeter level processing accuracy, making them suitable for scientific greening and the fine evaluation of forests. Large-scale and individual-scale studies of 2D and 3D tree environments could be effectively performed through remote sensing data acquired from different sensor platforms and the use of computer vision and deep learning approaches.

This Special Issue focuses on the difficulties in analyzing the spatial structure of forests, using Mobile LiDAR point clouds as an input, or fusing multi-modal data to finely divide individual tree instances. By applying the panoptic segmentation of tree environments, results refine the forest tree models in real 3D scenarios, further serving the scientific greening, green assessment, and resource management of forests. Original research papers are expected to use the recently developed techniques to process a wide variety of remote sensing data for tree and vegetation mapping. High-quality contributions covering (but not limited to) the topics listed below are invited to submit to this Special Issue:

• Classification, detection, and segmentation of trees;
• Tree and vegetation inventory;
• Fusion of multi-modal data in vegetation scenes;
• Tree modeling;
• Mapping and monitoring of forests;
• Application of advanced image processing methodologies for mapping forest vegetation;
• Vegetation structural characteristics;
• Inversion of vegetation characteristics using mobile LiDAR data;
• Early detection of forest disturbances;
• Segmentation and reconstruction of non-tree objects in tree scenes.

Dr. Sheng Xu
Dr. Shaobo Xia
Dr. Di Wang
Prof. Dr. Qiaolin Ye
Guest Editors

Manuscript Submission Information

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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.

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