一种基于零样本学习的人造林碳汇估算方法和系统
By combining robot dogs and zero-shot learning algorithms with LiDAR data and satellite remote sensing images, the problem of inaccurate tree identification in drone aerial photography was solved, and efficient and accurate carbon storage estimation of man-made forests was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA CARBON SINK ASSET OPERATION (SHENZHEN) CO LTD
- Filing Date
- 2024-07-02
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies make it difficult to effectively identify and count the number of trees during drone aerial photography, leading to inaccurate assessments of carbon storage in planted forests.
A robot dog was used for field sampling. Combined with lidar data and zero-shot learning algorithms, tree species identification and quantity estimation were performed by constructing class prototypes and compatibility functions through generative models. Satellite remote sensing images were used to improve the accuracy of identification and to calculate the carbon storage of soil and litter.
It enables efficient and accurate identification of tree numbers and species, improves the spatial coverage and data accuracy of carbon storage estimation in artificial forests, reduces human resource consumption, and is suitable for rapid estimation of carbon sinks over large areas.
Smart Images
Figure CN119006999B_ABST