Tree-Parts Instance Segmentation Dataset and Pre-Trained Model by KyteAi

Images

1130 images

Explore Dataset

Here are a few use cases for this project:

Biodiversity Monitoring: This model could be used by botanists and ecologists to differentiate tree species based on observed differences in their parts like trunk, canopy, and root structure. This could be beneficial in surveys to monitor biodiversity or track the spread of specific species in given regions.
Forest Conservation Programs: It could be employed in forest conservation initiatives to identify unhealthy trees by analyzing abnormal shapes or sizes of their parts. It can be used to track the progress of reforestation efforts by identifying young trees based on their smaller canopy and trunk size.
Educational Tools: This model could be integrated into educational software to teach students about different trees. It would enable them to interactively learn about different trees and their parts in a fun and engaging manner.
Urban Planning: Urban developers can use this model to plan tree planting in cities. They could evaluate which species of trees, based on their trunk, canopy, and root structure, would be most appropriate for their urban environments (e.g., smaller roots for sidewalk planting).
Virtual Reality (VR) Applications: For VR developers working on creating immersive nature experiences, this model could be used to generate realistic trees. By identifying different types of trees and their distinct parts, developers can produce more detailed and varied virtual environments.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite this Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ tree-parts_dataset,
    title = { Tree-Parts Dataset },
    type = { Open Source Dataset },
    author = { KyteAi },
    howpublished = { \url{ https://universe.roboflow.com/kyteai/tree-parts } },
    url = { https://universe.roboflow.com/kyteai/tree-parts },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { aug },
    note = { visited on 2023-12-05 },
}