Matches in Nanopublications for { ?s ?p ?o <https://w3id.org/np/RA0drtRrGXj38GaLXLwCLJ-iwnzbV97VIysQ75-8rcGNQ/assertion>. }
- zenodo.6387953 description "Contains outputs, (vector, raster and figures), generated in the Jupyter notebook of Tree crown delineation using detectreeRGB" assertion.
- zenodo.5494629 description "Contains input Datasets of detectreeRGB AI4ER MRes Project used in the Jupyter notebook of Tree crown delineation using detectreeRGB" assertion.
- forest-modelling-treecrown_detectreeRGB.ipynb description "Jupyter Notebook hosted by the Environmental Data Science Book." assertion.
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- 6bc62582-2f11-4983-a51c-0af32459eca6 description "The research object refers to the Tree crown delineation using detectreeRGB notebook published in the Environmental Data Science book. Purpose: Accurately delineating trees using detectron2, a library that provides state-of-the-art deep learning detection and segmentation algorithms. Modelling approach: An established deep learning model, Mask R-CNN was deployed from detectron2 library to delineate tree crowns accurately. A pre-trained model, named detectreeRGB, is provided to predict the location and extent of tree crowns from a top-down RGB image, captured by drone, aircraft or satellite. detectreeRGB was implemented in python 3.8 using pytorch v1.7.1 and detectron2 v0.5. Further details can be found in the repository documentation. Highlights: detectreeRGB advances the state-of-the-art in tree identification from RGB images by delineating exactly the extent of the tree crown. We demonstrate how to apply the pretrained model to a sample image fetched from a Zenodo repository. Our pre-trained model was developed using aircraft images of tropical forests in Malaysia. The model can be further trained using the user’s own images" assertion.
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