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Published September 23, 2024 | Version v4
Dataset Open

Explainable few-shot learning workflow for detecting invasive and exotic tree species

Description

This is the supporting dataset of research work: Explainable few-shot learning workflow for detecting invasive and exotic tree species. (Link to be added after the publication) In this research, we presents a workflow that tackles both challenges by proposing an explainable few-shot learning workflow for detecting invasive and exotic tree species in the Atlantic Forest of Brazil using Unmanned Aerial Vehicle (UAV) images. By integrating a Siamese network with explainable AI (XAI), the workflow enables the classification of tree species with minimal labeled data while providing visual, case-based explanations for the predictions.

The workflow is accessible in this GitHub repository (Link to be added after the publication). The required dataset of this workflow in provided in this Zenodo repository.

This dataset repository has the following contents

  • uav_img.zip: the UAV orthomosaic image (.tif) of the study area used in this research, with related metadata

  • tree_labels.zip: the labels of trees created by expert, available in .shp and .gpkg
  • cutouts.zip: tree cutouts used in this study. They are two sub-directories:

    • all_cutouts: all the candidated cutouts from three sources. See the README.md file insisde this folder for more information
    • selected cutout: the manually selected cutouts from all cutouts used for training.
  • training_pairs.zip: training data created for base network traning. It is created by pairing the selected cutouts.

  • netflora.zip: Netflora workflow prediction results

Files

cutouts.zip

Files (5.6 GB)

Name Size Download all
md5:bb71066e990cb8fe202c4c6021496e42
19.2 MB Preview Download
md5:47e83346234f5bfc78e5fd3eaaa28fea
32.1 MB Preview Download
md5:969cc09bd3ca931cb3936cb73fda5512
5.4 GB Preview Download
md5:d7b76a619ed115129be1f3ceec74a6a3
18.2 kB Preview Download
md5:a19beeae00b28279b25a27485050623b
111.8 MB Preview Download

Additional details

Funding

Talent Program Veni 2020 18091
Dutch Research Council

Software

Programming language
Python
Development Status
Concept