Leaf Vein Network CNN Images

<p>This download site contains the CNN vein network predictions and set of Matlab programs&nbsp; that were used for the analyses in Xu <em>et al.</em>, (2020) and Blonder <em>et al.,</em> (2020). These require Matlab 2020a or later. They may work on earlier versions of MatLab, but this has not been tested and cannot be guaranteed.</p> <p>The files are as follows:</p> <ol> <li>Zip files (e.g. BEL_downsampled_images.zip) containing a complete set of images of leaf vein predictions from a fully trained convolutional neural network (CNN), along with the ground truth data. Each folder in the unzipped file contains a sample represented by a CODE with format X-TY-BZ. X represents the name of a plot in the Global Ecosystems Monitoring network database (e.g. &#39;BEL&#39;). Tree (T) Y indicates the number of a tree within a plot (e.g. &#39;101&#39;) and Z represents the light stratum of the canopy where the leaf was collected (either &#39;S&#39; for &#39;sunlit&#39; or &#39;SH&#39; for &#39;shaded&#39;).</li> <li>A set of Matlab programs (Matlab files.zip) to compare the CNN predictions against other vein extraction approaches.</li> <li>A Matlab Readme file with instructions on how to run the analyses.</li> </ol> <p><strong>References</strong></p> <p><strong>Software GUI:</strong></p> <p>Xu, H., Blonder, B., Jodra, M., Malhi, Y. and Fricker, M.D. (2020) Automated and accurate segmentation of leaf venation networks via deep learning. <strong>New Phytol</strong>. (In press).</p> <p><strong>Analysis of trait data:</strong></p> <p>Blonder, B., S. Both, M. Jodra, H. Xu, M. Fricker, I. S. Matos, N. Majalap, D. F. R. P. Burslem, Y. Teh and Y. Malhi (2020) Linking functional traits to multiscale statistics of leaf venation networks. <strong>New Phytol</strong>. (In press).</p> <p><strong>Original image data set and ground truths</strong></p> <p>Blonder, B., Both, S., Jodra, M., Majalap, N., Burslem, D., Teh, Y. A., and Malhi, Y. (2019) Leaf venation networks of Bornean trees: images and hand‐traced segmentations. <strong>Ecology </strong>100: e02844.<a href="https://doi.org/10.1002/ecy.2844">10.1002/ecy.2844</a>.</p> <p>Available from: <a href="https://ora.ox.ac.uk/objects/uuid:de65fc07-4b8f-4277-a6c4-82836afbdeb3">https://ora.ox.ac.uk/objects/uuid:de65fc07-4b8f-4277-a6c4-82836afbdeb3</a></p>