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{"Abstract":["Abstract<\/strong><\/p>\n\nThe NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network\u2019s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. <\/p>\n\nHow can I see the data?<\/strong><\/p>\n\nA web server to look through predictions is available through idtrees.org<\/p>\n\nDataset Organization<\/strong><\/p>\n\nThe shapefiles.zip contains 11,000 shapefiles, each corresponding to a 1km^2 RGB tile from NEON (ID: DP3.30010.001). For example "2019_SOAP_4_302000_4100000_image.shp" are the predictions from "2019_SOAP_4_302000_4100000_image.tif" available from the NEON data portal: https://data.neonscience.org/data-products/explore?search=camera. NEON's file convention refers to the year of data collection (2019), the four letter site code (SOAP), the sampling event (4), and the utm coordinate of the top left corner (302000_4100000). For NEON site abbreviations and utm zones see https://www.neonscience.org/field-sites/field-sites-map. <\/p>\n\nThe predictions are also available as a single csv for each file. All available tiles for that site and year are combined into one large site. These data are not projected, but contain the utm coordinates for each bounding box (left, bottom, right, top). For both file types the following fields are available:<\/p>\n\nHeight: The crown height measured in meters. Crown height is defined as the 99th quartile of all canopy height pixels from a LiDAR height model (ID: DP3.30015.001)<\/p>\n\nArea: The crown area in m2<\/sup> of the rectangular bounding box.<\/p>\n\nLabel: All data in this release are "Tree".<\/p>\n\nScore: The confidence score from the DeepForest deep learning algorithm. The score ranges from 0 (low confidence) to 1 (high confidence)<\/p>\n\nHow were predictions made?<\/strong><\/p>\n\nThe DeepForest algorithm is available as a python package: https://deepforest.readthedocs.io/. Predictions were overlaid on the LiDAR-derived canopy height model. Predictions with heights less than 3m were removed.<\/p>\n\nHow were predictions validated?<\/strong><\/p>\n\nPlease see<\/p>\n\nWeinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics<\/em>, 56<\/em>, 101061.<\/p>\n\nWeinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. bioRxiv<\/em>.<\/p>\n\nWeinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." Remote Sensing<\/em> 11.11 (2019): 1309.<\/p>\n\nWere any sites removed?<\/strong><\/p>\n\nSeveral sites were removed due to poor NEON data quality. GRSM and PUUM both had lower quality RGB data that made them unsuitable for prediction. NEON surveys are updated annually and we expect future flights to correct these errors. We removed the GUIL puerto rico site due to its very steep topography and poor sunangle during data collection. The DeepForest algorithm responded poorly to predicting crowns in intensely shaded areas where there was very little sun penetration. We are happy to make these data are available upon request.<\/p>\n\n# Contact<\/p>\n\nWe welcome questions, ideas and general inquiries. The data can be used for many applications and we look forward to hearing from you. Contact ben.weinstein@weecology.org. <\/p>"],"Other":["Gordon and Betty Moore Foundation: GBMF4563"]}
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PVLAB: Remotely Sensed Surface Currents from Field Experiments (2013, 2018, 2021, 2022)
This archive contains 2-min mean remotely sensed surface currents generated using optical imagery and PIV (Dooley et al., 2024) from field experiments (2013, 2018, 2021, 2022) occurring at the U.S. Army Corps of Engineers Field Research Facility, in Duck, NC. The README.txt file contains information regarding variables found in the MATLAB (PVLAB_PIV_SurfaceFlows.mat) file, including estimate locations, units, and timestamps. Estimates were generated at times with appropriate conditions for remote sensing (e.g., sufficient foam tracer) that were within 1-day of measured bathymetry at the field site. Additional data for the field site (obtained by the USACE Field Research Facility or by NOAA) including the measured bathymetry and wave conditions can be found at: https://chlthredds.erdc.dren.mil/thredds/catalog/frf/catalog.html Remote sensing estimates are not perfect and errors in filtering out bad data are possible. Please reach out to Ciara Dooley at cdooley@whoi.edu, Steve Elgar at selgar@mac.com, and Britt Raubenheimer at braubenheimer@whoi.edu if you have any questions. Additionally, the optical imagery (large dataset, many TBs) used to generate flow estimates can be accessed by contacting the authors.
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- PAR ID:
- 10599414
- Publisher / Repository:
- Zenodo
- Date Published:
- Format(s):
- Medium: X
- Location:
- Zenod,o CERN, Europe
- Right(s):
- Creative Commons Attribution 4.0 International
- Institution:
- CERN
- Sponsoring Org:
- National Science Foundation
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