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Published July 9, 2021 | Version 1.0.2
Dataset Open

Datasets of SH's AI4ER MRes Project

  • 1. University of Cambridge

Contributors

Data curator:

Supervisor:

  • 1. University of Cambridge
  • 2. NERC

Description

This dataset contains the data used a generated during the course of SH's AI4ER MRes project. The dataset consists of LiDAR and RGB data over Sepilok Forest Reserve, in Sabah, Malaysia, collected and processed by NERC and NEODAAS, along with 901 manually delineated tree crowns in the area, and tree crown delineations predicted by two models: a Mask R-CNN model developed by SH, and the ITCfast algorithm, developed by Tom Swinfield and optimised by SH. LiDAR data is given for two years, 2014 and 2020, which was used to calculate changes in the carbon stock of the forest. The LiDAR and RGB data are provided as tiffs, while tree crowns are provided as shapefiles.

Files

RCD105_MA14_21_orthomosaic_20141023_reprojected_full_res.tif

Files (7.9 GB)

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Additional details

Funding

UKRI Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) EP/S022961/1
UK Research and Innovation
A 3D perspective on the effects of topography and wind on forest height and dynamics NE/S010750/1
UK Research and Innovation