1. Australian Antarctic Data Centre
2. Discover and Manage Data
3. Records
4. AAS_4628_Robinson_Ridge_UAV_Orthomosaics

Description

This dataset supports the development of machine learning models for vegetation segmentation in Antarctic ecosystems and enables reproducible remote sensing analyses by providing both imagery and associated ground-truth labels in standard GIS-compatible formats.

1. Ground Truth Labels This record contains manually and semi-automatically generated pixelwise shapefiles, used to annotate vegetation types with high confidence. This component contains both manually and semi-automatically generated pixel-wise shapefiles classifying four vegetation classes: Usnea spp., black lichen, moss, and non-vegetation. The purpose of these labels is to provide high-quality reference data for training and validating machine learning models for vegetation segmentation.

Manual labelling was conducted using ground truth points gathered during field campaigns. Polygons were drawn around confidently identified vegetation patches, focusing on central areas to minimise edge misclassification. (Files: .shp, .dbf, .prj, .shx, and readme.txt)

Semi-automatic labelling was introduced to address the limitations of manual annotation at a GSD of 2.93 cm/pixel. A suite of 28 vegetation indices (VIs) were evaluated, and three (MSAVI and GNDVI) were selected for their spectral separability between classes. (Files: .shp, .dbf, .prj, .shx, and readme.txt)

2. Orthomosaic The RGB and multispectral (MS) images captured by UAVs were processed into high-resolution orthomosaics using Agisoft Metashape 1.6.6 and georeferenced via QGIS 3.2.0. These orthomosaics serve as the base imagery for generating labels and training machine learning models. Output formats include .tif for orthomosaics, .ovr for overviews, and .pdf reports documenting the image processing steps.

Data Collection and Analysis Imagery was captured in January 2023 at Robinson Ridge, Antarctica, using a BMR3.9RTK UAV equipped with a MicaSense Altum sensor and Sony Alpha 5100 camera, flown at 70 m altitude (GSD ≈ 2.93 cm/pixel). Over 2,800 images were collected over ~5.15 ha.

Usage Notes · Embargoed files require permission for access. · Refer to the included readme.txt files in each record for file structure, formats, and usage instructions. · The dataset is optimised for developing and validating deep learning models for remote sensing classification in polar environments.

Purpose

This dataset supports the development of machine learning models for vegetation segmentation in Antarctic ecosystems and enables reproducible remote sensing analyses by providing both imagery and associated ground-truth labels in standard GIS-compatible formats

Quality

During data collection, poor image quality in certain sections of the orthomosaic—caused by lighting conditions and snow—reduced the visual clarity necessary for reliable interpretation.

In the analysis stage, accurately identifying vegetation species was challenging due to suboptimal image resolution. Furthermore, the lack of sufficient ground truth data made it difficult to validate segmentation results and assess accuracy.

Access

These data are not yet publicly available for download.

Temporal Coverages

• Start date: 2023-01-01 - Stop date: 2023-01-15

Spatial Coverages

Science Keywords

• EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > FUNGI > LICHENS
• EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PLANTS > MOSSES/HORNWORTS/LIVERWORTS
• EARTH SCIENCE > BIOSPHERE > VEGETATION > VEGETATION INDEX

Use Constraints

This data set conforms to the CCBY Attribution License (http://creativecommons.org/licenses/by/4.0/).

Please follow instructions listed in the citation reference provided at http://data.aad.gov.au/aadc/metadata/citation.cfm?entry_id=AAS_4628_Robinson_Ridge_UAV_Orthomosaics when using these data.