Metadata details
AAS_4628_UAS_Robinson_Ridge_Lichen_Maps
All scientific data collected by the Australian Antarctic program (AAp) are eventually described in the Catalogue of Australian Antarctic and Subantarctic Metadata (CAASM). CAASM can be used to search through AAp data descriptions, and it also provides links to access publicly available datasets, which can either be immediately downloaded or obtained from the Australian Antarctic Data Centre (AADC).
- Citation
- Amarasingam, N., Sandino, J., Doshi, A., Randall, K., King, D., Blackman, E., Barthelemy, J., Bollard, B., Robinson, S. and Gonzalez, F. (2025) Robinson Ridge Lichen Segmentation Maps Using UAV imagery and Machine Learning, Ver. 1, Australian Antarctic Data Centre - doi:10.26179/xahj-dq75, Accessed: 2026-08-24
- Title
- Robinson Ridge Lichen Segmentation Maps Using UAV imagery and Machine Learning
- Data Centre
- Australian Antarctic Data Centre, Australia
- DOI
- doi:10.26179/xahj-dq75
- Created Date
- 2025-05-13
- Revision Date
- 2025-05-14
- Parent record
- AAS_4628_UAS
Datasets and documents
Related links
Description
This is a child record, see the parent record AAS_4628_UAS for more information.
This work was completed as part of the program Securing Antarctica's Environmental Future (SAEF).
Dataset Components (Open Access)
1. Output Segmentation Maps
This record includes vegetation segmentation outputs generated by standalone deep learning models (DeepLabv3+, FCN, U-Net) and an ensemble stacking approach using XGBoost. These models were used to classify vegetation communities in the study area. The results provide detailed segmentation maps that highlight the distribution of different vegetation types. The output segmentation maps are critical for understanding and analysing the vegetation communities in Robinson Ridge, Antarctica. These maps, generated through advanced machine learning techniques, support further research, validation, and applications in environmental monitoring. The inclusion of Vegetation Indices (VIs) helps in deriving additional insights into vegetation health and distribution.
Data Collection and Analysis
Data was collected in January 2023 using a BMR3.9RTK UAV developed by SaiDynamics Australia, equipped with a MicaSense Altum multispectral sensor and a Sony Alpha 5100 RGB camera. Flights at 70 m altitude yielded a ground sampling distance of 2.93 cm/pixel. A total of 2,814 images were collected, covering around 5.15 hectares.
Usage Notes
Refer to the readme.txt files in each record for further details on data formats.
Files:
- .dat, .hdr, .tif – Segmentation maps and vegetation indices
- readme.txt – Description and metadata for data usage and 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 from the provided URL.
Temporal Coverages
- Start date: 2023-01-01 - Stop date: 2023-01-15
Spatial Coverages
Science Keywords
- EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PLANTS > MOSSES/HORNWORTS/LIVERWORTS
- EARTH SCIENCE > BIOSPHERE > VEGETATION > VEGETATION INDEX
- EARTH SCIENCE > SPECTRAL/ENGINEERING > VISIBLE WAVELENGTHS > VISIBLE IMAGERY
- EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > FUNGI > LICHENS
- EARTH SCIENCE > DATA ANALYSIS AND VISUALIZATION > IMAGE PROCESSING
- EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > TERRESTRIAL ECOSYSTEMS
Additional Keywords
- USNEA SPP.
- BLACK LICHEN
- SNOW/ROCK/BARE GROUND
- SEGMENTATION
- MACHINE LEARNING
- DEEP LEARNING
- ROTARY WING UAVS
- RGB IMAGING
- MULTISPECTRAL ORTHOMOSAIC
- MULTISPECTRAL IMAGING
- SAEF
- SECURING ANTARCTICA’S ENVIRONMENTAL FUTURE
- ANTARCTIC TERRESTRIAL ECOSYSTEMS
- HIGH-RESOLUTION MULTISPECTRAL DATA
- ORTHOMOSAIC IMAGE ANALYSIS
- EXTREME ENVIRONMENT REMOTE SENSING
- DRONE-BASED ENVIRONMENTAL MONITORING
- MOSS AND LICHEN DETECTION
Locations
- CONTINENT > ANTARCTICA > ROBINSON RIDGE
- GEOGRAPHIC REGION > POLAR
Platforms
- Unmanned Aerial Vehicle
Instruments
- CAMERAS
- High Resolution Visible Imaging System
- Multispectral Camera
Researchers
- amarasingam, narmilan (INVESTIGATOR)
- sandino, juan (INVESTIGATOR)
- doshi, ashray (INVESTIGATOR)
- randall, krystal (INVESTIGATOR)
- king, diana (INVESTIGATOR)
- blackman, elka (INVESTIGATOR)
- barthelemy, johan (INVESTIGATOR)
- bollard, barbara (INVESTIGATOR)
- robinson, sharon (INVESTIGATOR)
- gonzalez, felipe (INVESTIGATOR)
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_UAS_Robinson_Ridge_Lichen_Maps when using these data.
