Data

AusRichness: A machine learning ready dataset for plant species richness prediction in Australia

Commonwealth Scientific and Industrial Research Organisation
Guo, Yiqing ; Mokany, Karel ; Levick, Shaun ; Yang, Jinyan ; Moghadam, Peyman
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/7d5h-yp05&rft.title=AusRichness: A machine learning ready dataset for plant species richness prediction in Australia&rft.identifier=https://doi.org/10.25919/7d5h-yp05&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=This collection consists of a machine learning ready dataset for plant species richness prediction. This dataset was adopted for modelling the spatiotemporal distribution of plant species richness in Australia in the following paper:\n\nGuo, Y., Mokany, K., Levick, S. R., Yang, J., & Moghadam, P. (2024). Spatioformer: A Geo-encoded Transformer for Large-Scale Plant Species Richness Prediction. arXiv preprint arXiv:2410.19256.&rft.creator=Guo, Yiqing &rft.creator=Mokany, Karel &rft.creator=Levick, Shaun &rft.creator=Yang, Jinyan &rft.creator=Moghadam, Peyman &rft.date=2024&rft.edition=v1&rft_rights=Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence https://creativecommons.org/licenses/by-nc-sa/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2024.&rft_subject=vascular plant&rft_subject=biodiversity&rft_subject=mapping&rft_subject=species richness&rft_subject=prediction&rft_subject=Photogrammetry and remote sensing&rft_subject=Geomatic engineering&rft_subject=ENGINEERING&rft_subject=Conservation and biodiversity&rft_subject=Environmental management&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Deep learning&rft_subject=Machine learning&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence
https://creativecommons.org/licenses/by-nc-sa/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2024.

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This collection consists of a machine learning ready dataset for plant species richness prediction. This dataset was adopted for modelling the spatiotemporal distribution of plant species richness in Australia in the following paper:

Guo, Y., Mokany, K., Levick, S. R., Yang, J., & Moghadam, P. (2024). Spatioformer: A Geo-encoded Transformer for Large-Scale Plant Species Richness Prediction. arXiv preprint arXiv:2410.19256.

Available: 2024-12-17

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Subjects
Conservation and Biodiversity | Deep Learning | Engineering | Environmental Sciences | Environmental Management | Geomatic Engineering | Information and Computing Sciences | Machine Learning | Photogrammetry and Remote Sensing | biodiversity | mapping | prediction | species richness | vascular plant |

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