Data

Landshark

Commonwealth Scientific and Industrial Research Organisation
Steinberg, Dan
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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=http://hdl.handle.net/102.100.100/707126?index=1&rft.title=Landshark&rft.identifier=http://hdl.handle.net/102.100.100/707126?index=1&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=Landshark is a set of python command line tools that for supervised learning problems on large spatial raster datasets (with sparse targets). It solves problems in which the user has a set of target point measurements (such as geochemistry, soil classification, or depth to basement) and wants to relate those to a number of raster covariates (like satellite imagery or geophysics) to predict the targets on the raster grid.&rft.creator=Steinberg, Dan &rft.date=2025&rft.edition=v1&rft_rights=Licence Defined by Data Provider https://data.csiro.au/dap/ws/v2/licences/1161&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2025.&rft_subject=Machine learning&rft_subject=spatial prediction&rft_subject=raster prediction&rft_subject=supervised learning&rft_subject=python3&rft_subject=tensor flow&rft_subject=Deep learning&rft_subject=Machine learning&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=Neural networks&rft_subject=Spatial statistics&rft_subject=Statistics&rft_subject=MATHEMATICAL SCIENCES&rft.type=dataset&rft.language=English Access the data
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https://data.csiro.au/dap/ws/v2/licences/1161

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

All Rights (including copyright) CSIRO 2025.

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Landshark is a set of python command line tools that for supervised learning problems on large spatial raster datasets (with sparse targets). It solves problems in which the user has a set of target point measurements (such as geochemistry, soil classification, or depth to basement) and wants to relate those to a number of raster covariates (like satellite imagery or geophysics) to predict the targets on the raster grid.

Available: 2025-07-17

Data time period: 2017-01-01 to ..

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Subjects
Deep Learning | Information and Computing Sciences | Mathematical Sciences | Machine Learning | Machine learning | Neural Networks | Spatial Statistics | Statistics | python3 | raster prediction | spatial prediction | supervised learning | tensor flow |

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Identifiers
ACN 633 798 857