Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details at the time of acquisition.

Mining footprints as supplied by Glencore on two different dates:

4/11/2014

7/11/2014

Powerpoint and data packages for the following sites supplied:

Liddell OC, Mt Owen Complex (includes Glendell OC and Mt Owen OC mines), Mangoola OC, Ravensworth OC, Ravensworth UG, West Wallsend UG.)

Note, data packages only provided for Ulan \#3 UG and Ulan West UG Mines.

Further data provided on the 7th November:

• Ulan OC's data package

• Ulan complex Powerpoint presentation

• Bulga's 2012 and final landform spoil/dump surfaces

This dataset has been provided to the BA Programme for use within the programme only. Third parties should contact Glencore. http://www.glencore.com/.

Purpose

Mining footprints as supplied by Glencore on 2 different dates

Dataset History

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied.

The metadata was not provided by the data supplier and has been compiled by the programme based on known details at the time of acquisition.

No history was provided with the dataset.

Dataset Citation

Glencore (2014) Mining Footprints Glenncore 20141107. Bioregional Assessment Source Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/cd1a6a82-1bfd-4cae-bdca-40ba77166407.

Analyses of minerals in chondrules and associated silica-rich igneous rims in CR chondrites. Data are from quantitative wavelength dispersive spectroscopy (WDS) X-ray maps obtained on the JEOL JXA-8530F electron microprobe at the University of Manchester. Powerpoint files show locations of areas extracted from WDS maps: each area is an individual analysis of a mineral grain. Excel files contain extracted data for each location.

The data involved in this paper is from https://www.planet.com/explorer/. The resolution is 3m, and there are 3 main bands, RGB. Since the platform can only download a certain amount of data after applying for an account in the form of education, and at the same time the data is only retained for one month, we chose 8 major cities for the study, 2 images per city. we also provide detailed information on the data visualization and classification results that we have tested and retained in a PPT file called paper, we also provide detailed information on the data visualization and classification results of our tests in a PPT file called paper-result, which can be easily reviewed by reviewers. At the same time, reviewers can also download the data to verify the applicability of the results based on the coordinates of the data sources provided in this paper.The algorithms consist of three main types, one is based on traditional algorithms including object-based and pixel-based, in which we tested the generalization ability of four classifiers, including Random Forest, Support Vector Machine, Maximum Likelihood, and K-mean, in the form of classification in this different way. In addition, we tested two of the more mainstream deep learning classification algorithms, U-net and deeplabV3, both of which can be found and applied in the ArcGIS pro software. The traditional algorithms can be found by checking https://pro.arcgis.com/en/pro-app/latest/help/analysis/image-analyst/the-image-classification-wizard.htm to find the running process, while the related parameter settings and Sample selection rules can be found in detail in the article. Deep learning algorithms can be found at https://pro.arcgis.com/en/pro-app/latest/help/analysis/deep-learning/deep-learning-in-arcgis-pro.htm, and the related parameter settings and sample selection rules can be found in detail in the article. Finally, the big model is based on the SAM model, in which the running process of SAM is from https://github.com/facebookresearch/segment-anything, and you can also use the official Meta segmentation official website to provide a web-based segmentation platform for testing https:// segment-anything.com/. However, the official website has restrictions on the format of the data and the scope of processing.