The land remote sensing community has a long history of using supervised and unsupervised methods to help interpret and analyze remote sensing data sets. Until relatively recently, most remote sensing studies have used fairly conventional image processing and pattern recognition methodologies. In the past decade, NASA has launched a series of remote sensing missions known as the Earth Observing System (EOS). The data sets acquired by EOS instruments provide an extremely rich source of information related to the properties and dynamics of the Earth’s terrestrial ecosystems. However, these data are also characterized by large volumes and complex spectral, spatial and temporal attributes. Because of the volume and complexity of EOS data sets, efficient and effective analysis of them presents significant challenges that are difficult to address using conventional remote sensing approaches. In this paper we discuss results from applying a variety of different data mining approaches to global remote sensing data sets. Specifically, we describe three main problem domains and sets of analyses: (1) supervised classification of global land cover from using data from NASA’s Moderate Resolution Imaging Spectroradiometer; (2) the use of linear and non-linear cluster and dimensionality reduction methods to examine coupled climate-vegetation dynamics using a twenty year time series of data from the Advanced Very High Resolution Radiometer; and (3) the use of functional models, non-parametric clustering, and mixture models to help interpret and understand the feature space and class structure of high dimensional remote sensing data sets. The paper will not focus on specific details of algorithms. Instead we describe key results, successes, and lessons learned from ten years of research focusing on the use of data mining and machine learning methods for remote sensing and Earth science problems.

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 …Show full descriptionAbstract 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.

Abstract

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied:

*Glencore have committed to assisting the DoW with their Bioregional Assessment Program.

*This assistance involves the provision of 'Mined Out' surfaces and polygons for both Underground and Open Cut operations across the business.

*The Bulga Complex was nominated as the pilot site, and the nature of the data provided for the Bulga Complex has been detailed in this presentation.

*The Bulga complex is made up of both underground and surface operations.

*The Underground operations extract coal using longwall methods, and target 5 primary seam horizons: Whybrow, Blakefield, Glen Munro, and Woodlands Hill.

*The Bulga Open Cut reserves are spilt into 2 broad categories, current approved mining reserves (Main and Bayswater Pits), and Bulga Optimisation Project (BOP) reserves (East and East Extension Pits).

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

Dataset History

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied:

Please see power point presentation included in Data folder:

The data has been provided at the following:

1.Full Life of Mine Plans for both OC & UG at 5 yearly increments

»Year End 2012 (this is Actual data)

»Year End 2017

»Year End 2022

»Year End 2027, etc. at 5 year increments until end LOM

»Only the relevant data for each time period has been provided.

»Please note that LOM plans are conceptual. Variations may occur as the LOM plans are reviewed (annually).

2.Currently approved mine plans:

»For Bulga OC, the 2015 surface represents the completion of Main Pit

»For Bulga UG, all surfaces provided represent the currently approved Mine Plan (Blakefield, Glen Munro and Woodlands Hill Seams)

Mined Out' polygons by mining horizon:

Whybrow (South Bulga & Beltana Mines)

BUG_WHY_2012_Mined Out.Dwg

NB: Extraction from the Whybrow seam was completed in April 2011

Blakefield

BUG_BLK_2017_Mined Out.dwg

BUG_BLK_2022_Mined Out.dwg

BUG_BLK_2027_Mined Out.dwg

Glen Munro

BUG_GMUN_2027_mined Out.dwg

BUG_GMUN_2032_Mined Out.dwg

BUG_GMUN_2037_Mined Out.dwg

Woodlands Hill

BUG_WHILL_2027_Mined Out.dwg

BUG_WHILL_2032_Mined Out.dwg

BUG_WHILL_2037_Mined Out.dwg

All polygons have been fitted to the mining horizon grids.

Dataset Citation

Glencore Xstrata (2015) Mining Footprints Glencore Xstrata Bulga Pilot 20150203. Bioregional Assessment Source Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/e1e997b4-4f49-40c5-b930-26b87683f71f.

Aviation safety systems have undergone dramatic changes over the past fifty years. If you take a look at the early technology in this area, you'll see that there was a lot of work done in the area of so-called 'built-in-testing' (BIT) which essentially tests connectivity between different components. Technology has moved forward very far since that time. With the massive storage systems and advanced sensors and other communications systems, we are now able to capture and store vast quantities of health and control related data. This data is usually stored off-line for future analysis. In many cases, we also have an abundance of human-written text reports that relate to known safety issues. A key problem is to 'look' across all these data sources in order to find precursors to safety events. Although humans do look at many aspects of the data, it is difficult, if not impossible for them to integrate all the information available in a meaningful way. Other industries face this glut of data in their own way. Businesses have invested heavily in business intelligence products based on data mining that are designed to convert data into actionable information to maximize their profits or other metrics. The IVHM project is investing in data mining technologies to help sift through these massive data sets to uncover actionable information from a safety perspective. I've attached a presentation that Irv Statler and I gave at NASA HQ on this subject during an Aeronautics Technical Seminar. A video of the talk is also included. It's about 90 minutes long, so grab some popcorn. :)

Lithuania LT: Foreign Direct Investment Financial Flows: Outward: USD: Total: Mining and Quarrying data was reported at 0.775 USD mn in 2020. This records an increase from the previous number of 0.034 USD mn for 2019. Lithuania LT: Foreign Direct Investment Financial Flows: Outward: USD: Total: Mining and Quarrying data is updated yearly, averaging 0.767 USD mn from Dec 2014 (Median) to 2020, with 5 observations. The data reached an all-time high of 2.052 USD mn in 2015 and a record low of -2.136 USD mn in 2014. Lithuania LT: Foreign Direct Investment Financial Flows: Outward: USD: Total: Mining and Quarrying data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Financial Flows: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle).; Under the directional presentation , the direct investment flows and positions are organised according to the direction of the investment for the reporting economy-either outward or inward . So, for a particular country, all flows and positions of direct investors resident in that economy are shown under outward investment and all flows and positions for direct investment enterprises resident in that economy are shown under inward investment. The directional presentation reflects the direction of influence. For more details, see a complete note on ' Asset/liability versus directional presentation '; FDI financial flows are cross-border transactions between affiliated parties (direct investors, direct investment enterprises and/or fellow enterprises) recorded during the reference period (typically year or quarter). FDI positions represent the value of the stock of direct investments held at the end of the reference period (typically year or quarter). The change in direct investment positions from one period to the next is equal to the value of financial transactions recorded during the period plus other changes in prices, exchange rates, and volume. FDI income data are closely linked to the stocks of investments and are used for analysis of the productivity of the investment and calculation of the rate of return on the total funds invested. The main financial instrument components of FDI are equity and debt instruments. Equity includes common and preferred shares (exclusive of non-participating preference shares which should be included under debt), reserves, capital contributions and reinvestment of earnings. Dividends, distributed branch earnings, reinvested earnings and undistributed branch earnings are components of FDI income on equity . Reinvested earnings and reinvestment of earnings are separately identified components of equity in FDI income data and in FDI financial flows. Debt instruments include marketable securities such as bonds, debentures, commercial paper, promissory notes, non-participating preference shares and other tradable non-equity securities as well as loans, deposits, trade credit and other accounts payable/ receivable.The interest returns on the above instruments are included in FDI income on debt .; FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .

The aim of this study is to evaluate an approach to reduce STIs among high-risk groups including men who have sex with men, female sex workers and mining populations in Dar es Salaam and Shinyanga regions of Tanzania. Primary research questions: 1) Is there a reduction in prevalence of laboratory confirmed STIs (a combined measure of gonorrhea, chlamydia and Treponema pallidum) in men who have sex with men (MSM) in Dar es Salaam and female sex workers (FSW) in Shinyanga after six months of presumptive treatment of STIs (PPT) availability? 2) Is there a reduction in prevalence of symptomatic STIs in male mine workers in Shinyanga after six months of PPT availability for FSW? Secondary research questions: 3) What associations are observed between demographics, socio-economic status, biological risk factors, and STI prevalence? 4) Are there changes in sexual risk taking behaviors while receiving PPT? 5) What is the prevalence of cervical cancer lesions, as assessed through Visual Inspection with Acetic Acid (VIA), in FSW?

SK: Foreign Direct Investment Position: Inward: USD: Total: Extraction of Crude Petroleum and Natural Gas: Mining Support Service Activities data was reported at -32.277 USD mn in 2018. This records a decrease from the previous number of -18.753 USD mn for 2017. SK: Foreign Direct Investment Position: Inward: USD: Total: Extraction of Crude Petroleum and Natural Gas: Mining Support Service Activities data is updated yearly, averaging -4.586 USD mn from Dec 2013 (Median) to 2018, with 4 observations. The data reached an all-time high of 173.499 USD mn in 2013 and a record low of -32.277 USD mn in 2018. SK: Foreign Direct Investment Position: Inward: USD: Total: Extraction of Crude Petroleum and Natural Gas: Mining Support Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Slovakia – Table SK.OECD.FDI: Foreign Direct Investment Position: USD: by Industry: OECD Member: Annual. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle).; Under the directional presentation , the direct investment flows and positions are organised according to the direction of the investment for the reporting economy-either outward or inward . So, for a particular country, all flows and positions of direct investors resident in that economy are shown under outward investment and all flows and positions for direct investment enterprises resident in that economy are shown under inward investment. The directional presentation reflects the direction of influence. For more details, see a complete note on ' Asset/liability versus directional presentation '; FDI financial flows are cross-border transactions between affiliated parties (direct investors, direct investment enterprises and/or fellow enterprises) recorded during the reference period (typically year or quarter). FDI positions represent the value of the stock of direct investments held at the end of the reference period (typically year or quarter). The change in direct investment positions from one period to the next is equal to the value of financial transactions recorded during the period plus other changes in prices, exchange rates, and volume. FDI income data are closely linked to the stocks of investments and are used for analysis of the productivity of the investment and calculation of the rate of return on the total funds invested. The main financial instrument components of FDI are equity and debt instruments. Equity includes common and preferred shares (exclusive of non-participating preference shares which should be included under debt), reserves, capital contributions and reinvestment of earnings. Dividends, distributed branch earnings, reinvested earnings and undistributed branch earnings are components of FDI income on equity . Reinvested earnings and reinvestment of earnings are separately identified components of equity in FDI income data and in FDI financial flows. Debt instruments include marketable securities such as bonds, debentures, commercial paper, promissory notes, non-participating preference shares and other tradable non-equity securities as well as loans, deposits, trade credit and other accounts payable/ receivable.The interest returns on the above instruments are included in FDI income on debt .; Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Market value, Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.

Dr Andy Barnicoat's presentation at the China Mining Conference 2012 in Tianjin.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

This presentation and accompanying Excel data, provides an coal cost curve for 270 mines, with breakdown of costs into mining, processing, admin, royalties. Specific datasets include: Global Mine Level Cost Costs Global Company Equity Production Costs Country level Production Costs Peabody Cost Curve. Read More

This package contains presentations given during NT Resources week, at the Uncovering East Tennant workshop held in Darwin on September 3, 2019, and Mining the Territory, September 5, 2019. The presentation given by Andrew Heap at the Mining the Territory forum is a high level overview of the data collection and activities of GA and it's collaborative partners across Northern Australia in conjunction with the Exploring for the Future (EFTF) program. The workshop, held in collaboration with the Northern Territory Geological Survey, outlined new mineral exploration opportunities in the East Tennant area, which lies beneath the Barkly Tableland and extends approximately 250 km east of Tennant Creek. The East Tennant area has been the focus of geochemical, geological and geophysical data acquisition as part of Geoscience Australia's Exploring for the Future program. This free event showcased new science insights for the East Tennant area and how this under-explored region has opportunities for greenfield mineral discoveries.

This dataset was developed to provide mineral resource data for the region of northeast WA for use in future spatial analysis by a variety of users.

This database is not meant to be used or displayed at any scale larger than 1:24,000

This report is a tabular presentation of mineral activities for mining and exploration in Washington during 1985 to 1997. The data may be incomplete as it depended on published data or data volunteered by operators.

economic importance of the mining industry in the German Empire (Deutsches Reich); annual official mining statistics on federal states level of the German Empire (Deutsches Reich); In the available work the facts of the mining industry are summarized: the mining companies, the smelting companies, and further enterprises associated with these companies, processing the raw materials of the mining and smelting industry are analyzed. The presentation is not limited to the statistics of the products of the mining industry (production statistics), but also includes foreign trade (imports and exports), which documents the consumption of mining and smelting products under consideration of the production statistics within the German Empire territory (consumption statistics). Timeseries: Subjects for which timeseries are available in the search- and downloadsystem HISTAT (Topic: Produktion: Bergbau, verarbeitendes Gewerbe, Industrie (Production: mining, manufacturing, industry) ) Information on the data-version: Version 2.0.0; Date of change: 01.08.2010. Addition to the tables of subdivision D; Completely new included subdivisions: E, F, G, H, I, K, L, M, N, and O. Information on the contents of the subdivisions, please see the list below. A.1 Gewinnung der wichtigsten Bergwerks-, Salinen- und Hüttenerzeugnisse im Deutschen Reich und in Luxemburg (Extraction of the most important products from mining, salt mines and metallurgical products in Germany and in Luxembourg). A.2 Außenhandel der wichtigsten Montanerzeugnisse: Gesamtübersicht nach dem Wert in Millionen Mark. (Foreign trade of the main mining products: overview by values in million marks.) B. Die Steinkohlenindustrie (hard coal industry). C. Die Braunkohlenindustrie (brown coal industry). D. Die Eisenindustrie (iron industry). E. Die Erölindustrie (crude oil industry). F. Die Asphaltindustrie (asphalt industry). G. Die Graphitindustrie (graphite industry). H. Die Blei – Silber -Zink – Industrie (lead, silver, zinc industry). I. Die Arsen – Kupfer – Gold – Industrie (arsenic, copper, gold industry). K. Die Zinnindustrie (tinn industry). L. Nickelindustrie (nickel industry). M. Die Schwefelindustrie (sulfur industry). N. Die Salzindustrie (salt industry). O. Kalisalze (potash salt) Die ausführliche Statistik der deutschen Bergbauproduktion von Kurt Flegel und M. Tornow („Montanstatistik des Deutschen Reiches“, Berlin 1915) soll die Entwicklungsgeschichte der deutschen Montanindustrie widerspiegeln. Aufgrund der wirtschaftlichen Bedeutung der Montanindustrie wurden im Deutschen Reich in den größeren Bundesstaaten jährliche Montanstatistiken herausgegeben, während die Reichsstatistik auf nur wenigen Seiten in den Vierteljahresheften zur Statistik des Deutschen Reiches die Montanindustrie behandelt. Auf der „Montanstatistik des Deutschen Reiches“ von Flegel/Tornow beruhen die einschlägigen Teile der Untersuchung über „Das Wachstum der deutschen Wirtschaft seit der Mitte des 19. Jahrhunderts“ von Walter G. Hoffmann u. a. (Berlin – Heidelberg - New York 1965). Die „Montanstatistik des Deutschen Reiches“ behandelt den Zeitraum von 1860 bis 1912. Bei den veröffentlichten statistischen Übersichten stand die Erarbeitung von Überblicken für das Deutsche Reich im Vordergrund. Daher sind in dieser Veröffentlichung lange Reihen zu Einzelregionen nur vereinzelt anzutreffen. (siehe in HISTAT die ausführliche, regional tief gegliederte Studie „Statistik der Bergbauproduktion Deutschlands 1850 – 1914“ aus der Reihe „Quellen und Forschungen zur Historischen Statistik von Deutschland“, Band 8 (Herausgegeben von Wolfram Fischer, St. Katharinen 1989), Studiennummer: ZA8448. In der vorliegenden Arbeit wird unter Montanindustrie zusammengefasst: die bergbaulichen Betriebe, die Hüttenbetriebe und die mit diesen verbundenen, die Rohstoffe des Bergbaus weiterverarbeitenden Betriebe, z.B. die Kokereien, Steinkohlenteerdestillationen, Braunkohlenschwelereien, Salinen, Chlorkaliumfabriken. Die Gliederung der Statistiken ist der amtlichen Statistik angepasst. Danach werden zunächst behandelt: (1) Die bituminösen Mineralien; Steinkohle, Braunkohle, Erdöl, Graphit, Asphalt. (2) die Erze und Metalle, einschließlich Weiterverarbeitung des Roheisens. (3) Die Salze. Die Darstellung beschränkt sich nicht nur auf die Statistik der Erzeugnisse der Montanindustrie (Produktionsstatistik), sondern umfasst auch den Außenhandel (Einfuhr und Ausfuhr), der unter Berücksichtigung der Produktionsstatistik innerhalb des deutschen Zollgebiets den Verbrauch an Erzeugnissen der Montanindustrie erkennen lässt (Verbrauchsstatistik). Eine besondere Schwierigkeit für die vorliegende Darstellung liegt darin, dass das statistische Zahlenmaterial sich nicht einheitlich zurückverfolgen lässt. In der Produktionsstatistik ist im Jahr 1912, in der Außenhandelsstatistik am 1. März 1905, eine derartig tief eingreifende Änderung erfolgt, dass sich die Zahlen mit denen der Vorjahre nicht unmittelbar vergleichen lassen. Datentabellen in HISTAT Hinweise zur neuen Version: Version 2.0.0: Datum der Änderung: 01.08.2010. Ergänzung der Tabellen zur Untergliederung D; Vollständig neu aufgenommene Untergliederungen E, F, G, H, I, K, L, M, N und O. Zu den Inhalten der Datentabellen siehe die nachfolgende ´Untergliederung der Studie´. A.1 Gewinnung der wichtigsten Bergwerks-, Salinen- und Hüttenerzeugnisse im Deutschen Reich und in Luxemburg. A.2 Außenhandel der wichtigsten Montanerzeugnisse: Gesamtübersicht nach dem Wert in Millionen Mark. B. Die Steinkohlenindustrie. C. Die Braunkohlenindustrie. D. Die Eisenindustrie. E. Die Erölindustrie. F. Die Asphaltindustrie. G. Die Graphitindustrie. H. Die Blei – Silber -Zink – Industrie. I. Die Arsen – Kupfer – Gold – Industrie. K. Die Zinnindustrie. L. Nickelindustrie. M. Die Schwefelindustrie. N. Die Salzindustrie. O. Kalisalze Quellen: Veröffentlichungen des Kaiserlichen Statistischen Amtes: A. Statistik des Deutschen Reiches: - Erste Reihe, Bd. I – LXIII. Berlin 1873-1883. Fortgesetzt als: - Neue Folge, Bd. 1 – 149 (1884 – 1903). - Ohne den Zusatz ´Neue Folge´: Bd. 150fg. (1903fg). Darin regelmäßig jährlich: Auswärtiger Handel (2 Bände). B. Vierteljahreshefte zur Statistik des Deutschen Reichs seit 1892. C. Monatliche Nachweise über den auswärtigen Handel Deutschlands nebst Angaben über Großhandelspreise, deutsche See- und Bodenseefischerei und Handel der deutschen Schutzgebiete seit 1892. D. Statistisches Jahrbuch für das Deutsche Reich, Teil 1 und 2, Berlin 1907.