The overall accuracy, calculated from 1, stratified random points is This novel data set can help improving environmental impact assessments of the global mining sector, for example, regarding mining-induced deforestation or fragmentation and degradation of ecosystems.

It can also serve as a benchmark for further monitoring the temporal evolution of mining sites around the world and as training and validation data to support automated classification of mines using satellite images. We produced the global-scale data set on mining areas by visual interpretation of satellite images. This remote sensing technique is precise but also costly and time-intensive.

To make the visual interpretation viable on a global scale, we defined regions of interest ROI based on the SNL Metals and Mining database This was important to reduce the time spent inspecting the satellite images and delineating the mining extents.

Automated post-processing was also applied to check and correct possible invalid polygon geometries 34 , for instance polygons with self-intersections. We defined our ROI as a buffer around the geographical coordinates georeferenced points of active mines reported in the SNL Metals and Mining database The SNL database provides production information on more than 35, mines across the globe.

Among many other variables, SNL reports the approximate geographic coordinates of the extraction sites, from which we selected all mines reporting activity i. This subset added up to 6, mining locations extracting 76 different commodities, with a focus on coal, metal ores and industrial minerals.

Note that many mines, particularly regarding metal ore extraction, report more than one commodity in the SNL database see full list in Table 1. The buffer around the selected SNL mines was necessary to increase the efficiency and systematize the interpretation of the satellite images. The radius of the buffer should be as small as possible and cover all mining ground features, including open cuts, tailings dams, waste rock piles, water ponds, and processing infrastructure.

The polygons were delineated by two trained experts using an open-source web application 35 developed for this specific purpose. The web interface systematically displays buffers and markers with information about the mines.

As background, the app offers three options of satellite layers: Google Satellite, Microsoft Bing Imagery, and Sentinel-2 cloudless These images allow identifying ground features related to mines with high confidence 9. However, these data sources do not cover the whole globe with the same spatial resolution and contain out-of-date images for some regions The Sentinel-2 cloudless provides a mosaic built from Sentinel-2 images taken during the years and Combining these data layers, the experts identified and delineated the ground features related to mining.

All three satellite data sources were visually inspected before delineating the polygons. The majority of the inspected locations had at least two sources of clear images e.

Only very few locations lacked images with sufficient quality to draw the polygons, for example, due to cloud cover or low spatial resolution.

We used the source showing the largest mining extent for the delineation of the areas. This premise was taken because the largest extent of a mine is usually stable for several years as a long lifespan is intended due to economic reasons.

Besides, mining areas generally increase and could only reduce through ecological restoration, which can take a long time These conjectures do not ensure the temporal consistency of all delineated extents but helped to capture the largest and most up-to-date extent of the mines according to the available satellite images within our ROI. In some cases, the mining polygons can also extend beyond the ROI. Mining features intersecting the buffer borders were delineated to account for their full extent, even if they extend beyond the buffer limits.

Moreover, the mining polygons can contain isolated patches with forest or other land covers, which do not necessarily represent any mining feature on the ground.

These patches were included because we aim at accounting for the total area used by mining, including isolated spare areas that most probably cannot have other uses.

The delineated polygons do not distinguish the different ground features within the mines, i. As a final product from the delineation we obtained a set of polygons covering the total land used by mining within the ROI.

We applied geospatial and geometric operations to check and correct the raw data collection. This geoprocessing was performed to avoid double counting of mining areas, correct invalid geometries, and add attributes variables to the polygons. To avoid double-counting, we dissolved polygons that possibly overlapped or shared a common boundary, i.

After that, we removed sliver polygons unwanted small polygons and invalid polygon geometries, producing a consistent set of polygons. From this set of preprocessed polygons, we calculated the area of each feature and added information on the country where each polygon is located. We calculated the area in square kilometers by projecting each polygon to its respective Universal Transverse Mercator UTM zone.

The final set of polygons thus includes the geometries polygons covering the mining areas, their respective areas in square kilometers, country name, and ISO alpha-3 code of the corresponding country. This is useful because many modeling applications require standardized grid data The 30 arcsecond grid was derived from the percentage of area of the geometric intersection between each cell and the geometries of the mining polygons. These percentages were rounded to zero decimal digits to reduce the size of the data set.

Therefore, the percentage of the cell covered by mine should be greater than 0. To obtain the gridded mining area, we estimated the area of each cell in square kilometers and multiplied with the percentage of mining cover per cell, resulting in a 30 arcsecond global grid indicating the mining area within each cell. The 5 arcminute and 30 arcminute grid resolutions were downsampled form the 30 arcsecond grid.

All scripts used in the geoprocessing of data records are available with our open-source web application tool Our data records provide spatially explicit information on the direct land use of mining activities. The main data set consists of 21, mining polygons covering the extents of mining sites worldwide Grid data derived from the polygons is available at 30 arcsecond , 5 arcminute , and 30 arcminute spatial resolution, providing a ready-to-use data set for modeling purposes with the mining area in square kilometers per grid cell.

Figure 1 illustrates how the satellite images were used to delineate the mining extent. In this example, the area is used for coal mining in Mackenzie River, Queensland, Australia. The polygon in Fig. The Sentinel-2 cloudless mosaic is composed by images from the years and 33 while Microsoft Bing Fig.

Nevertheless, all three data sources contributed to providing pieces of evidence of mining in the mapped area. An example polygon delineated over a coal mine in Mackenzie River, Queensland, Australia.

a Shows the delineated polygon in purple and b shows the Sentinel-2 cloudless mosaic composed by images from the year 33 used to delineate the mining extent. c Shows a Microsoft Bing image from July and d a Google Satellite image from December The delineated polygons cover all infrastructure and land cover types directly related to mining activities.

This can produce large polygons, such as in the case of the Salar de Atacama , Chile. Figure 2 shows the delineated polygon extent and a detailed view of one of the mining plants. We decided to map the whole area because the mining plants, in fact, have brine pumping and monitoring wells spreading over the entire salt flat far beyond the actual evaporation ponds Alternative assumptions mapping only the evaporation ponds estimated an area of only However, it is important to note that the case of Salar de Atacama was rather isolated; in most cases, no features such as pipelines and wells outside the main mining sites could be identified from the available satellite images.

Mine on the Salar de Atacama salt flat, Chile. The purple polygon on the left side was derived from the Sentinel-2 images shown in the background. The polygon covers all infrastructure spread over the salt flat, including water pipelines, wells, and the actual mining plants.

The zoom boxes on the right side show Google Satellite images with a detailed view of water pipelines and wells over the salt flat as well as one of the mining plants. In many cases, mines are located following the structure of mineral deposits, making it easy to map them from satellite images. We selected three mines to illustrate these large-scale concentrated activities Fig. The first example Fig. Figure 3b shows the Batu Hijau copper-gold mine. Despite its large open cut, this mine does not use much area for unused material, as its tailings disposal takes place in the ocean The third example is the Super Pit gold mine in Australia, Fig.

This mine is located in one of the largest gold producing regions in the world. In the case of these large mines, coordinates reported in the SNL database were accurate.

Examples of mapped mining polygons with Google Satellite images background. a Carajás iron ore mine in Brazil, b Batu Hijau copper-gold mine in Indonesia, and c Super Pit gold mine in Australia. Contrasting to the above examples, in other regions the reported coordinates were of lower accuracy.

Figure 4 , for example, shows a large area with widely spread coal mining activities in East Kalimantan, Indonesia. The SNL database reports some mining locations in this region, however, they do not always spatially intersect the mining areas mapped from the satellite images.

Coal mining polygons in East Kalimantan, Indonesia, overplayed with the Sentinel-2 Cloudless images form the year provided by EOX Figure 5 shows an overview of the geographical distribution of our mapped mining area across the globe. From this figure we can see concentrations of mining areas in many regions, for example, in northern Chile mainly due to copper extraction and northeastern Australia and East Kalimantan in Indonesia because of coal mining.

The map at the top shows the global distribution of the mapped mining area. The maps at the bottom are zoomed to South America, Australia, and parts of South-East Asia.

These results show that mining areas are highly concentrated in only a few countries. However, it is worth mentioning that our polygons could be biased by the activities reported in the SNL database and could mask countries and commodities that are poorly reported.

For most African countries, however, SNL extraction of metals compares well to the national aggregates. Percentage of mining area mapped per country. Countries have different profiles regarding the spatial distribution of the mines. However, they vary with respect to the number of identified polygons, 5, and 1,, respectively. This discrepancy in the number of mining locations can be related to the high importance of the small-scale mining industry in China 45 , 46 , while Australia is characterized by fewer, large-scale mines Figure 7 displays the relationship between the mapped area and the number of polygons on a country level.

Most of the variation in mining area can be explained by a linear relationship to the number of polygons. A complete summary of the mining area mapped per county is shown in Table 2 and available from download with our data records Relationship between the mapped mining area and the number of features polygons on a country level. The solid line summarizes the relationship between area and number of features for the complete data set, the dashed line excludes China.

Our mining data set accounts for all land cover types related to mining that could be identified from the satellite images. However, it does not distinguish the different features within the polygons.

For example, we could not separate mining from quarry, because this would require additional information other than the satellite images. Although our data set does not cover all existing mines, to date, it is the most comprehensive database on mining extents openly available.

The data set can help filling existing gaps for spatially explicit mineral extraction assessments on a global scale. It opens up opportunities to improve environmental pressure and impact indicators of the mining sector and can support the development of automated systems to monitor mining sites worldwide. The precision of the delineated mining borders can vary according to the satellite data source and the location.

In general, the satellite sources used in this work provide sufficient spatial resolution and georeferencing accuracy to map mining areas 9. Images available from Google Earth, for instance, have an overall positional root mean squared error RMSE of These errors are acceptable for global scale environmental assessments.

The visual interpretation of satellite images depends on the previous knowledge of the perceiving person. The ground features related to mining are not always easy to identify in the satellite images and can be subject to the judgment of the person that delineates a particular mine.

For that reason, we obtained a second independent classification for a set of random points. These validation points were inspected independently by experts that did not participate in the delineation of the mines. They classified these validation points as mine or no-mine based on the three satellite data sources without information whether or not the points were originally mapped as part of a mining areas. The validation points are also part of our data records The overall agreement between the mapped areas and the validation points was In our case the mapped mining areas have An alternative way to visualize the accuracy of our data set is the Receiver Operating Characteristic ROC probability curve.

The graph in Fig. For our classification, the point is near the upper-left corner of the ROC curve, meaning that the classification performs well a perfect classifier would reach the point 0, 1. Besides, the area under the curve AUC in Fig.

Receiver Operating Characteristic ROC derived from 1, random points equally allocated between the mapped classes mine and no-mine. Looking at the spatial distribution of the validation points, we found that half of the points with disagreement i. On the other hand, of the points with an agreement i.

This shows that higher uncertainty lies on the borders of the delineated extents as it can be expected due to the use of several satellite data sources with different precision. These results also indicate that we have high confidence in the existence of mines within the mapped polygons. The global mining data set described here is available from PANGAEA under the license Creative Commons Attribution-ShareAlike 4. The data records include the mining polygons, validation points, mining area grid, and a summary of the mining area per country.

The mining polygons and validation points are encoded in GeoPackage geographic data structures 51 , such as:. The mining grids include a single layer one band raster encoded in Geographic Tagged Image File Format GeoTIFF Each grid cell over land has a float number data type Float32 greater than or equal to zero representing the mining area in square kilometers; grid cells over water have no-data values.

The summary of the mapped mining area per country derived from the mining polygons is available in Comma-separated values CSV 53 format, including four attributes:. Our spatially explicit data records can be combined with other geographical data to perform further statistical analysis, for example, to test spatially stratified heterogeneity 54 and non-stationarity of variables 55 , For that, users can open the data records using software that support Geographic Information System GIS , including, QGIS 57 , R 58 , and Python Besides, we also provide a tool for visual analysis of the geographical data records at www.

All the code and geoprocessing scripts used to produce the results of this paper are distributed under the GNU General Public License v3. The processing scripts were written in R 58 , Python 59 , and GDAL Geospatial Data Abstraction Library The web application to delineate the polygons was written in R Shiny 63 using a PostgreSQL 64 database with PostGIS 65 extension for storage.

The full app setup uses Docker 65 containers to facilitate management, portability, and reproducibility. The web application supports the delineation of areas from the satellite images layers. It systematically displays the regions of interest e.

Note that mining coordinates are not part of the web application and must be fed into the database by the user. To learn more about the application setup see www. The current version of app provides image layers from Sentinel-2 Cloudless 33 , Google Satellite, and Microsoft Bing Imagery. Further sources of satellite images can be added to the application via WMS.

Giljum, S. Global patterns of material flows and their socio-economic and environmental implications: A MFA study on all countries world-wide from to Resources 3 , — Article Google Scholar. IRP, U. Global Resources Outlook Natural Resources for the Future we Want. A Report of the International Resource Panel.

Report No. Krausmann, F. Material flow accounting: Measuring global material use for sustainable development. Calvo, G. Decreasing ore grades in global metallic mining: A theoretical issue or a global reality?

Resources 5 Prior, T. Resource depletion, peak minerals and the implications for sustainable resource management. Change 22 , — West, J.

Decreasing metal ore grades. Mudd, G. Global trends in gold mining: Towards quantifying environmental and resource sustainability. Policy 32 , 42—56 Sonter, L. Processes of land use change in mining regions. Werner, T. Assessing impacts of mining: Recent contributions from GIS and remote sensing. Kobayashi, H. A global extent site-level analysis of land cover and protected area overlap with mining activities as an indicator of biodiversity pressure.

Mining and biodiversity: key issues and research needs in conservation science. Islam, K. Integrating remote sensing and life cycle assessment to quantify the environmental impacts of copper-silver-gold mining: A case study from laos.

Butt, N. et al. Biodiversity risks from fossil fuel extraction. Science , — Article ADS CAS Google Scholar. Murguía, D. Global direct pressures on biodiversity by large-scale metal mining: Spatial distribution and implications for conservation. Google Scholar. Endl, A. Policy Bruckner, M. Measuring telecouplings in the global land system: A review and comparative evaluation of land footprint accounting methods.

Schaffartzik, A. Trading land: A review of approaches to accounting for upstream land requirements of traded products. USGS — United States Geological Survey.

Measuring the specific land requirements of large-scale metal mines for iron, bauxite, copper, gold and silver. Global-scale remote sensing of mine areas and analysis of factors explaining their extent.

Change 60 Mountrakis, G. Support vector machines in remote sensing: A review. ISPRS J. Belgiu, M. Random forest in remote sensing: A review of applications and future directions.

Zhu, X. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geosc. Wulder, M. Land cover 2. Harness the creativity of the entire team, citizens to experts. Maintain transparency, security, version control, and auditability. Combine AutoML , intuitive drag-and-drop workflows, and embedded Jupyter Notebooks that make creating and sharing reusable modules easy. Run workflows from Spotfire analytics to bring ML, data, processes, and people together to create operational solutions.

Cloud services, frameworks, and open source technologies like Python and R can be complex and overwhelming. TIBCO Data Science software simplifies data science and machine learning across hybrid ecosystems.

Use TensorFlow, SageMaker, Rekognition, Cognitive Services, and others to orchestrate the complexity of open source and create innovative solutions. Learn more or fire up an AWS Instance today. Many organizations struggle to deploy analytics into production environments.

As data drifts and models decay, being able to monitor, retrain, remodel, and automatically deploy new analytic models at the edge or directly within business systems lets you understand and act on trustworthy results. Uncover new business opportunities, revenue streams, and asset monetization for a sustained competitive advantage.

To trust your analyses, start with trusted data. TIBCO provides extensive support for enterprise governance in industries like finance, healthcare, insurance, manufacturing , and pharma, including ISO , FDA 21 CFR Part 11 and GxP, GDPR, and CCPC. Automated analytical models with big data machine learning algorithms iteratively learn from data and optimize performance. Let your computers find new patterns and insights without explicitly programming them where to look.

Learn how to handle wide data. A drag-and-drop interface allows easy creation of data prep, analytic, and scoring pipelines. Share and annotate data, scripts, models, and workflows. Model comparison with champion-challenger testing.

Deploy workflows and set to run on schedule. Push PMML or PFA real-time scoring to Cloud Foundry, AWS, or Google App Engine. Build models on EMR or Redshift and deploy on-premises to Oracle or Teradata. Use role-based security for any asset within the system. Built-in version control, audit logs, and approval processes. Analytic pipelines extended by seamlessly integrating with Amazon, Azure, and Google ecosystems along with Python, R, Jupyter Notebooks, C , and Scala.

Create custom operators that can be reused across your organization and run directly in-database, in-cluster, or at the edge. TIBCO Logo. Innovative customers. Tangible business impact. Learn how 75 companies across 15 industries are using our Connected Intelligence platform. Download Ebook. Share your story Explore success stories. Manufacturing Intelligence Manufacturing intelligence for the modern digital factory Learn More. Download EBook Watch Video. Learn More. Start Free Trial TIBCO Cloud Sign In.

TIBCO Partner Program Guide. Accelerating Customer Success Through Collaboration. Download Guide. Become a Partner Already a Partner? Sign In.

Who is TIBCO. Watch Now. Customer Success Stories TIBCO Platform Our products in action. Explore Opportunities. We strive to make a difference while doing work we are passionate about.

Create the future you want and join us today. View Jobs. TIBCO Logo Customers ×.

Data mining refers to extracting or mining knowledge from large amounts of data. In other words, data mining is the science, art, and technology of discovering large and complex bodies of data in order to discover useful patterns. Theoreticians and practitioners are continually seeking improved techniques to make the process more efficient, cost-effective, and accurate. Any situation can be analyzed in two ways in data mining:.

There are various statistical terms that one should be aware of while dealing with statistics. Some of these are:. This is the analysis of raw data using mathematical formulas, models, and techniques. Through the use of statistical methods, information is extracted from research data, and different ways are available to judge the robustness of research outputs. These techniques are taught in science curriculums. It is necessary to check and test several hypotheses. The hypotheses described above help us assess the validity of our data mining endeavor when attempting to infer any inferences from the data under study.

When using more complex and sophisticated statistical estimators and tests, these issues become more pronounced. For extracting knowledge from databases containing different types of observations, a variety of statistical methods are available in Data Mining and some of these are:. The first step in creating good statistics is having good data that was derived with an aim in mind. There are two main types of data: an input independent or predictor variable, which we control or are able to measure, and an output dependent or response variable which is observed.

A few will be quantitative measurements, but others may be qualitative or categorical variables called factors. Skip to content. js Blaze UI JavaScript Libraries jQuery jQuery Mobile jQuery UI jQuery EasyUI jQWidgets ReactJS React Bootstrap React Rebass React Desktop React Suite ReactJS Evergreen ReactJS Reactstrap Ant Design BlueprintJS p5. js Lodash TensorFlow.

js Moment. js Collect. Notes Ethics Notes Polity Notes Economics Notes UPSC Previous Year Papers SSC CGL SSC CGL Syllabus General Studies English Reasoning Subjectwise Practice Papers Previous Year Papers Banking Exams SBI Clerk SBI Clerk Syllabus General Awareness English Quantitative Aptitude Reasoning Ability SBI Clerk Practice Papers SBI PO SBI PO Syllabus General Awareness English Quantitative Aptitude Reasoning Ability Previous Year Papers SBI PO Practice Papers IBPS PO IBPS PO Syllabus English Notes Reasoning Notes Previous Year Papers Mock Question Papers IBPS Clerk IBPS Clerk Syllabus English Notes Previous Year Papers Jobs Apply for a Job Apply through Jobathon Post a Job Hire through Jobathon Practice All DSA Problems Problem of the Day Interview Series: Weekly Contests Bi-Wizard Coding: School Contests Events Practice SDE Sheet Curated DSA Lists Top 50 Array Problems Top 50 String Problems Top 50 Tree Problems Top 50 Graph Problems Top 50 DP Problems Contests.

Home Saved Videos Courses GBlog Puzzles What's New? Change Language. Related Articles. Improve Article. Save Article. Like Article. Difficulty Level : Expert Last Updated : 26 Jul, Read Discuss Courses Practice Video. Any situation can be analyzed in two ways in data mining: Statistical Analysis: In statistics, data is collected, analyzed, explored, and presented to identify patterns and trends. Alternatively, it is referred to as quantitative analysis.

Non-statistical Analysis: This analysis provides generalized information and includes sound, still images, and moving images.

In statistics, there are two main categories:. Please Login to comment Previous Data Cube or OLAP approach in Data Mining. Next Data warehouse development life cycle model. STING - Statistical Information Grid in Data Mining. Text Mining in Data Mining. Mining Collective Outliers Data Mining. Generalized Sequential Pattern GSP Mining in Data Mining. Frequent Pattern Mining in Data Mining. Methods For Clustering with Constraints in Data Mining.

Proximity-Based Methods in Data Mining. Difference Between Classification and Prediction methods in Data Mining. Graph Clustering Methods in Data Mining. Pattern Evaluation Methods in Data Mining. Article Contributed By :. Easy Normal Medium Hard Expert.

What's New. Improve your Coding Skills with Practice Try It! We use cookies to ensure you have the best browsing experience on our website. Start Your Coding Journey Now! Login Register.

Learn more or fire up an AWS Instance today. The map at the top shows the global distribution of the mapped mining area. SIGN IN Sign In. Support vector machines in remote sensing: A review. Develop End-to-End AI Solutions From data prep, to model build, to deployment and monitoring, TIBCO Data Science software allows organizations to automate the mundane and create business solutions fueled by machine learning ML algorithms that solve real world problems. Article ADS Google Scholar. Cloud services, frameworks, and open source technologies like Python and R can be complex and overwhelming.

Categories:

• Binary Options online
• Forex
• Binary Options
• Forex Brokers
• Forex Bitcoin