White Paper

GIS Data Annotation for Mining

The resource extraction sector has become the biggest consumer of GIS and geospatial data, and data annotation specialists have stepped up to fill a key role enriching GIS images and unearthing ground truth data. Companies are saving upto 80% while locating new mines using big data, as compared to traditional methods. Many mining operations now routinely monitor mine output, and corresponding safety threats based on ambient temperature changes, changes in flow rates, weather changes, and even dust propagation.

Download the White Paper to:

  • Understand the scope of data requirements for mining related analysis
  • Know how mining companies have become among the most frequent users of geospatial data with two primary use cases: exploration and environment protection activities
  • See how GIS data annotation can help in resource-estimation projects
  • Explore ways in which federal and state agencies analyze GIS data

First 300 words:

A series of cylindrical objects sit in the Middle East desert as the afternoon sun falls steadily to the horizon. Far overhead a satellite snaps a series of  high-resolution images of the ground and facilities below. And somehow, in the days to come, a team of data analysts will examine those images and determine that the objects are oil storage tanks – and, after annotating hundreds of similar objects across the globe, that the world supply of oil within has run unexpectedly low.

It takes a highly trained pair of eyes to look at a series of seemingly abstract objects, discern the shape of storage tanks, take into account the shadowing from the time of day and angle of the sun, and subtle changes in shape that together present a compelling set of clues to the ground truth below. Fed enough images and correct modeling, a machine algorithm can eventually match that performance – but only after that expert human has developed the necessary training data to educate the machine equivalent of a three-year-old child and take it through graduate school.

Oil, gas, minerals, metal ore – that combined resource extraction sector has in recent years become an ever-hungrier consumer of GIS and other geospatial data – in terms of the breadth, depth, and frequency of that data collection and analysis. The insatiable appetite for geospatial data should come as little surprise; few industries are as closely connected to spatial data as mining, where data requirements span both above and below ground data – everything from high resolution satellite imagery to underground sensor data and surface GIS mapping.