Geospatial analytics specialist Descartes Labs offers a unique and powerful analytics platform that provides its clients with a wide range of geospatial and nongeospatial data for visualisation and predictive analytics.
The information that is derived from this data can be computed, integrated and interpreted using the latest machine learning and artificial intelligence (AI) methods to provide statistical predictions to meet clients’ needs.
“As the process of mineral exploration generates a huge volume of data over time, managing this data is a challenge and remote sensing data is one of the largest most challenging datasets. Satellite and airborne imagery, or remote sensing, is a method of mapping the composition of the surface of the earth from a distance. It is one of the earlier components of the exploration process,” says Descartes Labs principal remote sensing consultant Lori Wickert.
Mining companies therefore use this data to highlight potential new mineral exploration targets.
Moreover, the mining company can differentiate the geological materials of the earth’s surface and search for specific alteration minerals which may be associated with ore deposits.
This method of exploration enhances timelines as Descartes Labs has successfully bypassed the entire process. Instead of the traditional method of remote sensing which comprises multiple and time-consuming steps, Descartes Labs has successfully loaded the entire archive of most of the satellite-borne datasets that could be of interest to all mineral exploration companies.
In terms of data, this includes everything from historic Landsat data, to the newest Landsat-8 sensor, ASTER, Sentinel-2 and many other datasets. There are also many other datasets such as geological, geophysical, environmental, agricultural and forestry available.
The advantages of these existing datasets are that they are driven by pre-computation and scalability, speed, accessibility and the ability to integrate the data and the results with the client’s data both during the initial exploration steps and well into the development stages.
Moreover, it is possible to load and interpret other datasets such as geophysics, geochemistry, structural data and geological mapping on the Descartes Labs platform. Clients can combine the results in novel and unique ways to quickly harness the power of machine learning and AI downstream in the exploration workflow.
This method allows clients to exponentially increase the number of mineral targets qualified and disqualified over any given time and over a variety of geographic scales.
“Compared with traditional consulting approaches, the platform offers 50% to 75% cost savings. The cost of the system varies by the number of people who would require access to the system and how much customisation is done by Descartes Labs to digitise the exploration workflow,” concludes Descartes Labs enterprise sales senior director James Orsulak.