Descartes Labs, a New Mexico-based geospatial analytics company, yesterday announced the availability of the Descartes Labs Platform, a cloud-based geospatial analytics platform. It provides enterprises with a real-time geospatial data catalog and flexible modeling environment in one complete package.
By handling nearly all geospatial modeling functions in the cloud, organizations can quickly evaluate the output of models, speed development and proof-of-concept creation.
This modeling tool enables forecasting capabilities across industries, including agriculture, energy, sustainability, mining, shipping, financial services, and insurance, to facilitate everything from agricultural monitoring to mineral exploration. This is especially critical for commodity-focused companies facing sustainability and efficiency challenges.
The Descartes Labs platform offers global-scale geospatial data for organizations that have never been able to access it before, opening up possibilities to add artificial intelligence (AI) as a new core competency. By removing the barriers to geospatial data science, the potential use cases for both sustainability are expanded.
“The Descartes Labs Platform was built to model the physical world. Predictive models empower business leaders to save on costs and implement novel strategies to address climate and sustainability challenges,” said Phil Fraher, CEO of Descartes Labs. “The Descartes Labs Platform enables teams to rapidly innovate on petabyte-scale data sets and leverage exquisite geospatial data in a matter of hours instead of days or weeks.”
The platform exposes three primary components that work in tandem to accelerate productivity across the key functions of IT, engineering, data science, and business leadership:
Petabytes of analysis-ready geospatial data with the ability to rapidly ingest, clean, calibrate and benefit from any internal or third party data source.
Cloud-based data science environment that combines the Descartes Labs Platform APIs, visualization tools, and a model repository with a hosted JupyterLab interface.
The ability to rapidly deploy models and applications to produce valuable insights and generate ROI across multiple vertical and horizontal use cases.
Built in Python with native AI, machine learning tools, and petabytes of analysis-ready data, customers can use the platform to develop location-based computer vision and machine learning models on top of curated and custom datasets. The platform provides access to Descartes Labs’ APIs including Catalog, Raster, Scenes, Tasks, Workflows, Monitoring and more. This enables the rapid and collaborative development of global-scale commodity and earth systems analytics.
Image: NASA Image of Nanjing, China