“Covid is ushering in a new era of policies aimed at social need instead of financial stability which will likely create cyclically stronger, more commodity-intensive economic growth, that should create the elusive cyclical upswing in demand.”
— Goldman Sachs, Commodities Outlook 2021, Nov. 18, 2020
Ever since the launch of Landsat 1 in 1972, remote sensing has been used by the mining industry to map surface geology and alteration minerals associated with ore deposits of interest. Despite its usefulness, mineral targeting workflows have struggled to scale beyond individual project areas. …
By Sepideh Khajehei — Sepideh is an Applied Scientist at Descartes Labs working on the Forestry & Climate team. She has a Ph.D. in Civil and Environmental Engineering from Portland State University with domain expertise in hydrology, weather and climate risk, and natural hazard prediction. With collaboration by Alex Diamond, Julio Herrera Estrada, and Jason Schatz.
In December 2019, Goldman Sachs issued an environmental policy framework that halted the company’s financing of deals in environmentally damaging industry sectors. Carbon-intensive projects like coal-fired power generation, mountaintop removal mining, and arctic oil and gas exploration would be avoided in favor of projects…
By Terry Conlon — Ph.D. Student in the Sustainable Engineering Lab at Columbia University. Terry completed the following work as a Descartes Labs intern this summer, where he worked with Rose Rustowicz to apply deep learning methods to timeseries of satellite imagery.
Despite numerous advances in the collection and distribution of satellite imagery — including those that allow sensing at sub-meter resolutions, rapid orbital periods, and public access to petabytes of data — one fundamental problem remains: how do you deal with clouds? …
By Christopher Ren — Chick Keller Postdoctoral Fellow in the Space Data Science & System Group, Intelligence and Space Research Division (ISR-3) at Los Alamos National Laboratory. Chris’s research interests include anomalous change detection, multi-sensor data fusion, remote sensing, and machine learning.
One of the techniques developed at the Intelligence and Space Research Division at Los Alamos National Laboratory to analyze satellite imagery time-series is known as ‘anomalous change detection’ (ACD). In the ACD problem setting, we point the algorithm towards a pair of images taken at the same location but at different times and ask the question “which pixels/regions…
We’re excited to announce the release of our Economic Activity Signals (EAS) quick-start package — a collection of geographic data feeds that track real-world behavior to help estimate daily changes in supply, demand, distribution, and the operational state of facilities on a global scale.
The package helps data science, procurement, sourcing, and business line leaders in agriculture, CPG, mining, materials, and industrials implement agile and resilient strategies using near-real-time data. By cleaning and combining premium vector datasets, EAS captures the following signals:
In January 2020, we granted access to the Descartes Labs Platform API and data refinery for our first round of academic and non-profit researchers. This first cohort is now a few months into their year of free platform access, and we are ready to accept applications for our second round of research projects beginning this summer.
We believe that we’ve built something special at Descartes Labs. Our platform allows for rapid iteration and hypothesis testing, along with easy and efficient scaling across large geographies. But don’t take our word for it. …
In 2018, approximately 360,500 acres burned across our headquarters’ state of New Mexico. After a winter without snow, the wildfire season brought 1,334 fire starts in the state that year. With the effects of climate change and the present-day consequences of historical forest management policies hitting us so close to home, we set out to build an automated, early-warning wildfire detector using satellite data.
Today, we’re excited to announce that Descartes Labs will utilize our wildfire detector to alert the state’s Energy, Minerals, and Natural Resources Department’s Forestry Division of wildfires in real-time. …
Updated with aggregated mobility data from Friday, June 5th, 2020
Review our aggregated mobility tracking methodology in our technical paper: Mobility Changes in Response to COVID-19. We are making views of this mobility data freely available at the US admin1 (state) and admin2 (county) level under a Creative Commons Attribution (CC BY 4.0) license at the following GitHub repository.
Coronavirus is having a profound impact on everyday life — canceled schools, closed restaurants, working at home, no gatherings of more than 10 people, etc. — with one commenter saying “it’s like we’re pushing the pause button on the economy.”
At Descartes Labs, we’ve been working at scale for years to solve some of the world’s hardest and most important geospatial AI challenges. Our custom solutions have helped global enterprises transform their physical supply chains to become more efficient and profitable.
After years of refinement, we’re now making our platform and tools publicly available so that customers can build models to transform businesses more quickly, efficiently, and cost-effectively.
The Descartes Labs Platform is made up of three components that work together to accelerate productivity across IT, engineering, data science, and business leadership.
By Phil Fraher and Mark Johnson
Five years ago, a group of brilliant scientists from Los Alamos National Laboratory founded Descartes Labs with the bold vision to transform massive global datasets into useful information so that we can be better stewards of our planet’s natural resources.
Early on, many told us that we could never build a company in New Mexico. And we fully believed that we would follow the path of many tech startups and move out to the Bay Area. Boy, were we wrong.
Our only non-Los Alamos National Lab cofounder, Mark Johnson, brought critical early-stage experience in…
Descartes Labs is building a data refinery for geospatial data.