
DevGlobal and NASA’s Six-Year Initiative to Bring Together Humanitarians and Scientists
NASA Lifelines launched today to accelerate and improve humanitarian decision-making using Earth science. Led by the services firm
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NASA Lifelines launched today to accelerate and improve humanitarian decision-making using Earth science. Led by the services firm
DevGlobal’s Jeff Pituch and James Haithcoat recently lead an effort to capture the processes and lessons learned by each grant partner organization of the Patrick J. McGovern Foundation’s 2022 Climate + Data Accelerator Program.
DevGlobal Principal Consultant, James Haithcoat, spoke at St. Louis’s T-REX Nonprofit Technology Innovation Center as part of their
In support of PepsiCo’s end-to-end transformation strategy for sustainability in the agricultural sector, DevGlobal is facilitating the launch
DevGlobal leads community of practice for geospatial impact evaluations with award from Gates Foundation DevGlobal will support a
DevGlobal community, 2022 was a year of immense growth. Growth for us meant focusing in on our strengths
Get the Most Data for your Dollar – How the Pros Negotiate a Humanitarian License Fight for health
Agricultural practices and food systems play a major role in many pressing challenges, including climate change, nutritional security,
For those of us working in geospatially focused organizations, the applicability of using geographic information systems (GIS) to
The ramp project is working with the World Health Organization to map healthcare buildings, a tool that will support humanitarian emergency response teams in the likelihood of the next pandemic. In this blog post, we discuss the labeling process, answering a fundamental question: How can we ensure generating high-quality labels working with remote teams?
“What is good enough” is a living question that has evolved over the span of the ramp project. It starts at the end, in defining our use case and engaging end-users as advisors to get an idea of how the model outputs will be utilized. Working backward in this way has allowed us to craft our approach to establishing training data guidance and quality assurance . . .
The ramp project is an ambitious endeavor for many reasons, not the least of which is the curation and open release of high-quality training data in the form of tens of thousands of high-resolution imagery chips with accompanying labels that “teach” our machine learning model . . .