The white paper explores a data ethics framework to preemptively address farmers’ concerns in low- and middle-income countries. We released a white paper exploring a data ethics framework for farmers to achieve data ownership. It addresses the data ownership issue resulting from smart farming. In particular, the white paper examines how high-income countries currently address smart farming’s resulting data ownership issues and identifies ways to preemptively address relevant data ethics issues and policies for low- and middle-income countries.
I had the privilege of speaking at the closing plenary at the Pecora conference last month. The session’s theme was “The Next 50 Years: Synergy and Collaboration.” It gave me a chance to reflect on my experiences within the Earth science community and our plans for the future of Radiant Earth Foundation.
Inspired by the wonderful presentations Paul Ramsey has given over the years, this post attempts to create a readable version of my presentation at Pecora. It is the first in an ongoing series on our approach to create a more sustainable ecosystem of open machine learning and Earth science community.
Developing climate change related models and the importance of finding high-quality machine learning ready data sources. Meet Mohammad Alasawdah, our Community Voice for the last quarter of 2022 and an avid user of Radiant MLHub. Mohammad is an Earth observation and climate data science researcher at Eurac Research. Mohammad holds a joint master of science degree in geospatial technologies from the University of Münster, NOVA University, and Jaume I University.
In this Q&A, Mohammad talks to us about developing climate change related models and the importance of finding high-quality machine learning ready data sources.
Franklin is an Azavea server that imports and serves STAC catalogs, storing data in Postgres. It allows users to import and query STAC catalogs as simply as possible; “from Static Data to a Dynamic API in Minutes.” At Azavea, we are proponents of STAC as it democratizes geospatial information, making it easier to work with, index, and be discovered.
This past month, our team updated Franklin to support the latest STAC API spec as a means to streamline the user experience.
Discover data usage scenarios for open machine learning ready Earth observation repositories like Radiant MLHub. In about one month, we will launch the inclusive innovation 2030 (ii2030) process, where key stakeholders will assemble to turn a systemic challenge into an opportunity for all of us. We will attempt to answer a critical question: “How might we ensure open access to high-quality machine learning (ML) ready Earth observation (EO) data on a sustainable basis?” The ii2030 challenge will support open repositories like Radiant MLHub, which hostsa collection of geospatial training datasets and ML models.
Our Community Voice for this quarter is Emmanuel Siaw-Darko. He joined Radiant Earth as a Machine Learning Intern after winning third place in the AI4FoodSecurity data challenge for his model to classify crop types in South Africa and Germany. In this Q&A, Emmanuel talks to us about his data science journey and working at Radiant to build baseline models that data scientists can use to compare their algorithms.
Washington, July 5 — Radiant Earth Foundation today announced a plan for an executive leadership transition later this year. Effective November 1, 2022, Hamed Alemohammad, Executive Director and Chief Data Scientist, will transition to an advisor and member of the Board of Directors at Radiant Earth Foundation. With this transition, the Board of Directors has opened a position to hire the new Executive Director for the organization.
Radiant Earth Foundation, a nonprofit empowering organizations with open geospatial training data, models, and metadata standards, has been supporting the ramp project’s labeling efforts for building rooftops. Producing building footprint data is a methodical task that requires polygons to be labeled over satellite and drone imagery, usually by hand on a computer, to provide context or confirm the details of each polygon. The labels paired with the imagery become the training data inputs for a building footprint extraction model. 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?
In April over 70 speakers and 800 participants came together for the incredible Cloud-Native Geospatial Outreach Event. Our goal for the event was to highlight just how far the movement around COG, STAC, Zarr & COPC has come, and to accelerate its path towards becoming the way to make geospatial information accessible to the world. Almost everyone who attended was blown away by the breadth and depth of what was covered, and it was clear that cloud-native geospatial is already having a substantive impact on the world.
Identifying sustainable business models for open machine learning ready Earth observation repositories –
When Gedeon Jean first realized the power of Earth observation (EO) data to detect environmental changes, he was mesmerized. As a Machine Learning Research Engineer, he saw the potential of combining machine learning (ML) and EO to develop diverse predictive applications for Rwanda, his native country. Rwanda is increasingly experiencing natural disasters due to climate change, including landslides, floods, and earthquakes, which take a socio-economic toll on an already vulnerable population.