Fragile Earth is an annual gathering that brings together research, industry, and policy community around enhancing scientific discovery in the earth sciences through the joint use of data, theory, and computation.
Since 2016 the AI for Good Foundation has organised the Fragile Earth community and associated events, bringing together researchers, subject matter experts, governments, and policy groups under one roof to learn and discuss how Artificial Intelligence can help address problems with the Earth’s Biome and threats to its stability.
We’re partnering with a variety other organisations in order to develop workshops, datasets, community engagement, and research that can have a direct impact on these themes.
Fragile Earth at KDD 2020
The Fragile Earth workshop brought together research, industry, and policy communities at KDD Virtual Conference 2020 to discuss, address and help develop the technological foundations for advancing and meeting the SDGs.
The applications and agenda of interest include food security, sustainable agricultural practices, crop yield forecasting and improvement, restoring degraded landscapes to productive landscapes, clean water management, sustainable and clean energy production, energy efficient and low waste food supply chain, and the future of intelligent technologies in tackling these topics in an ever urbanizing world.
The mission of KDD is to promote the rapid maturation of the field of knowledge discovery in data and data-mining.
Research Papers from KDD 2020
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling
By Nishant Yadav, Sai Ravela, and Auroop R. Ganguly
High-Resolution Air Quality Prediction Using Low-Cost Sensors
By Thibaut Cassard, Grégoire Jauvion and David Lissmyr
Online Learning Algorithm for Hurricane Intensity Prediction
By Ding Wang, Boyang Liu and Pang-Ning Tan
People-Centered Climate Hazard Impact Assessment using Machine Learning: A Drought Risk Perspective
By Markus Enenkel and Molly Brown
The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines
By Joyjit Chatterjee and Nina Dethlefs
Mapping New Informal Settlements using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis
By Isabelle Tingzon, Niccolo Dejito, Ren Avell Flores, Rodolfo De Guzman, Liliana Carvajal, Katerine Zapata Erazo, Ivan Enrique Contreras Cala, Jeffrey Villaveces, Daniela Rubio and Rayid Ghani
Trust and Transparency in Contact Tracing Applications
By Stacy Hobson, Michael Hind, Aleksandra Mojsilovic and Kush Varshney
Optimizing crop cut collection for determining field-scale yields in an insurance context
By Ritvik Sahajpal, Inbal Becker-Reshef and Sylvain Coutu
Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization
By Hannah Kerner, Ritvik Sahajpal, Sergii Skakun, Inbal Becker-Reshef, Brian Barker, Mehdi Hosseini, Estefania Puricelli and Patrick Gray
Towards a Global Species Dataset by Fusing Remote Sensing and Citizen Science Data with Graph Neural Networks
By Kenza Amara, David Dao and Bjoern Luetjens
Leveraging traditional ecological knowledge in ecosystem restoration projects utilizing machine learning
By Bogdana Rakova and Alexander Winter
Subscribe to community updates, send us your work to showcase, and get involved in organising our future workshops and summits.
Cargill is working to nourish the world. We're bringing together people, ideas, and resources to deliver products, technology and ways of operating that build successful businesses and communities.
Share this Page
Join our efforts to unlock AI’s potential towards serving humanity.