Fragile Earth:

Data Science for a Sustainable Planet

August 24th, 2020

Research Papers from KDD 2020

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Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling

By Nishant Yadav, Sai Ravela, and Auroop R. Ganguly

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High-Resolution Air Quality Prediction Using Low-Cost Sensors

By Thibaut Cassard, Grégoire Jauvion and David Lissmyr

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Online Learning Algorithm for Hurricane Intensity Prediction

By Ding Wang, Boyang Liu and Pang-Ning Tan

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People-Centered Climate Hazard Impact Assessment using Machine Learning: A Drought Risk Perspective

By Markus Enenkel and Molly Brown

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The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines

By Joyjit Chatterjee and Nina Dethlefs

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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

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Trust and Transparency in Contact Tracing Applications

By Stacy Hobson, Michael Hind, Aleksandra Mojsilovic and Kush Varshney

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Optimizing crop cut collection for determining field-scale yields in an insurance context

By Ritvik Sahajpal, Inbal Becker-Reshef and Sylvain Coutu

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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

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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

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Leveraging traditional ecological knowledge in ecosystem restoration projects utilizing machine learning

By Bogdana Rakova and Alexander Winter

Call for Papers

We are excited to announce the Fragile Earth workshop at KDD ’20, Earth Day in San Diego on August 24th. Fragile Earth will bring together the research, industry, and policy community around enhancing scientific discovery in the earth sciences through the joint use of data, theory, and computation.

Whether it is food security, water scarcity, energy use, land restoration, climate models, or the incorporation of theoretical thinking into data driven frameworks to accelerate progress on the United Nations’ Sustainable Development Goals, and related areas, we invite you to be part of the community!

We solicit three categories of papers + posters: research papers, extended abstracts, and position/vision papers.

Research papers should be 8-10 pages in length, extended abstracts 1-4 pages and position or vision papers between 2 to 6 pages. All should follow the ACM template:

https://www.acm.org/publications/proceedings-template

Posters must fit standard 24″ x 36″ poster boards, and authors must print the posters themselves ahead of the conference.

All submissions in PDF format please!

Areas

Two key technological challenges posed by the Sustainable Development Goals are (a) how to achieve accurate, robust and scalable modeling on physical, environmental, system and societal data, and (b) how to ensure that the obtained models are socially acceptable for use in the associated policy and decision making support.

A key technological enabler for the former is theory-guided data science and scientific discovery, which by augmenting data driven modeling with domain physics and constraints, realizes both accuracy and flexibility in modeling. For the latter, leveraging the emerging techniques of trustworthy machine learning and artificial intelligence to attain the interpretability, accountability, fairness and privacy required for social adoption would be key, along with explicit consideration and inclusion of the viewpoints of policy makers.

Applications of interest include but are not limited to:

  1. Food security, sustainable agricultural practices, crop yield forecasting and improvement, energy efficient and low waste food supply chain.
  2. Fostering degraded landscapes to productive landscapes, clean water management, sustainable and clean energy production.
  3. The future of intelligent technologies in tackling these topics in an ever urbanizing world.

Methodological contributions of interest include but are not limited to:

  1. The integration of physics into data driven environmental modeling and use of advanced machine learning techniques to enhance or speed up physical models.
  2. Addressing interpretability of theory guided and data driven models of environment, e.g. by incorporation of physics into causal explanation of models.
  3. Privacy aware schemes for data sharing in agriculture and food systems and addressing fairness of benefit and credit assignment in data sharing for sustainability.

Additionally, we welcome cross domain and policy and paradigmatic topics:

  1. Paradigms for enhancing scientific discovery through theory guided data science.
  2. Data-informed Food/Energy/Water/Earth Sciences policy discussions.
  3. Frameworks for helping the scientific and KDD communities to work together.

Accepted papers will be allocated to three themed sessions and a poster session. We expect presentations to last 15-20 minutes (including questions), and will prioritize submissions based on relevance, scientific rigour, and potential for societal impact.

Workshop papers will not be published as a part of the SIGKDD Conference proceedings.

The EXTENDED submission deadline is June 20th, 2020!

News & Updates

  • Best paper awards and registration fee waivers for early career authors will be provided. This is made possible through generous funding from Cargill, Inc.
  • Please check back here regularly for updates on travel fund availability/applications for PhD students or those with special circumstances. Please reach out to with any questions.

Organizers

Naoki Abe

Distinguished Research Staff Member, IBM Research AI

Kathleen Buckingham

Research Manager, World Resources Institute

Bistra Dilkina

Associate Professor, CS, USC; Associate Director, USC Center for AI in Society (CAIS)

Emre Eftelioglu

Applied Scientist at Amazon US

Auroop Ganguly

Auroop R. Ganguly

Professor at Northeastern University in Boston, Director of the Sustainability and Data Sciences Laboratory (SDS Lab). A co-founder and the chief scientific adviser of risQ Inc.

James Hodson

CEO, AI for Good Foundation, Chief Science Officer, Cognism, Inc. Researcher, Jozef Stefan Institute, Artificial Intelligence Lab

Ramakrishnan Kannan

CEO, AI for Good Foundation, Chief Science Officer, Cognism, Inc. Researcher, Jozef Stefan Institute, Artificial Intelligence Lab