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Webinar Series AI for Earth and Sustainability Science​

Since February 2023 the AI for Earth and Sustainability Science webinar series highlights seminal and recent progress in AI-enabled modelling and understanding of the Earth system from local to global scale and AI-science based diagnosis, prediction and remedy of environmental crises.

Interdisciplinary researchers from academia, industry, UN and government agencies as well as NGOs will talk about tackling systemic real-world challenges with AI, including disaster risk reduction and preparedness, environmental degradation, climate change, societal impacts and dynamics, and the sustainable and responsible use of natural resources such as water and energy.

The webinar series is curated by the ELLIS Unit Jena and the ELLIS program “Machine Learning for Earth and Climate Sciences” in collaboration with AI for Good.

 

Curators

Gustau Camps-Valls

University of Valencia

Maria Piles

University of Valencia

Markus Reichstein

MPI for Biogeochemistry

Joachim Denzler

University of Jena

OUR SPEAKERS AT A GLANCE

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Novel approaches to model assessment and interpretation in geospatial machine learning: addressing spatial dependence and high dimensionality

As the interpretability and explainability of artificial intelligence decisions has been gaining attention, novel approaches are needed to develop diagnostic tools that account for the unique challenges of environmental data, especially the spatial dependence of measurements and the high dimensionality of feature spaces. These are addressed by novel methods presented in this contribution with examples such as the regionalization of pollutants in the environment, and remotely-sensed mapping of mountain permafrost features as an essential climate variable and potential hazard.
Alexander Brenning
NOV 8, 2023

Machine learning for Earth Observation: Emissions, mitigation and sequestration

Constellations of satellites such as the SENTINELs provide almost daily coverage of the earth using a variety of sensors: multispectral and hyperspectral images, synthetic aperture radar, LIDAR, etc. Recent advances in machine learning allow us to process these images at planet scale on a near real-time basis and to paint a very broad picture of key climate related topics such as greenhouse gas emissions, wildfire risk, flooding or carbon sequestration in forests. This talk will cover some of these examples in depth, illustrating some key open numerical challenge.
Alexandre d’Aspremont
OCT 25, 2023

Food security and AI: From data collection to early warning systems

Characterizing the socio-economic status of populations and providing reliable, up-to-date estimates of who the most vulnerable are, how many of them there are, where they reside, and why they are vulnerable, is essential for governments and humanitarian organizations to make informed and timely decisions regarding the implementation of humanitarian assistance policies and programs. Anticipatory and preventive actions have been demonstrated to yield significant benefits compared to post-disaster and post-shock aid. It is, therefore, crucial to understand the drivers of food insecurity in a context where extreme weather events play an increasingly significant role in exacerbating already critical situations. The work of WFP’s Hunger Monitoring Unit revolves around collecting reliable food security data, which is then analyzed in conjunction with climate, economic, and conflict data to support early warning systems, including the use of machine learning.
Giulia Martini
OCT 11, 2023

Using machine learning to track human development at global scale and high spatial resolution

The Human Development Index (HDI) is widely used by policymakers and academics to summarize three key dimensions of wellbeing: the population’s health, human capital, and standard of living. A more comprehensive measure of wellbeing than income or wealth alone, HDI is used to categorize countries by their level of human development, which, in turn, can determine allocations of global resources. However, the United Nations releases official global estimates of HDI annually only at the highly aggregated national level, preventing their use for many policy and aid applications. This project builds on recent advances in machine learning and satellite imagery to develop the first global estimates of HDI for second-level administrative units (e.g., municipalities/counties) and for a global ~10km × 10km grid. To accomplish this, we develop and validate a generalizable downscaling technique based on satellite imagery that allows for training and prediction with observations of arbitrary shape and size. Our results indicate that more than half of the global population was previously assigned to the incorrect HDI quintile within each country, due to aggregation bias resulting from lower resolution estimates.
Hannah Druckenmiller
SEP 27, 2023

Physics-informed machine learning to push the ocean frontier in climate

The global ocean is central to the planet’s health and modulates global levels of heat and carbon, biological productivity, and sea level. However, open questions remain about what drives the circulation which hinders our understanding and ability to monitor ongoing, rapid changes. Climate models suggest that the ocean surrounding Antarctica, a critical region, is changing. However, the limited observations in one of the Earth’s most extreme and inaccessible environments poorly constrain the physical drivers. Here, machine learning is used to construct hypotheses that lead to new theoretical understanding of the circulation and to design a monitoring framework that can assess sensitivity to climate change. Monitoring the circulation is challenging because observations are generally limited to sparse data from the surface. With our theoretical insight, we developed a new physics-informed methodology to fill this gap using available data. The monitoring method Tracking global Heating with Ocean Regimes (THOR) can ‘reason’ using geophysical fluid dynamics. It is explicitly transparent and consists of a series of neural networks that combine eXplainable AI and Bayesian confidence scores for its predictions. We reveal differences in model physics that cause model divergence and spread in projections, opening the door to further discovery and observational strategies.
Maike Sonnewald
MAY 24, 2023

Global vegetation monitoring with probabilistic deep learning

Mapping forest structure on a global scale is an important component for understanding the Earth’s carbon cycle and conserving biodiversity. Several new space missions have been developed to support these goals by measuring forest structure to estimate biomass and carbon stocks. To analyze the vast amount of remote sensing data, efficient modelling approaches are needed that are robust to the inherent noise in these data. Data-driven approaches, especially modern deep learning methods, promise great potential for interpreting and combining data from different space missions to estimate vegetation parameters with enhanced spatial and temporal resolution. This talk will present recent research results for global vegetation height mapping exploiting ESA’s Sentinel-2 optical imagery and NASA’s GEDI full waveform LIDAR. By integrating probabilistic deep learning approaches (i.e. deep ensembles), the predictive uncertainty of the models is quantified, which is crucial for downstream applications that depend on reliable estimates.
Nico Lang
MAY 10, 2023

Remote sensing enables monitoring life above and under water

The oceans are a major source of biodiversity and food. The preservation of the quality of the foodchain, as well as of life underwater is a primary concern for society. However, observing water media routinely is a complex task that remote sensing technology empowered by AI can unlock. This talk will present research avenues where the use of remote sensing can help monitoring marine ecosystem, both above – via the monitoring of floating plastic debris – and under the water surface – via 3D reconstruction and mapping of coral reefs.
Devis Tuia
MAY 10, 2023

Unraveling the Effects of Climate Extremes on Ecosystems: towards predictive capabilities

Climate change is causing an increase in the frequency and intensity of extreme events such as heatwaves and droughts, which have severe consequences on ecosystems and human well-being. Understanding the complex dynamics of these events is crucial for developing robust models and scenarios for the future. This talk will discuss the challenges and opportunities of leveraging machine learning to enhance our understanding of the impact of climate extremes on ecosystems. Using local case studies on the impacts of climate extremes on forests and other ecosystems, we will demonstrate the benefits of utilizing AI and high-resolution satellite-derived data cubes for addressing this quest. We will also present the DeepExtremes project, funded by the European Space Agency, aiming to develop explainable AI methods for global extreme event impact detection and prediction. This presentation will invite the community to address this pressing environmental issue together. By effectively combining Earth system science, Earth system data cube approaches and AI, we expect to advance the management of consequences of extreme events and contribute to the global effort to use AI for good.
Miguel Mahecha
APR 26, 2023

Mapping the urban forest across North America: A case study in Computer vision for large-scale environmental monitoring

Generalization to novel domains is a fundamental challenge for computer vision. Near-perfect accuracy on benchmarks is common, but these models do not work as expected when deployed outside of the training distribution. To build computer vision systems that solve real-world problems at global scale, we need benchmarks that fully capture real-world complexity, including geographic domain shift, long-tailed distributions, and data noise. We propose urban forest monitoring as an ideal testbed for studying and improving upon these computer vision challenges, while working towards filling a crucial environmental and societal need. Urban forests provide significant benefits to urban societies. However, planning and maintaining these forests is expensive. One particularly costly aspect of urban forest management is monitoring the existing trees in a city: e.g., tracking tree locations, species, and health. We introduce a new large-scale dataset that joins public tree censuses from 23 cities with a large collection of street level and aerial imagery, containing over 2.5M trees and 300 genera. This benchmark enables the exploration of automated urban forest monitoring across modalities and with respect to geographic distribution shifts, vital for such a system to be deployed at-scale.
Laure Zanna
APR 26, 2023

Towards physics-AI hybrid modeling in hydrology: Opportunities and challenges

Recent advances in AI provide unprecedented opportunities to predict and understand components of the hydrological cycles. Nonetheless, many critical issues remain unresolved. For instance, with the remarkable predicting power of machine learning demonstrated in the field, it is under debate whether and how hydrological theory can still have a place in hydrological models or even benefit from “black-box” machine learning methods. In this presentation, we introduce two case studies that attempt to incorporate hydrological knowledge and machine learning in a unified framework. The first study develops a novel deep learning architecture that is encoded with a conceptual hydrological model, resulting in an end-to-end hydrology-AI hybrid learning system. The simulation results show that the hybrid model has improved prediction accuracy, robust transferability, and good intelligence for inferring unobserved processes. The second study develops an explainable machine learning approach to improve our understanding of runoff mechanisms, thereby gaining a deeper insight into how floods are changing under climate change. Overall, this presentation stresses how physics-AI integration can help us u
Shijie Jiang
APR 12, 2023

Deep learning and the dynamics of physical processes

Deep learning has been studied for a few years for the modeling of complex physical processes in industrial fields such as aeronautics or energy production and in scientific fields such as environment or health.  This area of research, although still emerging, is rapidly gaining momentum and developing as an interdisciplinary field. It raises new challenges for the interaction between machine learning and physics. This talk will focus on deep learning approaches for modeling dynamic physical systems and illustrate three main challenges: incorporating prior physical knowledge into learning models, generalizing learning models to multiple environments, and learning models operating continuously in space and time thus allowing flexible extrapolation at arbitrary spatiotemporal locations. This presentation will be illustrated by applications in different domains.
Patrick Gallinari
MAR 29, 2023

Climate modeling with AI: Hype or Reality?

Climate simulations remain one of the best tools to understand and predict global and regional climate change. Uncertainties in climate predictions originate partly from the poor or lacking representation of processes, such as ocean turbulence and clouds, that are not resolved in global climate models but impact the large-scale temperature, rainfall, sea level, etc. The representation of these unresolved processes has been a bottleneck in improving climate simulations and projections. The explosion of climate data and the power of machine learning (ML) algorithms are suddenly offering new opportunities: can we deepen our understanding of these unresolved processes and simultaneously improve their representation in climate models to reduce climate projections uncertainty? This talk discusses the advantages and challenges of using machine learning for climate projections. The focus will be on recent work in which we leverage ML tools to learn representations of unresolved ocean processes – in particular, learning symbolic expression. Some of this work suggests that machine learning could open the door to discovering new physics from data and enhance climate predictions. Yet, many questions remain unanswered, making the next decade exciting and challenging for ML + climate modeling for robust and actionable climate projections. 
Laure Zanna
MAR 29, 2023

Long Short-Term Memory networks for large-scale rainfall-runoff modeling

Flooding is one of the most common and costly types of natural disaster, with disproportionate human impacts in developing countries and communities. Google has developed an AI-based river and inundation forecasting system, and is partnering with governments and water agencies around the world to provide real-time flood alerts directly to individuals, communities, and NGOs through existing Google information channels like Maps, Search, and Android Alerts. This talk will cover the background, development, and impact of this effort.
Grey Nearing
MAR 15, 2023

Long Short-Term Memory networks for large-scale rainfall-runoff modeling

This talk will neither be about the depths of deep learning nor the depths of hydrology. Rather it will be a mix of both worlds from the perspective of solving problems in one field with tools from the other. If you are interested to hear about real-world applications of deep learning models to engineering tasks and all the research behind it, this talk is for you. More specifically, the talk will revisit the outcomes of years of research in the intersection of deep learning and rainfall-runoff modeling and why a more than 30-year-old neural network architecture (Long Short-Term Memory, LSTM) seems to be a perfect fit for this application. It will also be the tale of a young researcher that tried many (too complicated?) things but always found out that simplicity wins. More than anything, This talk may inspire (young) researchers to look for interdisciplinary problems/projects and to learn about new tools/techniques from different fields.
Frederik Kratzert
MAR 1, 2023

The role of AI in achieving the Sustainable Development Goals

The emergence of Artificial Intelligence (AI) is rapidly progressing with wide impacts across many sectors. As part of AI for Good’s new series on AI for Earth and Sustainability Science, this session provides an assessment of the impact of AI on the achievement of the Sustainable Development Goals (SDGs) specifically looking at Sustainable Cities (SDG11) and Climate Change (SDG13). Using a consensus-based expert elicitation process, it is clear that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research may be overlooking important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards leading to less than optimal outcomes for the environment and related sustainability targets.
Ricardo Vinuesa
FEB 1, 2023

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Dr. Conrad H. PHILIPP
European Laboratory for Learning and Intelligent Systems (ELLIS)
ELLIS Unit Jena | Project Coordinator