-
-# Earth and climate sciences
-
-
-
-## Motivation
+# Motivation
**Machine learning has yielded many successful results and developments in remote sensing, geosciences and climate sciences. However, there are still strong limitations for the general adoption of machine learning algorithms for predicting and understanding the Earth and climate systems.**
-
-
-
-
+
+
+
+
+
+
The current statistical treatment of biophysical parameters is strongly limited by the quantity and quality of EO data, as well as because of the abuse of standard off-the-shelf methods, which in general are not well-adapted to the particular EO data characteristics. Specifically, current regression models used for EO applications are still deficient because they rely on limited amount of meteorological and remote sensing data, do not observe the particular data characteristics, and often make strong assumptions of linearity, homoscedasticity or Gaussianity. These limitations translate into certain risks of overfitting and unreasonably large uncertainties for the predictions, suggesting a lack of explanatory variables and deficiencies in model specification. Graphical models have been seldom used in EO data analysis. The few works restrict to local studies, use limited amount of data and explanatory variables, consider remote sensing input features only, apply standard structure learning algorithms driven by univariate (often unconditioned) dependence estimates, and do not extract causal relations or identify new drivers in the problem.
We advocate that machine learning algorithms for EO applications need to be guided both by data and by prior physical knowledge. This combination is the way to restrict the family of possible solutions and thus obtain nonparametric flexible models that respect the physical rules governing the Earth climate system. We are equally concerned about the ‘black-box' criticism to statistical learning algorithms, for which we aim to design self-explanatory models and take a leap towards the relevant concept of causal inference from empirical EO data.
-## Challenges and approaches
+# Challenges and approaches
Our main goal is to develop new machine learning models for the ambitious goal of modeling and understanding the Earth and climate systems with data, models and machine learning. This main scientific goal translates into the following objectives:
@@ -41,19 +39,7 @@ Our main goal is to develop new machine learning models for the ambitious goal o
- **Discover knowledge and causal relations in Earth observation data.** We investigate graphical causal models and regression-based causal schemes applied to large heterogeneous EO data streams. This requires improved measures of (conditional) independence, designing experiments in controlled situations and using high-quality data. Learning the hierarchy of the relations between variables and related covariates, as well as their causal relations, may in turn allow the discovery of hidden essential variables, drivers and confounders. Moving from correlation to dependence and then to causation concepts is fundamental to advance the field of Earth Observation and the science of climate change.
-### Related Projects
-
-{{< projects "usmile" "xaida" "deepcube" "elise" "sedal" "cloud" "mapict" >}}
-
-
-
-# AI for sustainability and social sciences
-
-
-
-## Motivation
+# Motivation
**The Earth is a highly complex, dynamic, and networked system where very different physical, chemical and biological processes interact, to form the world we know. The description of such a complex system needs of the integration of different disciplines such as Physics, Chemistry, Mathematics and other applied sciences, leading to what has been coined as Earth System Science (ESS). The analysis of the Earth system involves studying interacting processes occurring in several spheres (atmosphere, hydrosphere, cryosphere, geosphere, pedosphere, biosphere, and magnetosphere) as well as the anthroposphere where the Society acts.**
-
-
-
-
+
+
+ 
+
+
+
Earth system science provides the physical basis of the world we live in, with the final objective of obtaining a sustainable development of our society, see the United Nations [Sustainable Development Goals](https://sustainabledevelopment.un.org/). We develop AI models for tackling pressing questions in the climate-society interplay. We tackle problems where the human is in the middle (both a cause and an effect); where SDGs, fairness and ethics are implied, and where elusive concepts like wealth, well-being and development are involved. Our unique approach involves exploiting massive amount of data and machine learning algorithms to model and understand the environment-human interactions.
-## Challenges and approaches
+# Challenges and approaches
Our main goal is to develop new machine learning models for the efficient treatment of biophysical land parameters and related covariates at local and global scales. This main scientific goal translates into the following objectives:
@@ -30,26 +28,4 @@ Our main goal is to develop new machine learning models for the efficient treatm
- **Algorithmic Fairness.** New social and economic activities massively exploit big data and machine learning algorithms to do inference on people's lives. Applications include automatic curricula evaluation, wage determination, and risk assessment for credits and loans. Recently, many governments and institutions have raised concerns about the lack of fairness, equity and ethics in machine learning to treat these problems. It has been shown that not including sensitive features that bias fairness, such as gender or race, is not enough to mitigate the discrimination when other related features are included (SDGs 5 and 10). Instead, including fairness in the objective function has been shown to be more efficient. We develop novel fair regression and dimensionality reduction methods to tackle such problems. The proposed methods allow us to tackle pressing societal problems like predicting income using gender and/or race discrimination as sensitive variables, contraceptive method prediction under demographic and socio-economic sensitive descriptors, or predicting climate change protecting against anthropogenic factors. Neural networks, Gaussian processes, kernel machines and optimal transport are our favourite tools.
-- **Causality in the biosphere-anthroposphere coupled system.** The terrestrial biosphere and the anthroposphere are deeply coupled in multiple ways. Humans depend on a range of ecosystem goods and services but, at the same time, they heavily engineer and modify land ecosystems. Over the past decades, human development has generally made substantial progress in terms of education (SDG 2), health services (SDG 2), life expectancy (SDG 2) and many other aspects around the planet with very few exceptions due to e.g. warfare and in sub-Saharan Africa. The grand question is if, and how, one can identify and quantify relationships between changes in land-ecosystem states and the human development metrics at the global scale. We tackle problems of food insecurity (SDGs 1, 2), climate-induced migration (SDGs 1, 2), infants well-being (SDGs 3, 4) and conflicts (SDGs 10, 11). Our hypothesis is that we can now tackle causal discovery problems relying on both assumed relations and exploiting observational data. We rely on the science of causal inference to unravel relations between coupled variables beyond correlations even in the presence of non-linearities and non-stationarities.
-
-
-### Releted Projects
-
-{{< projects deepcube xaida scale mapict >}}
-
-
-
-
+
- 
+
+