-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
32e4115
commit 9f4e812
Showing
90 changed files
with
588 additions
and
598 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,7 +1,6 @@ | ||
--- | ||
title: "SEDAL Grant" | ||
date: 2016-01-01 | ||
draft: false | ||
externalLink: "./2016/sedal-grant/sedal/index.html" | ||
--- | ||
|
||
|
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,24 +1,30 @@ | ||
--- | ||
title: "SEDAL: Statistical Learning for Earth Observation Data Analysis" | ||
type: "news" | ||
layout: "single" | ||
--- | ||
|
||
# Objectives | ||
|
||
**Improve prediction models by adaptation to Earth Observation data characteristics.** We will rely on the framework of kernel learning, which has emerged as the most appropriate framework for remote sensing data analysis in the last decade. The new retrieval models will be adapted to the particular signal characteristics, such as unevenly sampled time series and missing data, non-Gaussianity, presence of heteroscedastic and non-stationary processes, and non-i.i.d. (spatial and temporal) relations. Models based on kernels and GPs will allow us to advance in uncertainty quantification using predictive variances under biophysical constraints. Advances in sparse, reduced-rank and divide-and-conquer schemes will address the computational cost problem. The proposed kernel framework aims to improve results in terms of accuracy, reduced uncertainty, consistency of the estimations and computational efficiency. | ||
|
||
<br> | ||
|
||
**Discover knowledge and causal relations in Earth observation data.** We will investigate graphical causal models and regression-based causal schemes applied to large heterogeneous EO data streams. This will require 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. | ||
|
||
# Research | ||
|
||
SEDAL aims at contributing novel machine learning algorithms along these lines: | ||
|
||
- Advanced remote sensing data and EO time series processing and statistical characterization | ||
- Advanced regression methods, involving kernel methods, Gaussian processes, random forests, and deep nets | ||
- Efficient large-scale model implementations | ||
- Uncertainty quantification and propagation | ||
- Physically-based models, emulation of RTMs, and design of physically-meaningful priors in machine learning regression | ||
- Knowledge discovery and structure learning from empirical EO data | ||
- (Conditional) Dependence estimation of EO variables and observations | ||
- Graphical models, structure learning, Bayesian networks and causal inference from empirical EO data | ||
|
||
The target EO applications are: | ||
- SEDAL aims at contributing novel machine learning algorithms along these lines: | ||
- Advanced remote sensing data and EO time series processing and statistical characterization | ||
- Advanced regression methods, involving kernel methods, Gaussian processes, random forests, and deep nets | ||
- Efficient large-scale model implementations | ||
- Uncertainty quantification and propagation | ||
- Physically-based models, emulation of RTMs, and design of physically-meaningful priors in machine learning regression | ||
- Knowledge discovery and structure learning from empirical EO data | ||
- (Conditional) Dependence estimation of EO variables and observations | ||
- Graphical models, structure learning, Bayesian networks and causal inference from empirical EO data | ||
|
||
- Improved retrieval (regression) algorithms at local, regional, and global planetary scales | ||
- Structure inference and relevance determination of essential climate variables and observations | ||
- Climate change detection, anomalies, extremes, and causal inference attribution | ||
- The target EO applications are: | ||
- Improved retrieval (regression) algorithms at local, regional, and global planetary scales | ||
- Structure inference and relevance determination of essential climate variables and observations | ||
- Climate change detection, anomalies, extremes, and causal inference attribution |
31 changes: 6 additions & 25 deletions
31
content/news/2016/sedal-grant/motivation.md → ...ews/2016/sedal-grant/motivation/_index.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,30 +1,11 @@ | ||
# Motivation | ||
--- | ||
title: "Motivation" | ||
type: "research" | ||
layout: "single" | ||
--- | ||
|
||
Despite the many successful results and developments, there are still strong limitations for the general adoption of machine learning algorithms for predicting and understanding EO data. Machine learning and signal processing have advanced enormously in the last decade (both at theoretical and applied levels) but have not moved forward the field of EO data analysis to its full potential. | ||
|
||
The current statistical treatment of biophysical parameters is strongly limited by the quantity and quality of EO data, as well as by the abuse of standard off-the-shelf methods, which, in general, are not well-adapted to the particular characteristics of EO data. Specifically, current regression models used for EO applications are still deficient because they rely on limited amounts 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 amounts 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 of 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. | ||
|
||
# Related Projects | ||
|
||
<br> | ||
|
||
## Cloud Detection in the Cloud | ||
- **Google Earth Engine Research Award, L. Gomez-Chova** | ||
- 01/16 - 12/17 | ||
- [Cloud detection in the cloud](/old_pages/other/cloud_detection.html) | ||
|
||
## LIFE-VISION: Learning Image Features to Encode Visual Information | ||
- **Spanish Ministry of Economy and Competitiveness, 2012. TIN2012-38102-C03-01** | ||
- 01/13 - 12/15 | ||
- [LIFE-VISION](http://lifevisionproject.wordpress.com/) | ||
|
||
## GEOLEARN | ||
- **Spanish Ministry of Economy and Competitiveness** | ||
- 2016 | ||
- [GEOLEARN](/old_pages/other/motivation_sd.html) | ||
|
||
## ESA CCI Soil Moisture | ||
- **European Space Agency** | ||
- [ESA CCI Soil Moisture](http://esa-soilmoisture-cci.org/) | ||
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 of 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. |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,12 @@ | ||
--- | ||
title: 'ESA Climate Change Initiative Phase II Soil Moisture (CCI SM 2 Project)' | ||
logo: 'esa.webp' | ||
pi: 'M. Piles (Visiting Scientist), Wouter Dorigo (TU Wien, PI)' | ||
uvpi: '' | ||
years: '2017' | ||
website: 'http://www.esa-soilmoisture-cci.org/' | ||
funding_source: 'ESA' | ||
role: '' | ||
project_type: '' | ||
partners: [] | ||
--- |
14 changes: 14 additions & 0 deletions
14
content/news/2016/sedal-grant/motivation/cloud-detection-in-the-cloud.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,14 @@ | ||
--- | ||
title: 'Cloud Detection in the Cloud' | ||
logo: 'google.webp' | ||
pi: '' | ||
uvpi: 'L. Gomez-Chova' | ||
years: '2016-2017' | ||
website: 'https://isp.uv.es/projects/cdc/GEE_cloud_detection_results.html' | ||
funding_source: 'Google Earth Engine Research Award' | ||
role: '' | ||
project_type: '' | ||
partners: [] | ||
type: "news" | ||
layout: "single_projects" | ||
--- |
12 changes: 12 additions & 0 deletions
12
content/news/2016/sedal-grant/motivation/esa-climate-change-initiative.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,12 @@ | ||
--- | ||
title: 'ESA Climate Change Initiative (CCI) Phase 1: Essential Climate Variable (ECV) Cloud' | ||
logo: 'esa.webp' | ||
pi: '' | ||
uvpi: '' | ||
years: '2010-2013' | ||
website: 'http://www.esa-cci.org/' | ||
funding_source: 'ESA' | ||
role: '' | ||
project_type: '' | ||
partners: [] | ||
--- |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,12 @@ | ||
--- | ||
title: 'GEOLEARN: Advances in Machine Learning for Large Scale Remote Sensing Data Processing' | ||
logo: 'mineco.webp' | ||
pi: '' | ||
uvpi: 'Jordi Munoz-Mari' | ||
years: '2015-2018' | ||
website: 'http://harpo.uv.es/wiki/geolearn:start' | ||
funding_source: 'Spanish Ministry of Economy and Competitiveness' | ||
role: '' | ||
project_type: '' | ||
partners: [] | ||
--- |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,12 @@ | ||
--- | ||
title: 'LIFE-VISION: Learning Image Features to Encode Visual Information' | ||
logo: 'mineco.webp' | ||
pi: '' | ||
uvpi: '' | ||
years: '2012-2015' | ||
website: 'http://lifevisionproject.wordpress.com/' | ||
funding_source: 'Spanish Ministry of Economy and Competitiveness' | ||
role: '' | ||
project_type: '' | ||
partners: [] | ||
--- |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.