IPL website 0.0.1

.github/workflows/gh-page.yml

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+name: GitHub Pages
+
+on:
+  push:
+    branches:
+      - main  # Set a branch to deploy
+  pull_request:
+
+jobs:
+  deploy:
+    runs-on: ubuntu-22.04
+    concurrency:
+      group: ${{ github.workflow }}-${{ github.ref }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: true  # Fetch Hugo themes (true OR recursive)
+          fetch-depth: 0    # Fetch all history for .GitInfo and .Lastmod
+
+      - name: Setup Hugo
+        uses: peaceiris/actions-hugo@v2
+        with:
+          hugo-version: '0.121.2'
+          # extended: true
+
+      - name: Build
+        run: hugo --minify
+
+      - name: Deploy
+        uses: peaceiris/actions-gh-pages@v3
+        if: github.ref == 'refs/heads/main'
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./docs

content/_index.md

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+---
+title: "index"
+banner: "/isp/images/isp_banner.webp"
+---
+
+Our vision is to develop novel artificial intelligence (AI) methods to model and understand complex systems, and more specifically the visual brain, Earth and climate systems, and their human interactions. Our approach to signal, image, and vision processing combines statistical learning theory with the understanding of the underlying physics, processes and biological vision. The problems posed in these disciplines require similar mathematical tools, where model inversion, uncertainty estimation, and causal inference play a central role. Our research on AI pivots around three main pillars: encoding domain knowledge in machine learning, understanding model representations and predictions, as well as learning causal relations from observational data.

content/code/_index.md

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+---
+title: code
+---

content/collaborators/_index.md

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+---
+title: collaborators
+type:
+---

content/contact/_index.md

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+---
+title: contact
+---

content/courses/_index.md

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+---
+title: courses
+---

content/data/_index.md

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+---
+title: data
+---

content/data/data_links/cloudsen12/_index.md

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+---
+title: "CloudSEN12"
+type: data/generic_dataset
+subtitle: "A Benchmark Dataset for Cloud Semantic Understanding"
+logo: /isp/images/data/cloudsen12/logo.gif
+banner: cloudsen12/banner.png
+github: https://github.com/cloudsen12
+huggingface: https://huggingface.co/isp-uv-es/cloudsen12
+tags:
+    - Sentinel-2
+    - UNet
+    - Cloud Detection
+    - Multi-Modal
+    - Remote Sensing
+authors:
+    - name: "Herrera Fernando"
+      image: "cloudsen12/authors/fernando.png"
+    - name: "Loja Jhomira"
+      image: "cloudsen12/authors/jhomira.jpeg"
+    - name: "Ysuhuaylas Luis"
+      image: "cloudsen12/authors/luis.jpeg"
+    - name: "Gonzales Karen"
+      image: "cloudsen12/authors/andrea.jpeg"
+    - name: "LLactayo Valeria"
+      image: "cloudsen12/authors/valeria.jpg"
+    - name: "Bautista Lesly"
+      image: "cloudsen12/authors/lesly.jpg"
+    - name: "Diaz Lissette"
+      image: "cloudsen12/authors/lissette.png"
+    - name: "Flores Angie"
+      image: "cloudsen12/authors/angie.jpg"
+    - name: "Cuenca Nicole"
+      image: "cloudsen12/authors/nicole.jpg"
+    - name: "Inga Joselyn"
+      image: "cloudsen12/authors/inga.jpg"
+    - name: "Espinoza Wendy"
+      image: "cloudsen12/authors/wendy.jpg"
+    - name: "Fernando Prudencio"
+      image: "cloudsen12/authors/fernando.png"
+    - name: "Yali Roy"
+      image: "cloudsen12/authors/roy.jpg"
+    - name: "Aybar Cesar"
+      image: "cloudsen12/authors/cesar.jpg"
+    - name: "Mateo-García Gonzalo"
+      image: "cloudsen12/authors/gonzalo.png"
+    - name: "Gomez-Chova Luis"
+      image: "cloudsen12/authors/gomez.png"
+    - name: "Tiede Dirk"
+      image: "cloudsen12/authors/dirk.jpg"
+    - name: "Sudmanns Martin"
+      image: "cloudsen12/authors/martin.png"
+    - name: "David Montero"
+      image: "cloudsen12/authors/david.jpg"
+examples:
+    - title: "Download CloudSEN12 using easystac"
+      link: "https://colab.research.google.com/github/cloudsen12/examples/blob/master/example01.ipynb"
+    - title: "Make a prediction using UnetMobV2 (CloudSEN12)"
+      link: "https://colab.research.google.com/github/cloudsen12/examples/blob/master/example02.ipynb"
+    - title: "CloudSEN12 and PyTorch Lightning"
+      link: "https://colab.research.google.com/github/cloudsen12/examples/blob/master/example03.ipynb"
+    - title: "Visualize CloudSEN12 using geemap"
+      link: "https://colab.research.google.com/github/cloudsen12/examples/blob/master/example04.ipynb"
+    - title: "Compare cloud masking models"
+      link: "https://colab.research.google.com/github/cloudsen12/examples/blob/master/example05.ipynb"
+    - title: "Visualize CloudSEN12 in GEE code editor"
+      link: "https://github.com/cloudsen12/examples/tree/main/js"
+citation: |
+    @article{aybar2022cloudsen12,
+      title={CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2},
+      author={Aybar, Cesar and Ysuhuaylas, Luis and Loja, Jhomira and Gonzales, Karen and Herrera, Fernando and Bautista, Lesly and Yali, Roy and Flores, Angie and Diaz, Lissette and Cuenca, Nicole and others},
+      journal={Scientific data},
+      volume={9},
+      number={1},
+      pages={782},
+      year={2022},
+      publisher={Nature Publishing Group UK London}
+    }
+---
+
+## Introduction
+
+CloudSEN12 is a LARGE dataset (~1 TB) for cloud semantic understanding that consists of 49,400 image patches (IP) that are evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge cloud detection algorithms.
+
+CloudSEN12 is designed to support both weakly and self-/semi-supervised learning strategies by including three distinct forms of hand-crafted labeling data: high-quality, scribble and no-annotation. For more details on how we created the dataset see our paper: [CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2](https://www.nature.com/articles/s41597-022-01878-2).
+

content/data/data_links/motion_texture_color_statistics/_index.md

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+---
+title: "Motion Texture Color Statistics"
+type: data/motion_texture_color_statistics
+---
+hola

content/facilities/_index.md

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+---
+title: facilities
+---

content/news/_index.md

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+---
+title: news
+---

content/people/_index.md

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+---
+title: people
+---

content/projects/_index.md

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+---
+title: projects
+---

content/projects/projects_links/ai4cs/_index.md

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+---
+title: "ai4cs"
+type: projects/generic_projects
+
+News:
+  - title: "The project activities started with the Kick-off meeting in Valencia on Feb 6th, 2023."
+    url: "https://www.pcuv.es/es/home"
+  - title: "Projecte 'Artificial Intelligence for complex systems: Brain, Earth, Climate, Society', financiat per la Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, codi: CIPROM/2021/56.
+  Liderat per Gustau Camps-Valls i Maria Piles, membres de Image Signal Processing (ISP) de la Universitat de València"
+    url: ""
+
+---
+
+# AI4CS - AI for complex systems: Brain, Earth, Climate, Society
+
+Our vision in AI4CS is to develop novel artificial intelligence methods to model and understand com- plex systems, and more specifically the visual brain, Earth and climate systems and the biosphere- anthroposphere interactions. A perfect storm is over us: (i) an ever increasing amount of observational and sensory data, (ii) improved high resolution yet mechanistic models are available, and (iii) advanced ma- chine learning techniques able to extract patterns and identify drivers from data. In the last decade, machine learning models have helped to monitor, predict and forecast all kind of variables and parameters of interest from observational data. They help in quantifying visual stimuli, to monitor land, oceans, and the atmosphere, as well as to study socio-economic variables at different scales and spheres. Current approaches, however, face three important challenges: (1) they cannot deal efficiently with the particular characteristics of data, (2) they do not respect the most elementary laws of physics, and (3) they just interpolate but nothing fundamental is learned from data.
+
+<div style="width: 100%; overflow-x: auto;">
+    <img src="/isp/images/research/ai4cs_agenda.jpg" style="width: 100%; display: block; margin: auto;">
+</div>
+
+In AI4CS we tackle these three problems by designing algorithms able to deal with huge amounts of com- plex, heterogeneous, multisource, and structured data. Firstly, a new generation of targeted AI methods to improve efficiency, prediction accuracy, and uncertainty quantification and error propagation. Secondly, we push the boundaries of a new family of hybrid physics-aware machine learning models that encode physical knowledge about the problem, constraints, inductive biases and domain knowledge, with the goal of attain- ing self-explanatory models learned from empirical data. Finally, the project deals with learning graphical causal models to explain the potentially complex interactions between key observed variables, and discover hidden essential drivers and confounding factors. The AI4CS project vision thus seizes the fundamental prob- lem of moving from correlation to dependence and then to causation through data analysis. The theoretical developments are guided by the inherent ventures of modeling and understanding complex systems at different spatio-temporal resolutions, spheres and interactions.
+
+The long-term vision of AI4CS is tied to open new frontiers and foster research towards algorithms capable of discovering knowledge from data, a stepping stone before the more ambitious far-end goal of machine reasoning in complex systems science. AI4CS is a unified AI research agenda for complex systems, steering modeling and understanding complex systems with advanced AI, from targeted, robust, trustworthy and physics-aware machine learning, with the more ambitious goal of advancing in model interpretability and causality.
+
+The researchers teamed up in AI4CS are led by the Image and Signal Processing (ISP) group at the Universitat de València, and several outstanding researchers join from different institutions to collaborate actively together. The AI4CS research team is formed by 13 senior researchers (42 six-year research periods), and very active (average h = 36). In addition, the ISP includes the help of 5 assistant professors that support key research activities. The team has received funding from the EU excellence pillars in the last years: (1) four ERC grants (consolidator, synergy and proof-of-concept) related to geosciences, visual neuroscience and cli- mate modeling with AI, (2) seven H2020 projects for the development of AI both theoretically and with Earth, vision and societal applications, and (3) are involved in 6 MINECO projects in the intersection of remote sens- ing, Earth and vision sciences. The group is also very active in technology transfer internationally, with projects in collaboration with ESA, NASA, EUMETSAT, Google and Davalor Salud. The group is compromised with higher-level education: ISP lectures in several endorsed masters, participates in COST actions, coordinates activities in an ELLIS research program, and is a core member of ELISE and i-AIDA for the excellence of AI science, transfer and education in Europe. We contribute to knowledge transfer, as well as to the development and adoption of AI in the industry and private sectors.
+
+<div class="col-md-9">
+        <img src="/isp/images/projects/ai4cs/csic.jpg" height="80">
+        <img src="/isp/images/projects/ai4cs/upc.png" height="80">
+        <img src="/isp/images/projects/ai4cs/ua.png" height="80">
+        <img src="/isp/images/projects/ai4cs/upv.png" height="80">
+        <img src="/isp/images/projects/ai4cs/urjc.png" height="60">
+        <img src="/isp/images/projects/ai4cs/logo_uv.png" height="85">
+</div>
+
+The research of the team is recognized worldwide and at a national level, but not regionally yet. The ISP has an excellent track-record, is young, internationally visible, active and productive, but still gender unbalanced and with two relevant areas that need consolidation and support. PROMETEO is the right framework to address these deficiencies, and consolidate our team and AI4CS research as part of the Comunitat Valenciana pole of AI research.
+
+Keywords: Artificial intelligence, Machine learning, causal inference, graphical models, environment, climate change, remote sensing, Earth science, Climate science, Social sciences.

content/projects/projects_links/ai4cs/meetings/_index.md

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+---
+title: "ai4cs"
+type: "projects/generic_projects/meetings"
+
+subtitle1: 
+  title: "Network meetings"
+  class: "label lightblue"
+
+images:
+  - url: "/isp/images/projects/gva.jpg"  
+    height: "150px"
+    class: "img_nm"
+---

content/projects/projects_links/ai4cs/soft+data/_index.md

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+---
+title: "ai4cs"
+type: "projects/generic_projects/soft+data"
+
+subtitles:
+  - title: "Datasets"
+    class: "label lightblue"
+    options:
+      - name: "Image and Signal Processing (ISP) group - Universitat de València datasets repo"
+        url: "/isp/data/"
+      - name: "CSIC datasets repo"
+        url: "/isp/data/"
+      - name: "UAH datasets repo"
+        url: "/isp/data/"
+      - name: "UPC datasets repo"
+        url: "/isp/data/"
+      - name: "UPV datasets repo"
+        url: "/isp/data/"
+
+  - title: "Toolboxes"
+    class: "label lightblue"
+    options:
+      - name: "Image and Signal Processing (ISP) group - Universitat de València codes repo"
+        url: "/isp/code/"
+      - name: "CSIC codes repo"
+        url: "/isp/code/"
+      - name: "UAH codes repo"
+        url: "/isp/code/"
+      - name: "UPC codes repo"
+        url: "/isp/code/"
+      - name: "UPV codes repo"
+        url: "/isp/code/"
+
+  - title: "Websites and applications"
+    class: "label lightblue"
+    options:
+      - name: "Image and Signal Processing (ISP) group - Universitat de València web"
+        url: "/isp/"
+      - name: "CSIC web"
+        url: "/isp/"
+      - name: "UAH web"
+        url: "/isp/"
+      - name: "UPC web"
+        url: "/isp/"
+      - name: "UPV web"
+        url: "/isp/"
+
+
+---

content/projects/projects_links/ai4cs/stays/_index.md

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+---
+title: "ai4cs"
+type: "projects/generic_projects/stays"
+
+subtitles:
+  - title: "Stays and short visits"
+    class: "label lightblue"
+    options:
+      - name: "Jorge Vila Tomás, visits CSIC in 2023-2024"
+      - name: "Deborah Bassotto, visits UAH in 2023, and PIK in 2024"
+      - name: "Nate Mankovich, visits MPI Tübingen in 2023-2024"
+      - name: "Emiliano Diaz, visits Deepmind in 2023-2024"
+      - name: "Vassilis Sitokonstantinou, visits MSR in 2023-2024"
+
+
+  - title: "Planned stays for 2023"
+    class: "label red"
+    options:
+      - name: "URJC-UV just started collaborating in Bayesian physics-aware learning."
+      - name: "UAH-UV just started collaborating in extreme event detection and attribution."
+      - name: "UPM-UV just started collaborating in hybrid modeling for water content modeling."
+      - name: "UPV-UV just started collaborating in methane plume identification and understanding."
+
+---