EXPLAINABLE AI PIPELINES FOR BIG COPERNICUS DATA

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Creation of training datasets, data cubes & ontologies - v2

This is an update scaleup of the v1 deliverable

DeepCube Platform - v1

Platform integration and system

DeepCube Platform - v2

Updated version of based on changes made to DeepCube’s platform. In addition, this deliverable includes the user manual and documentation of the platform.

Analytics and DL architectures for fire risk assessment UC-v1

Report containing a consolidate version of all DL architectures that DeepCubes pilots will implement

Analytics and DL architectures for the droughts UC- v1

Report containing a consolidate version of all DL architectures that DeepCubes pilots will implement

Final dissemination and communication report

Detailed report of all activities (M01-M36) that took place during the course of the project.

Project Management & Quality plan

Guidelines to ensure high quality research development and reporting along with a project time plan

DeepCube platform requirements, specs and architecture-v2

Updated version of the technical requirements deliverable based on any updates from 1st Use Case implementation and evaluation cycle

EO and non-EO data ingestion report - v2

This is an update of the v1 deliverable.

DeepCube technical components-v1

Inputs on developments adjustments finetuning of the technological components

DeepCube technical components - v2

Updates from 1st Use Case implementation and evaluation cycle This deliverable also includes user manuals and documentations for the individual components

Status of Liaison activities v2

Updates on liaison activities

Initial Communication and Dissemination plan

At the early stage of the proposal to update communication plan and dissemination plan identifying target audiences key messages channels tools and metrics

DeepCube platform requirements, specs and architecture-v1

Technical specifications and the architectural design of the DeepCube platform

EO and non-EO data ingestion report - v1

Information on all data ingested along with precise information on data origin

Mid-term dissemination plan

Report updates on dissemination and communication plan for the next period M18M36 including also a detailed summary of the main activities that took place during the first 18 months

Status of Liaison activities v1

Provision of tangible liaison activities with other projects

Creation of training datasets, data cubes & ontologies-v1

Describe in detail both the process and the generated trained datasets. Report all technical specifications and stored datasets stored on all Data Cubes. This report provides the ontologies and mappings to be used for realizing the DeepCube Semantic Cube. The deliverable provides the data cubes with ARD per se, to allow further exploitation.

Data Management Plan v2

Updated version of the DMP report

Data Management Plan v1

DMP formulation contributing in making data FAIR

Website & Material

Website up and running along with any other material will support outreach activities brochures leaflets video newsletter etc

Pubblicazioni

Pluto: A global volcanic activity early warning system powered by large scale self-supervised deep learning on InSAR data

Autori: Nikolaos Ioannis Bountos, Dimitrios Michail, Themistocles Herekakis, Angeliki Thanasou, Ioannis Papoutsis
Pubblicato in: EGU General Assembly 2023, 2023
Editore: European Geosciences Union
DOI: 10.5194/egusphere-egu23-5913

Learning drivers of climate-induced human migrations with Gaussian processes

Autori: Jose M. Tarraga, Maria Piles, Gustau Camps-Valls
Pubblicato in: NeurIPS 2020 Workshop on Machine Learning for the Developing World, 2020
Editore: NeurIPS 2020 Workshop on Machine Learning for the Developing World

Sen4AgriNet: A Harmonized Multi-Country, Multi-Temporal Benchmark Dataset for Agricultural Earth Observation Machine Learning Applications

Autori: D. Sykas, I. Papoutsis, D. Zografakis
Pubblicato in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Numero 12 October 2021, 2021, Pagina/e 5830-5833
Editore: IEEE
DOI: 10.1109/igarss47720.2021.9553603

Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean

Autori: Spyros Kondylatos, Ioannis Prapas, Gustau Camps-Valls, Ioannis Papoutsis
Pubblicato in: 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks, 2023
Editore: NeurIPS
DOI: 10.48550/arxiv.2306.05144

Deep Learning Methods for Daily Wildfire Danger Forecasting

Autori: Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis, Gustau Camps-Valls, Michele Ronco, Miguel-Ángel Fernández-Torres, Maria Piles Guillem, Nuno Carvalhais
Pubblicato in: Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response, 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Numero 4 November 2021, 2021
Editore: NeurIPS

Inspecting the link between climate and human displacement with Explainable AI and Causal inference

Autori: José María Tárraga Habas, Michele Ronco, Maria Teresa Miranda, Eva Sevillano Marco, Qiang Wang, María Piles, Jordi Muñoz, and Gustau Camps-Valls
Pubblicato in: EGU General Assembly 2022, 2022
Editore: EGU22-11200
DOI: 10.5194/egusphere-egu22-11200

Explainable deep learning for wildfire danger estimation

Autori: Ronco, M., Prapas, I., Kondylatos, S., Papoutsis, I., Camps-Valls, G., Fernández-Torres, M.-Á., Piles Guillem, M., and Carvalhais, N.
Pubblicato in: EGU General Assembly 2022, 2022
Editore: EGU22-11787
DOI: 10.5194/egusphere-egu22-11787

AutoAblation: Automated Parallel Ablation Studies for Deep Learning

Autori: Sina Sheikholeslami, Moritz Meister, Tianze Wang, Amir H. Payberah, Vladimir Vlassov, Jim Dowling
Pubblicato in: EuroMLSys '21: Proceedings of the 1st Workshop on Machine Learning and Systems, 2021, Pagina/e 55-61
Editore: ACM
DOI: 10.1145/3437984.3458834

DEEPCUBE: EXPLAINABLE AI PIPELINES FOR BIG COPERNICUS DATA

Autori: Ioannis Papoutsis, Alkyoni Baglatzi, Souzana Touloumtzi, Markus Reichstein, Nuno Carvalhais, Fabian Gans, Gustau Camps-Valls, Maria Piles, Theofilos Kakantousis, Jim Dowling, Manolis Koubarakis, Dimitris Bilidas, Despina-Athanasia Pantazi, George Stamoulis, Christophe Demange, Leo-Gad Journel, Marco Bianchi, Chiara Gervasi, Alessio Rucci, Ioannis Tsampoulatidis, Eleni Kamateri, Tarek Habib, Alejan
Pubblicato in: Proceedings of the 2021 conference on Big Data from Space (BiDS’21), 2021
Editore: Proceedings of the 2021 conference on Big Data from Space (BiDS’21)

Hephaestus: A large scale multitask dataset towards InSAR understanding

Autori: N.I. Bountos, I. Papoutsis, D. Michail, A. Karavias, P. Elias, I. Parcharidis
Pubblicato in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022
Editore: IEEE
DOI: 10.48550/arxiv.2204.09435

Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs

Autori: Claire Robin, Christian Requena-Mesa, Vitus Benson, Lazaro Alonso, Jeran Poehls, Nuno Carvalhais, Markus Reichstein
Pubblicato in: Tackling Climate Change with Machine Learning: workshop at NeurIPS 2022, 2022
Editore: NeurIPS 2022
DOI: 10.48550/arxiv.2210.13648

Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification

Autori: Maria Ioannidou; Alkiviadis Koukos; Vasileios Sitokonstantinou; Ioannis Papoutsis; Charalampos Kontoes
Pubblicato in: Remote Sensing; Volume 14; Numero 22; Pages: 5739, Numero 13 November 2022, 2022, ISSN 2072-4292
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/rs14225739

Integration of a Deep-Learning-Based Fire Model Into a Global Land Surface Model

Autori: Rackhun Son, Tobias Stacke, Veronika Gayler, Julia E. M. S. Nabel, Reiner Schnur, Lazaro Alonso, Christian Requena-Mesa, Alexander J. Winkler, Stijn Hantson, Sönke Zaehle, Ulrich Weber, Nuno Carvalhais
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero Volume 16, Numero1, 2024, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2023ms003710

Self-supervised Contrastive Learning for Volcanic Unrest Detection

Autori: Nikolaos Ioannis Bountos, Ioannis Papoutsis, Dimitrios Michail, Nantheera Anantrasirichai
Pubblicato in: IEEE Geoscience and Remote Sensing Letters, Numero Volume 19, 2021, Pagina/e 1-5, ISSN 1558-0571
Editore: IEEE
DOI: 10.1109/lgrs.2021.3104506

A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning

Autori: Dimitrios Sykas; Maria Sdraka; Dimitrios Zografakis; Ioannis Papoutsis
Pubblicato in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Numero Volume 15, 2022, ISSN 2151-1535
Editore: IEEE
DOI: 10.1109/jstars.2022.3164771

Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models

Autori: Shanning Bao, Lazaro Alonso, Siyuan Wang, Johannes Gensheimer, Ranit De, Nuno Carvalhais
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero Volume 15, Numero 8, 2023, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2022ms003464

Exploring interactions between socioeconomic context and natural hazards on human population displacement

Autori: Michele Ronco, José María Tárraga, Jordi Muñoz, María Piles, Eva Sevillano Marco, Qiang Wang, Maria Teresa Miranda Espinosa, Sylvain Ponserre, Gustau Camps-Valls
Pubblicato in: Nature Communications, Numero 14, 2023, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-023-43809-8

Role of locality, fidelity and symmetry regularization in learning explainable representations

Autori: Michele Ronco, Gustau Camps-Valls
Pubblicato in: Neurocomputing, Numero Volume 562, 2023, ISSN 0925-2312
Editore: Elsevier BV
DOI: 10.1016/j.neucom.2023.126884

Wildfire Danger Prediction and Understanding With Deep Learning

Autori: S. Kondylatos, I. Prapas, M. Ronco, I. Papoutsis, G. Camps-Valls, M. Piles, M. Fernández-Torres, N. Carvalhais
Pubblicato in: Geophysical Research Letters, Numero Volume 49, Numero17, 2022, ISSN 1944-8007
Editore: American Geophysical Union
DOI: 10.1029/2022gl099368

Learning From Synthetic InSAR With Vision Transformers: The Case of Volcanic Unrest Detection

Autori: Nikolaos Ioannis Bountos; Dimitrios Michail; Ioannis Papoutsis
Pubblicato in: IEEE Transactions on Geoscience and Remote Sensing (Volume 60), Numero 08 June 2022, 2022, ISSN 1558-0644
Editore: IEEE
DOI: 10.1109/tgrs.2022.3180891

Benchmarking and scaling of deep learning models for land cover image classification

Autori: Ioannis Papoutsis, Nikolaos Ioannis Bountos, Angelos Zavras, Dimitrios Michail, Christos Tryfonopoulos
Pubblicato in: ISPRS Journal of Photogrammetry and Remote Sensing, Numero Volume 195, January 2023, 2023, ISSN 1872-8235
Editore: Elsevier
DOI: 10.1016/j.isprsjprs.2022.11.012

Learning class prototypes from Synthetic InSAR with Vision Transformers

Autori: Nikolaos Ioannis Bountos, Dimitrios Michail, Ioannis Papoutsis
Pubblicato in: 2022
Editore: arXiv
DOI: 10.48550/arxiv.2201.03016

Efficient deep learning models for land cover image classification

Autori: Ioannis Papoutsis, Nikolaos-Ioannis Bountos, Angelos Zavras, Dimitrios Michail, Christos Tryfonopoulos
Pubblicato in: 2021
Editore: arXiv

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Risultati finali

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