ExtremeEarth Open Workshop - Introduction
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The document provides an agenda for a three-hour online workshop on the ExtremeEarth project. The workshop will include presentations on using the ExtremeEarth platform for deep learning applications in earth observation, such as classifying sea ice and crop types from satellite imagery. It will also cover scaling deep learning pipelines with earth data and interlinking and querying big geospatial data sets. The document lists the presentation topics, speakers, and times on the agenda, as well as meeting access details and contact information for the workshop organizer.
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![Three Years of the
ExtremeEarth Project
09/12/2021
Online
Workshop
Program
Agenda Times are in CET
09:00 - 09:15 ExtremeEarth: Overview and achievements
Manolis Koubarakis - National and Kapodistrian University of Athens
09:15 - 10:15 ExtremeEarth: Water Demand and Water Availability for Agriculture and
Food Security in two European Catchments
Florian Appel, Eva Gleisberg - VISTA GmbH
Lorenzo Bruzzone, Daniele Marinelli, Giulio Weikmann - University of Trento
10:15 - 11:15 ExtremeEarth: Applying Deep Learning to Classify Sea Ice in the Polar
Regions
Alistair Everett, William Copeland - MET Norway Andrew Fleming - UKRI-BAS
David Arthurs - Polar View
11:15 - 12:00 The ExtremeEarth platform: Scalable Deep Learning pipelines with
Earth Observation data and Hopsworks
Theofilos Kakantousis - Logical Clocks
Desta Haileselassie Hagos - KTH Royal Institute of Technology
12:00 - 13:00 Lunch break
13:00 - 13:30 AI Crop Type Mapping
Lorenzo Bruzzone, Giulio Weikmann - University of Trento
13:30 - 14:00 Deep Learning Algorithms for Sea Ice Classification from Sentinel-1
SAR images: Capabilities and Challenges
Torbjørn Eltoft, Salman Khaleghian, Qiang Wang - UiT the Arctic University of Norway
14:00 - 14:30 EO Benchmarking Tool and Deep Learning Algorithms for Polar Areas
Octavian Dumitru, Mihai Datcu, Wei Yao - German Aerospace Center (DLR)
14:30 - 15:00 Interlinking big linked geospatial data
George Papadakis - National and Kapodistrian University of Athens
15:00 - 15:30 Querying big linked geospatial data
Dimitris Bilidas - National and Kapodistrian University of Athens
15:30 - 16:00 Federating big linked geospatial data
Antonis Troumpoukis - NCSR Demokritos
16:00 - 17:00 Q&A
Meeting Room Information
Link: https://uoa.webex.com/uoa/j.php?MTID=mb1e4e094eb891dc1e0c84400b7cc393e
Meeting number: 2732 507 1159
Password: vDkxrXPJ443
Contact Information
Prof. Manolis Koubarakis
Department of Informatics and Telecommunications
National and Kapodistrian University of Athens
University Campus, Ilisia
15784 Athens Greece
+30 210 727 5213
koubarak [at] di.uoa.gr
This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 825258.](https://image.slidesharecdn.com/extremeearth-workshop-info-211217133553/85/ExtremeEarth-Open-Workshop-Introduction-1-320.jpg)
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ExtremeEarth Open Workshop - Overview and Achievements
This document summarizes the objectives and achievements of the ExtremeEarth project, which developed artificial intelligence and big data techniques to analyze large volumes of Copernicus Earth observation data. The project created scalable deep learning models and large training datasets for food security and polar use cases. It also developed linked geospatial data systems that can handle petabyte-scale Copernicus data. The techniques were integrated into the Hopsworks platform and deployed on CREODIAS to enable extreme Earth analytics.
AI models for Ice Classification - ExtremeEarth Open Workshop
(1) Deep learning algorithms show potential for sea ice classification from SAR images but face challenges from scarce and inaccurate training data.
(2) Researchers generated training datasets by manually labeling SAR image patches with ice types, assisted by optical images.
(3) A modified VGG-16 network trained on augmented SAR patch data achieved 97.3% accuracy classifying ice vs water.
Big Linked Data Interlinking - ExtremeEarth Open Workshop
This document discusses approaches for interlinking big linked geospatial data. It describes detecting proximity and topological relations between geospatial entities. Examples of topological relations identified include a linestring touching a polygon, a linestring intersecting another linestring, and a polygon containing another polygon. The document also discusses challenges like quadratic time complexity and introduces techniques like progressive and approximate geospatial interlinking to improve efficiency and scalability.
Food Security Use Case - ExtremeEarth Open Workshop
The document discusses using Earth observation data and modeling to assess water availability and demand for agriculture in two European river basins. It describes combining Sentinel-2 satellite imagery processing with crop mapping, water balance modeling, and linked data platforms to generate information on crop types, water availability, irrigation needs, and yields. Case studies are presented for the Danube and Douro River basins focusing on integrating these data and tools to support sustainable food production and irrigation management decisions.
Artificial Intelligence and Big Data Technologies for Copernicus Data: the Ex...
The ExtremeEarth Project developed artificial intelligence and big data techniques to analyze large volumes of Copernicus Earth observation data. Key aspects included developing deep learning models for crop mapping and sea ice classification using large training datasets. Technologies were integrated into the Hopsworks platform and deployed on CREODIAS to enable distributed deep learning at extreme scales for food security and polar use cases. The goal was to make these techniques and platforms openly available to support the European EO industry.
ExtremeEarth Data Science Pipeline for Linked Earth Observation Data
Presentation in Data Week 2021. The main objective of this workshop is to bring together four pioneer H2020 projects that are at the frontier of European research and innovation and are developing Artificial Intelligence and Big Data technologies for Copernicus data. These projects are ExtremeEarth (http://earthanalytics.eu/), AI4Copernicus (https://ai4copernicus-project.eu/), DeepCube (https://deepcube-h2020.eu/) and CALLISTO (https://callisto-h2020.eu/). The first two of the projects have been funded by ICT calls while the other two have been funded by DT-SPACE calls.
Artificial Intelligence in the Earth Observation Domain: Current European Res...
The document summarizes four European projects applying artificial intelligence to earth observation data: ExtremeEarth, AI4Copernicus, DeepCube, and Callisto. ExtremeEarth develops scalable AI and big data techniques for food security and polar use cases. AI4Copernicus aims to make the AI4EU platform useful for Copernicus data users through open calls. DeepCube focuses on explainable AI for problems like drought forecasting. Callisto combines satellite and other data through digital twins to support sustainable development. The projects emphasize distributed deep learning, semantic technologies, cloud/edge computing, and high-impact use cases.
Snow Monitoring for Water Availability and Irrigation
Presentation by VISTA on techniques for snow monitoring to create water availability and irrigation models for crops.
Polar Use Case in ExtremeEarth-phiweek19
This document discusses using machine learning to help ships navigate safely in polar regions by providing updated sea ice information. It notes that polar regions are experiencing increased activity and risk of accidents. The Polar Code requires ships have up-to-date ice information. Current ice charts can be out of date or require expert interpretation. The project aims to use satellite data and machine learning to generate high resolution, frequently updated ice products to minimize risks. Challenges include needing extensive training data and addressing the scale and resolution of satellite imagery. Potential applications include detecting ice edges, types and concentrations as well as icebergs.
The ExtremeEarth infrastructure-phiweek19
1) ExtremeEarth is a project that develops an infrastructure for storing, accessing, processing, analyzing and visualizing large amounts of Copernicus data.
2) The infrastructure consists of multiple layers including product layers called Thematic Exploitation Platforms (TEPs), a processing layer called Hopsworks, and a data layer using the CREODIAS DIAS.
3) Hopsworks is an open-source platform for AI that supports data science and machine learning workflows and can run on various cloud platforms. It provides tools for data engineering, machine learning pipelines and model serving.
Scalable Deep Learning in ExtremeEarth-phiweek19
This document summarizes a presentation about scalable deep learning techniques for analyzing Copernicus Earth observation data using the Hopsworks platform. The presentation discusses Hopsworks' end-to-end machine learning pipelines, feature engineering capabilities, distributed deep learning techniques like data parallel training, and applications of these techniques to challenges in classifying satellite imagery like sea ice mapping. Deep learning architectures, preprocessing steps, and distributed training methods are highlighted as areas of ongoing work and improvement for analyzing large volumes of remote sensing data on Hopsworks.
Food security use case in ExtremeEarth-phiweek19
The document discusses using extreme data analytics and earth observation to manage food security on an extremely dynamic planet. It focuses on a use case analyzing water availability and irrigation recommendations in the Danube river basin. Key techniques include using Sentinel-2 data and deep learning models to map crop types over large areas and monitor crop conditions. Hydrological modeling combines this with snow cover, soil moisture, and reservoir data to assess water availability. The results can provide basin-wide information layers and field-specific irrigation recommendations to users.
Big linked geospatial data tools in ExtremeEarth-phiweek19
Introduction to the big linked geospatial data tools that are implemented in the ExtremeEarth project
Copernicus and AI workshop 2020
Copernicus is one of the largest Earth Observation data providers. Copernicus’ archives are growing data repositories containing a wealth of data and information that is of utmost importance for policy support and many economic and industrial domains.
AI needs vast amounts of data to be developed. AI works by identifying patterns in available data and then applying this knowledge to new data. The larger the data set is, the better AI can learn and discover. Bringing AI technologies that scale at the Petabyte level to Copernicus operations is an opportunity that Europe shall seize to maximise return on investment and to develop a new generation of products and services based on Copernicus data assets.
This workshop will present EU programmes, challenges and opportunities to connect Copernicus, its data assets and stakeholders to the digital world.
LPS19 ExtremeEarth Project
ExtremeEarth is a H2020 project that aims to develop extreme data analytics techniques using big Copernicus data and apply these technologies to food security and polar use cases. The project involves 11 partners from 7 countries and has a budget of ~6 million Euro over 36 months. It will integrate artificial intelligence and deep learning methods to extract information from Copernicus satellite imagery for applications in monitoring crop growth/yield and sea ice conditions. The food security use case will generate water availability maps for irrigation management while the polar use case focuses on automated regional sea ice charts for maritime safety.
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ExtremeEarth Open Workshop - Introduction
- 1. Three Years of the ExtremeEarth Project 09/12/2021 Online Workshop Program Agenda Times are in CET 09:00 - 09:15 ExtremeEarth: Overview and achievements Manolis Koubarakis - National and Kapodistrian University of Athens 09:15 - 10:15 ExtremeEarth: Water Demand and Water Availability for Agriculture and Food Security in two European Catchments Florian Appel, Eva Gleisberg - VISTA GmbH Lorenzo Bruzzone, Daniele Marinelli, Giulio Weikmann - University of Trento 10:15 - 11:15 ExtremeEarth: Applying Deep Learning to Classify Sea Ice in the Polar Regions Alistair Everett, William Copeland - MET Norway Andrew Fleming - UKRI-BAS David Arthurs - Polar View 11:15 - 12:00 The ExtremeEarth platform: Scalable Deep Learning pipelines with Earth Observation data and Hopsworks Theofilos Kakantousis - Logical Clocks Desta Haileselassie Hagos - KTH Royal Institute of Technology 12:00 - 13:00 Lunch break 13:00 - 13:30 AI Crop Type Mapping Lorenzo Bruzzone, Giulio Weikmann - University of Trento 13:30 - 14:00 Deep Learning Algorithms for Sea Ice Classification from Sentinel-1 SAR images: Capabilities and Challenges Torbjørn Eltoft, Salman Khaleghian, Qiang Wang - UiT the Arctic University of Norway 14:00 - 14:30 EO Benchmarking Tool and Deep Learning Algorithms for Polar Areas Octavian Dumitru, Mihai Datcu, Wei Yao - German Aerospace Center (DLR) 14:30 - 15:00 Interlinking big linked geospatial data George Papadakis - National and Kapodistrian University of Athens 15:00 - 15:30 Querying big linked geospatial data Dimitris Bilidas - National and Kapodistrian University of Athens 15:30 - 16:00 Federating big linked geospatial data Antonis Troumpoukis - NCSR Demokritos 16:00 - 17:00 Q&A Meeting Room Information Link: https://uoa.webex.com/uoa/j.php?MTID=mb1e4e094eb891dc1e0c84400b7cc393e Meeting number: 2732 507 1159 Password: vDkxrXPJ443 Contact Information Prof. Manolis Koubarakis Department of Informatics and Telecommunications National and Kapodistrian University of Athens University Campus, Ilisia 15784 Athens Greece +30 210 727 5213 koubarak [at] di.uoa.gr This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825258.