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Tuesday 5 November
12:30–13:00 Registration Weather Room
Chairperson: J.-N. Thépaut (ECMWF) Lecture Theatre
13:00–13:20 Welcome + Scope and motivations for the workshop V.-H Peuch, ECMWF
13:20–13:40 ECMWF vision for Big Data, AI and cloud Computing Martin Palkovic, ECMWF
13:40–14:00 Copernicus Big Data, the DIASes and AI D. Quintart, EC/DG-GROW
14:00–14:20 The Rise of Artifi cial Intelligence for Earth Observation P.-P. Mathieu, ESA
14:20– 14:40 Progress and challenges for the use of deep learning to improve weather forecast P. Dueben, ECMWF
14:40– 15:00 Network analysis and climate science, global and regional opportunities - remote talk
A. Bracco, F. Falasca, L. Novi, J. Crétat, P. Braconnot, Georgia Tech
15:00–15:30 Coff ee break Lobby
Chairperson: C. Vitolo (ECMWF) Lecture Theatre
15:30–15:50Machine Learning applications in environmental remote-sensing: moving from data reproduction to spatial representation
H. Meyer, C. Reudenbach and T. Nauss, Uni. Muenster and Uni. Marburg
15:50–16:10 Artifi cial Intelligence in Earth Observation - Application in the Copernicus Programme
P. Helber, B. Bischke, J. Hees and A. Dengel, DKFI
16:10–16:30 Super Resolution of SENTINEL- 2 images with Deep Neural Networks
L. De Juan and D. Nobileau, Capgemini
16:30–16:50BIGMIG: Use of Deep Convolutional LSTMs and Sentinel 2 for spatio-temporal semantic segmentation of smallholder agriculture in Mozambique
J. Reay, A. Prieto Nemesio, E. C. de Grandi, A. S. López, GMV
16:50–17:10On the detection of metocean features on SAR images using Deep Learning: perspectives for Copernicus Sentinel-1
N. Longepe, C. Wang, A. Mouche, P. Tandeo and R. Husson, CLS
17:10–17:30 Potential of Deep Learning Technique in Satellite based Fire Detection and Emission Estimation Study
T. Zhang, M. Wooster and D. Fisher, King’s College London
17:30–17:50
Python for Earth Observation (PYEO) - a machine-learning-based, automated processing chain for Sentinel-2 image stack classifi cation and change detection
Y. Gou, Uni. Leicester
18:00–20:00 Poster session (drinks and fi nger food) Weather Room, Lobby and Canteen
Copernicus Climate Change ServiceCopernicus Atmosphere Monitoring Service
IMPLEMENTED BY
The 1st Artifi cial Intelligence for Copernicus Workshop. ECMWF, Reading (UK)5-7 November 2019
AgendaAttend remotely using the vimeo invitationhttps://vimeo.com/event/16576
Thursday 7 November
08:30–09:00 Registration Weather Room
09:00–13:00 4 parallel discussion sessions continued
1. Lecture Theatre Splinter group 1 2. Large Committee Room Splinter group 23. Meeting Room 1 Splinter group 34. Meeting Room 6 Splinter group 4
10:45–11:15 Coff ee break Lobby
13:00–14:00 Lunch ECMWF Canteen
Chairperson: V.-H. Peuch ECMWF Lecture Theatre
14:00–14:2014:20–14:4014:40–15:0015:00–15:20
Report from Splinter group 1 discussionReport from Splinter group 2 discussionReport from Splinter group 3 discussionReport from Splinter group 4 discussion
15:20–16:00 General discussion and concluding remarks J.-N. Thepaut and F. Rabier, ECMWF
16:00 End of the workshop
Wednesday 6 November
08:30–09:00 Registration Weather Room
Chairperson: S. Siemen (ECMWF) Lecture Theatre
09:00–09:20 Using Deep Learning to identify weather patternsJ. Kunkel, B. Lawrence, D. Galea and J. Adie, Uni. Readingand NVIDIA AI Tech Center
09:20–09:40 Deep Learning application for High Energy Physics: examples from the LHC S. Vallecorsa, CERN
09:40–10:00 Fusing radar and imager data for improved cloud classification D. Watson-Parris et al., Uni. Oxford
10:00–10:20 Using Self-Organising Maps to understand non-linear cloud-circulation couplings
S. Adams and M. Webb, UK Met Offi ce
10:20–10:40 Improving Advection Baselines for Precipitation Nowcasting with Deep Learning
K. Lenc, S. Ravuri, P. Mirowski, M. Wilson and S. Mohamed, Deepmind
10:45–11:15 Coff ee break Lobby
Chairperson: P. Dueben (ECMWF) Lecture Theatre
11.20–11:40 Deep Learning for satellite precipitation estimation S. Dewitte, A. Moraux, B. Cornelis, A. Munteanu, RMIB and VUB
11:40–12:00 Downscaling of Low Resolution Wind Fields using Neural Networks
M. Kern and K. Höhlen, Technical Uni. Munich
12:00–12:20 Can neural networks be effective replacements for parameterisation schemes?
M. Chantry, T. Palmer and P. Dueben, Uni. Oxford and ECMWF
12:20–12:40 On the use of CAMS data to improve air quality regional maps and forecasts through data fusion
J. Sousa, B. Maiheu, S. Vranckx, L. Janssens, VITO
12:40–13:00 A fl ood forecasting case study using diff erent machine learning models (ESoWC prize 2019)
L. Kugler and S. Lehner, Uni. Vienna
13:00–14:00 Lunch ECMWF Canteen
Chairperson: V.-H. Peuch (ECMWF) Lecture Theatre
14:00–14:20 Wavelet-based retrieval of weather analogues from ERA5 B. Raoult, ECMWF
14:20–14:40 Developing AI activities in the British Antarctic Survey A. Fleming, A. Faul and S. Hosking, British Antarctic Survey
14:40–15:00 Challenges in Bayesian Network Modelling of Climate and Weather Data - remote talk M. Scutari, IDSIA
15:00–15:20 Exascale Deep Learning for climate analytics - remote talk T. Kurth, LBL
15:20–15:40 On the suitability of convolutional neural networks for climate downscaling
J. Baño-Medina, R. Manzanas and J. M. Gutiérrez, Universidad de Cantabria
15:40–16:00 Predicting vegetation health in Kenya using Machine Learning and climate data (ESoWC prize 2019)
T. Lees, G. Tseng, S. Reece, S. Dadson, Uni. Oxford and Okra Solar
16:00–16:30 Coff ee break Lobby
16:30–18:00 4 parallel discussion sessions
16:30–16:45 Introduction to the discussion sessions (in splinter groups)
1. Tackling challenges in satellite-based climate monitoring with Artifi cial Intelligence
2. ADAM: a geospatial data hub for AI applications
3. Using the CMEMS data to feed a high-resolution process-based model in order to develop a sewage management tool based on artifi cial neural networks: Application to the sanitation system of Muskiz
4. Automatic land categorisation by processing S-2 images with transfer learned CNN
5. Machine Learning meets Wavelets in Magnetic Earth Observation
6. Post-processed correction of systematic numerical weather prediction temperature errors using machine learning
7. Automatic young tree detection on SAR data using machine learning algorithms
8. Resource management of image-processing workflows with Deep Reinforcement Learning
9. AsSISt: Aircraft Support & Maintenance Service
10. Convolutional Neural Networks for evaluating atmospherically-forced sea level variations
11. Swedish National Space Data Lab on Kubernetics
12. Lessons learned training AI cloud products for CLARA-A3 and CLAAS-3, with reduced retrieval
1. Lecture Theatre Splinter group 1 2. Large Committee Room Splinter group 23. Meeting Room 1 Splinter group 34. Meeting Room 6 Splinter group 4
U. Pfeifroth and S. Finkensieper, DWD
R. M. Figuera and S. Natali, SISTEMA GmbH
J. Garcia-Alba, Universidad de Cantabria
G. Margarit-Martin and O. Dutta,GMV
O. Kounchev,Bulgarian Academy of Science
R. Isaksson, SMHI
S. Daniel and S. Angeli
L. De Juan, Capgemini
L. De Juan, Capgemini
M. Lalande et al., CNES & IGE Uni. Grenoble Alpes
G. Kovács et al., Luleå Uni. Of Technology
Nina Håkansson, SMHI