The 1^st BigSkyEarth Training School was held in the Institute for Remote Sensing Methodology at the German Aerospace Center DLR Oberpfaffenhofen in Germany.

The Training School gathered 33 trainees from 14 countries with expert instructors from Europe and the US for hands-on training on Big Data analytics.

Since the identified challenges are similar in astronomy and Earth Observation, with signal processing, statistics, machine learning, and computer science as the common denominator, the Training School has aimed at boosting the communication within and between disciplines and applications areas, by propagating and advancing relevant common solutions developed within Big Data analysis and management research and industrial environments.

The goal has been to share valuable know-how encompassing from sensor and data modelling, features extraction and metadata, information representation, data structures, pattern recognition, statistical/machine learning, data analytics, advanced visualisation, to data mining and KDD. Specific computer science topics have been addressed as particular programming techniques, cloud computing methodologies and related topics. The key aspect has been to address all these topics in synergy, setting them up in a logical interdisciplinary framework bridging the diverse areas addressed by the school.

The participants were selected for the school based on the quality and relevance of the research project descriptions provided as part of their application procedures. Their backgrounds ranged from astronomy — around 40% — to remote sensing — 55% — and computer science. The institutions they work for go from observatories to startup companies. About 20% were women, again from both astronomical and Earth observation backgrounds.

After acceptance in the school, they prepared for the attendance by going through the JPL-Caltech Summer School on the Coursera platform. After that they gathered at the Oberpfaffenhofen site of the German Aerospace Center DLR for a week of theoretical and practical work. Lessons were given on topics ranging from feature extraction for astronomical time series to large scale machine learning and visualization.

Speakers included Ashish Mahabbal from Caltech (USA), Mihai Datcu from the German Aerospace Center DLR (Germany), Brian Mac Namee from the School of Computer Science at University College Dublin (Ireland), Stefano Cavuoti from the Osservatorio Astronomico di Capodimonte (Italy), Marco Quartulli from Vicomtech-IK4 (Spain), Maria Gritsevich from the Finnish Geospatial Research Institute (Finland), Giovanni Nico from CNR IAC (Italy), Laurent Eyer from the University of Geneva (Switzerland) and Eric Feigelson from Penn State University (USA).

Cross-domain working groups of participants were established carrying forward specific data analysis projects focusing on regression tasks, high-dimensionality analytics, feature engineering and clustering, supervised classification and thematic mapping, and visualization. The material for the course — presentations and hands-on work notebooks by the speakers and the material being produced by the participant groups — is in the process of being published online.

List of trainees:

Name	country
Gor Mikayelyan	Armenia
Mihran Vardanyan	Armenia
Praveen K. Pandey	Belgium
Darko Zaric	Croatia
Matti Aleksanteri Eskelinen	Finland
Billy Braithwaite	Finland
Zhang Chao	France
Nestor Koueya	France
Sina Montazeri	Germany
Fathalrahman Adam	Germany
Hu Jingliang	Germany
Kersten Clauss	Germany
Homa Ansari	Germany
Margherita Grossi	Germany
Patrick Hannawald	Germany
Danfeng Hong	Germany
Dimitris Marmanis	Germany
Mohammad Omidalizarandi	Germany
Melinda Nagy	Hungary
Patricia O’Byrne	Ireland
Luigi Mascolo	Italy
Rafał Wojaczyński	Poland
Justyna Średzińska	Poland
Andreea Griparis	Romania
Alexandru-Cosmin Grivei	Romania
Aleš Marsetič	Slovenia
Klemen Čotar	Slovenia
Iñaki Arruabarrena	Spain
Daniel Garabato Míguez	Spain
Carlos de Hoyos Fernández de Córdova	Spain
Maria Manolopoulou	UK
Christopher Frohmaier	UK