Dr. André Lage-Freitas is an Assistant Professor at Universidade Federal de Alagoas (Brazil) and member of LaCCAN lab (Laboratório de Computação Científica e Análise Numérica). He holds a Ph.D. in Computer Science from the Institut National des Sciences Appliquées de Rennes (INRIA, France). His main research interest lies on how to ease the use of Cloud computing infrastructures for Data Scientists by focusing on big remote sensing data sets. He held for two years the Research Coordinator (Manager) position under the UFAL Vice-Presidency of Research and Graduate Course. He has experience in international research projects such as CloudArray project (coordinator) funded by Microsoft Azure Research and further international projects funded by European Union and Latin America, e.g., EOxposure H2020, S-CUBE FP7, CONICYT (Chile), PUC Valparaiso, and FAPEAL (Brazil). He is a member of the IEEE and of the SBC (Sociedade Brasileira de Computação). He is Counsellor of IEEE-UFAL Student Branch and Enactus UFAL. He reviews research projects for FAPESP (São Paulo Research Foundation) and he is a reviewer of the IEEE Geoscience and Remote Sensing Letters journal.

Google Scholar: http://goo.gl/mlPor0

ORCID: http://orcid.org/0000-0001-6533-9863

Lattes CV: http://lattes.cnpq.br/3203407648310274

Effortless huge imagery processing in the Cloud - The case of the Image Processing Service (IPS)

Cloud computing technologies provide on-demand services for computing and storing a huge amount of data. Nevertheless, using these services implies handling low-level operational details such as creating, starting, stopping virtual instances in addition to dealing with data storage issues. In this talk, I'll present some current challenges and the Image Processing Service (IPS) which enables the processing of huge data sets by using cloud computing through an easy user interface. We validated the preliminary version of IPS by processing gigabytes of fully Polarimetric Synthetic Aperture Radar (PolSAR) data sets in the Microsoft Azure Cloud. The evaluation uses actual PolSAR data collected from the NASA Unmanned Aerial Vehicle SAR (UAVSAR) Project. The methodology employed is fully reproducible and relies on Free/Libre and Open Source Software (FLOSS).