=Paper=
{{Paper
|id=Vol-2994/tutorial1
|storemode=property
|title=Machine and Deep Learning for Earth Observation: Advanced Approaches and Practical Use Cases
|pdfUrl=https://ceur-ws.org/Vol-2994/tutorial1.pdf
|volume=Vol-2994
|authors=Dino Ienco,Roberto Interdonato
|dblpUrl=https://dblp.org/rec/conf/sebd/IencoI21
}}
==Machine and Deep Learning for Earth Observation: Advanced Approaches and Practical Use Cases==
<pdf width="1500px">https://ceur-ws.org/Vol-2994/tutorial1.pdf</pdf>
<pre>
Tutorial: Machine and Deep Learning for Earth
Observation: Advanced Approaches and Practical
Use Cases – Abstract
Dino Ienco1,3 , Roberto Interdonato2,3
1
  INRAE, TETIS, Montpellier, France
2
  Cirad, TETIS, Montpellier, France
3
  TETIS, Univ. of Montpellier, APT, Cirad, CNRS, INRAE, Montpellier, France


                                         Abstract
                                         Nowadays, modern space missions continuously collect information about the earth surface that cor-
                                         responds to massive amounts of data. The multitude of Earth Observation (EO) systems allows the
                                         acquisition of data via different sensors (e.g., optical, radar, LiDAR) at different spatial and temporal
                                         resolutions, with diverse spectral characteristics. This huge and diverse volume of information opens
                                         up new opportunities to better understand and monitor agricultural, natural and anthropized spaces at
                                         different scales.
                                             In this context, data-intensive methodologies such as machine and deep learning approaches are
                                         demonstrating their value, as they already did in several domains dealing with signal data. Multiple
                                         data science challenges were already addressed using satellite imagery (e.g., building footprints, road
                                         networks, iceberg detection) but crucial open questions remain unsolved (e.g., biodiversity monitoring,
                                         urban mapping, deforestation tracking and food risk prevention, triaging disaster zones). We are at
                                         the beginning of a new era for the analysis of Earth Observation data (EOD) where one of the main
                                         questions is how to leverage the complementarity and the diversity of the information collected by the
                                         different available observation systems, in order to answer important societal challenges and monitor
                                         changes on the Earth Surface.
                                             The MDL4EO team (Machine and Deep Learning for Earth Observation) at the UMR TETIS (Mont-
                                         pellier, France) has the objective to scientifically contribute to this new era providing AI methods and
                                         algorithms able to extract valuable knowledge from massive heterogeneous Earth Observation Data.
                                             In this tutorial, we will discuss in detail the main research questions addressed by the MDL4EO team:
                                         How to exploit data streams of satellite Images (Satellite image Time Series) in order to characterize
                                         natural and agricultural areas? How to effectively combine multi-sensor and multi-scale information?
                                         How to transfer knowledge between different geographical areas?
                                             It’s time to fill the gap between Remote Sensing and AI. To this end, MDL4EO is working in that direc-
                                         tion bringing together different expertises: Data Science, Computer Vision, Machine Learning, Remote
                                         Sensing and Geoinformatics. In this tutorial we will give an overview on how such interdisciplinary
                                         dynamics can be successfully exploited in an applied research context.

                                         Keywords
                                         Remote Sensing, Earth Observation Data, Deep Learning, Land Cover Classification




SEBD 2021: The 29th Italian Symposium on Advanced Database Systems, September 5-9, 2021, Pizzo Calabro (VV),
Italy
" dino.ienco@inrae.fr (D. Ienco); roberto.interdonato@cirad.fr (R. Interdonato)
                                       © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
    CEUR
    Workshop
    Proceedings
                  http://ceur-ws.org
                  ISSN 1613-0073       CEUR Workshop Proceedings (CEUR-WS.org)

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