MDL4EO: Machine and Deep Learning for Earth Observation Data
Research - Montpellier, Occitanie, France
MDL4EO: Machine and Deep Learning for Earth Observation Data Employees
MDL4EO: Machine and Deep Learning for Earth Observation Data Details
Modern Earth Observation systems provide huge amount of data from different sensors at different temporal, spatial and spectral resolutions. Such amount of information is commonly represented by means of multispectral imagery and, due to its complexity, it requires new techniques and method to be correctly exploited to extract valuable knowledge.Recently, data science and, in particular, machine (and deep) learning algorithms have demonstrated their ability to cope with image and signal analysis providing extraordinary results. Multiple data science challenges were already launched using satellite imagery (i.e. building footprints, road networks, iceberg detection, etc…) but crucial open questions remain unsolved (i.e. biodiversity monitoring, urban mapping, deforestation tracking and food risk prevention, triaging disaster zones, etc..). We are at the beginning of a new era for the analysis of Earth Observation data (EOD) where one of the main question is how to leverage the complementarity and the diversity of the different Earth Observation systems to answer important social challenges and monitor changes on the Earth Surface.The MDL4EO team (Machine and Deep Learning for Earth Observation) at the UMR TETIS (Montpellier, France) has the objective to scientifically contribute to this new era providing AI methods and algorithms to extract valuable knowledge from modern Earth Observation Data. The amount of data being collected by remote sensors is accelerating rapidly and we cannot manage them manually, this is why machine/deep learning lends itself well to remote sensing.
