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Computational Intelligence for Multi-Source Remote Sensing Data Analytics


Earth Observation (EO) is undergoing a radical transformation due to the massive volume of observations acquired by remote sensing and in-situ sensor networks. While satellites provide coarse-resolution, yet global-scale monitoring of environmental processes, in-situ sensor networks acquire high-accuracy localized measurements. Extracting information from spaceborne and ground based instruments requires innovative solutions which will allow the autonomous integration of diverse in nature and scale observations in order to provide high-quality geophysical parameter estimation. CALCHAS will demonstrate cutting edge technologies targeting three major factors towards the vision of fully automated multi-source EO data understanding, namely (i) the fusion of observations from different sources and modalities, (ii) the efficient aggregation of the sampling scales associated with spaceborne and in-situ measurements, and (iii) the analysis of time-series of dynamic observations. To that end, the paradigm-shifting signal processing and learning framework of Deep Learning will be utilized and extended through powerful mathematical tools and appropriate methodologies like supervised and generative learning, dramatically extending the current scope of single source data analysis. The developed framework will be employed for analyzing time-series of measurements from active and passive microwave and multispectral spaceborne imaging instruments (SMAP, SMOS and Sentinels), and in-situ sensor measurements, targeting the high-accuracy spatial and temporal resolution enhancement for observations and soil moisture estimation. The merits of the developed technology will be demonstrated in two intelligent water management case studies, namely optimized irrigation management and water pipeline leakage detection.

Field of science

  • /natural sciences/computer and information sciences/data science/data analysis
  • /engineering and technology/environmental engineering/natural resource management/water management
  • /engineering and technology/electrical engineering, electronic engineering, information engineering/electronic engineering/signal processing
  • /engineering and technology/environmental engineering/remote sensing
  • /natural sciences/computer and information sciences/artificial intelligence/computational intelligence
  • /engineering and technology/electrical engineering, electronic engineering, information engineering/electronic engineering/sensors/smart sensors
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning/deep learning

Call for proposal

See other projects for this call

Funding Scheme

MSCA-IF-GF - Global Fellowships


N Plastira Str 100
70013 Irakleio
Activity type
Research Organisations
EU contribution
€ 215 492,16

Partners (1)

United States
3551 Trousdale Pkwy Adm 352
90089 5013 Los Angeles Ca
Activity type
Higher or Secondary Education Establishments