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Computational Intelligence for Multi-Source Remote Sensing Data Analytics

Project description

Deep learning in earth observation for better data

Earth observation (EO) is changing considerably because of the large amounts of observations obtained from remote sensing and in-situ sensor networks that acquire very precise localised measurements. Novel solutions are needed to obtain data from spaceborne and ground-based instruments for estimating geophysical parameters. To better understand multisource EO data, the EU-funded CALCHAS project will gather observations from different sources, combine sampling scales associated with spaceborne and in-situ measurements and analyse time series of dynamic observations. Mathematical tools will be used to extend the present capacity of single-source data analysis. The project will analyse time series of measurements from active and passive microwave and multispectral spaceborne imaging instruments, and in-situ sensor measurements.


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.


Net EU contribution
€ 215 492,16
70013 Irakleio

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Νησιά Αιγαίου Κρήτη Ηράκλειο
Activity type
Research Organisations
Total cost
€ 215 492,16

Partners (1)