CALCHAS’ primary focus was on the introduction of state-of-the-art deep machine learning methods for the analysis of Earth Observation data, specifically:
Soil Moisture Retrieval from Satellite observations where the objective was the high spatial and temporal resolution estimation of surface soil moisture from remote sensing observation, utilizing available in-situ measurements. The proposed model achieves superior accuracy at finer spatial scales compared to the gold-standard NASA products.
Flood Detection from Satellite observations where we demonstrated accurate flood detection and delineation from diverse sets of satellite observations. The developed model can detect flooded areas at different spatial locations, periods, and specific types of available observations.
Forecasting of Essential Climate Variables using climate reanalysis data, focusing on soil moisture and surface temperature. The proposed approach outperformed state-of-the-art methods when evaluated on predicting monthly averages at 1km spatial resolution.
Modeling high-dimensional Signals, where we investigated how to simultaneously encode observations from multiple instruments across time at different spatial locations. The developed model achieved more than 7% increase in land cover classification accuracy compared to state-of-the-art models.
Analyzing large-scale observations by training deep learning models on large-scale multi-source Earth observations over multiple interconnected computing platforms. The developed model demonstrated increased efficiency in resource utilization such as CPU time and network load.
In terms of dissemination & exploitation activities, quantifiable outcomes of the work include:
*The publication of six articles in prestigious journals and the presentation of this work in major international conferences like IGARSS and ICASSP, as well as more specialized ones like the ESA-ECMWF workshop on Machine Learning for Earth System Observation and Prediction.
*The organization of the Earth Data Workshop, which featured speakers from the regional government, academics from different research institutes, and industry. Furthermore, of 3 Ph.D. students and two MSc were co-supervised within the project.
*The funding of three follow-up projectts, two from ESA and one from NASA, as well as the involvement in the NASA active research mission Cyclone Global Navigation Satellite System.
*Participation in dissemination actions including the Researchers Night and the Science is Wonderful event, researching more than 50 students from elementary schools. Furthermore, relevant videos were uploaded on social media platforms, such as YouTube, which have been seen by more than 100 users.
*The development of three open-access Analysis Ready Datasets that include the first Soil Moisture dataset, a large-scale multi-source flood detection dataset, and a climate variable forecasting dataset.
