Eleni Kroupi, Maria Kesa, Victor Diego Navarro-Sánchez, Salman Saeed, Camille Pelloquin, Bahaa Alhaddad, Laura Moreno, Aureli Soria-Frisch, Giulio Ruffini
Currently, analyzing satellite images requires an unsustainable amount of manual labor. Semiautomatic solutions for land-cover classification of satellite images entail the incorporation of expert knowledge. To increase the scalability of the built solutions, methods that automate image processing and analysis pipelines are required. Recently, deep learning (DL) models have been applied to challenging vision problems with great success. We expect that the use of DL models will soon outperform shallow networks and other classification algorithms, as recently achieved in multiple domains. Here, we consider the task of land-cover classification of satellite images. This seems particularly appropriate for deep classifiers due to the combined high dimensionality of the data with the presence of compositional dependencies between pixels, which can be used to characterize a particular class. We develop a pipeline for analyzing satellite images using a deep convolutional neural network for practical applications. We present its successful application for land-cover classification, where it achieves 86% classification accuracy on unseen raw images.
Characterization of snow pack evolution is a key parameter for regions where water supply is mainly due to snow melt
and runoff.
Main goal of the project "AGORA" is to study the impact of assimilating earth observed data in a water prediction
numerical model. The site chosen for the study cover the Pyrenees area in Catalonia.
As first step, an observation of the water balance terms, such as snow cover, snow water equivalent, changes in soil
water content, have been done through extensive in situ campaigns in the area of study.
Collocated earth observed data from both passive, e.g. MERIS, MODIS, and active, e.g. ENVISAT-ASAR, ENVISATRA,
sensors have been collected during the surveys. These measurements will be used to develop and validate
algorithms for the characterization of the snow pack appropriately tuned for the area of interest.
The final phase of the project will evaluate the impact of assimilating remote sensing data into a hydrological model
specifically developed to cope with the significant weather changes in time and space characteristic of the area of study.
Preliminary results of the activity scheduled during the first year of the project will be highlighted. The importance of
developing application based on both remote sensed and in situ data will be discussed.
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