This document describes a use case that makes use of machine learning techniques along with various remote sensing data sources to detect changes in land cover for economical uses. The objective of this use case is to demonstrate the capacity of remote sensing in extracting adequate information that can be used to economical purposes such as decision making policies, damage evaluation on crops caused by natural hazards for insurance purposes, etc. This use case contains two sub-use cases that have strong economical impacts and are aligned with our core business at TerraNIS. The first sub-use case is dedicated to explore the change of land cover especially urban expansion and agricultural land. The second sub-use case is concerned with estimating the effect of natural hazards such as frost on vineyards. The two sub-use cases are built on the change detection techniques developed in the framework of CANDELA project.
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This project has received funding from the European Union's Horizon 2020
research and innovation programmeunder grant agreement No 776193