Project Overview

The project's core objective is to streamline geospatial research activities by providing a comprehensive solution that handles the entire data workflow. This includes:

  1. Automatic collection and availability of analysis-ready geospatial data, such as Copernicus Sentinel satellite imagery, Lidar, meteorological, and hydrological data.
  2. Machine Learning models and mathematical algorithms execution in a scalable cloud processing environment.
  3. Simplified data processing and publishing, supporting both vector and raster formats and web services or GIS-supported file formats.

ForestRadar enhances this workflow by introducing smooth execution of ML models and fast delivery of results, ensuring no constraints related to the area of interest (AoI), date, or data type. The platform's efficiency in processing and delivering results, from data acquisition to the execution of ML models, sets a new standard for geospatial research projects.

Key Features


Data Lifecycle Management: ForestRadar offers a complete solution covering data acquisition, storage, processing, and publishing, crucial for efficient and cost-effective geospatial research.

Geographic Scalability: The platform's geographical flexibility is highlighted, capable of handling data over multiple regions or continents, making it ideal for large-scale research projects.

Efficiency Gains: Promises significant time and resource savings in geospatial data preparation and processing by providing an automated, scalable cloud processing environment.

Customizability: ForestRadar's modular design allows it to meet specific research needs, including custom algorithm development and data analysis.


More details in OCRE EO Catalogue

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