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TU Berlin

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BigEarth - Accurate and Scalable Processing of Big Data in Earth Observation

Lupe

BigEarth is a research project funded by the European Research Council (ERC) Starting Grant for the period 2018-2023 and Prof. Dr. Begüm Demir is the Principle Investigator.

For more information,  visit: http://bigearth.eu/

 

 

IDEAL-VGI - Information Discovery from Big Earth Observation Data Archives by Learning from Volunteered Geographic Information

Lupe

IDEAL-VGI is funded by the German Research Foundation for the period 2019-2022 under the Priority Programme “Volunteered Geographic Information (VGI): Interpretation, Visualisation and Social Computing” [SPP 1894].  The IDEAL-VGI project contributes to the following research domains indicated in the priority programme: 1) information retrieval and analysis of VGI (machine learning and algorithmic interpretation for VGI and quality assessment and uncertainty analysis of VGI); and 2) active participation, social context and privacy awareness (information management and decision analysis based on VGI data).

BIFOLD: Berlin Institute for the Foundations of Learning and Data

Lupe

BIFOLD aims to conduct research into the scientific foundations of Big Data and Machine Learning, to advance AI application development, and greatly increase the impact to society, the economy, and science. Prof. Dr. Begüm Demir is one of the Principle Investigators. For more information, visit: https://bifold.berlin

 

 

 

TreeSatAI-Künstliche Intelligenz mit Erdbeobachtungs- und Multi-Source Geodaten für das Infrastruktur-, Naturschutz- und Waldmonitoring

Lupe

 

TreeSatAI is funded by the Federal Ministry of Education and Research for the period 2020-2022. The overall goal of TreeSatAI is the prototypical development of AI methods for the monitoring of forests and tree inventories on local, regional and global scales. Based on freely accessible geodata from different sources (remote sensing, administration, social media, mobile apps, monitoring libraries, open image databases) prototypes for deep learning based extraction and classification of tree and stand features for four different use cases in the field of forest, nature conservation and infrastructure monitoring will be developed. Our project partners are: Geoinformation in Environmental Planning Group of TU Berlin, LiveEO, LUP, DFKI and Vision Impulse.

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