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TreeSatAI - Artificial Intelligence with Earth Observation and Multi-Source Geodata for Infrastructure, Nature and Forest Monitoring

| Press release | Farming & Agricultural Technology | Data Management & Analysis | Image Recognition & Understanding | Sensors & Networks | Smart Data & Knowledge Services | Kaiserslautern

The goal of the TreeSatAI project is the development of artificial intelligence methods for the monitoring of forests and tree populations at local, regional and global level. The project is funded by the German Federal Ministry of Education and Research (BMBF). Using freely accessible geodata from different sources (remote sensing data, administrative information, 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 fields of forest, nature conservation and infrastructure monitoring are developed.

© TU Berlin / FG Geoinformation in der Umweltplanung
A drone ready to take off for field tests

Remote sensing data from various satellite missions of ESA and NASA, aerial image data as well as geodata on the state of the environment are increasingly available free of charge and in large quantities. At the same time, texts, photos and videos from social media platforms such as Flickr, Twitter or Open Street Map provide access to further information about our environment. However, a manual evaluation of the resulting huge amounts of data would be too time-consuming and labor-intensive.

The Deep Learning Competence Center of DFKI and the research area Smart Data & Knowledge Services have been developing AI procedures for the analysis of aerial and satellite images for some time now, which enable both local evaluation and global analysis. In TreeSatAI, the scientists intend to use CNNs (Convolutional Neural Networks) as well as specialized LSTM models (Long Short-Term Memory) from the field of Deep Learning to enable the automated temporal analysis of forest areas over a large area and thus support environmental and forest experts. One of the major challenges is the acquisition of sufficient, high-quality training data to train the algorithms and the evaluation of the resulting models by experts from the forest and environmental sector. Therefore, the project will use and combine the different competences of the project partners to meet the numerous challenges of this ambitious project.


  • TU Berlin: Geoinformation in Environmental Planning (Consortium management)
  • TU Berlin: Remote Sensing Image Analysis Group
  • LiveEO GmbH
  • LUP GmbH
  • Vision Impulse GmbH

01.06.2020 - 31.05.2022

Funding reference:
BMBF 01IS20014D



© TU Berlin / Hartmut Kenneweg
Forested areas in the Harz Mountains damaged by drought stress and bark beetles