iMonitor: AI for Monitoring Changes and Food Supply from Space

Funded by Munich Aerospace

Project Leader
Prof. Dr. Xiaoxiang Zhu

Project Scientist
Stella Ofori-Ampofo

Cooperation Partners
TUM (Chair of Data Science in Earth Observations: Prof. Dr. Zhu), DLR (Remote Sensing Technology Institute, Earth Observation Center), IABG (Innovationszentrum and Geodaten Factory)

Runtime
2021 – 2024

With the launch of the Sentinel satellite missions, the European Copernicus program has made

freely available an unprecedented volume of Earth observation data. These data provide information of the covered area in different spectral bands and at different times. The former is used to identify vegetated areas via the characteristic reflectivity of plants, the latter allows researchers to observe changes over time – a valuable source of information in times of rapid climate change and its consequences for food security. However, due to the complex nature of Earth observation data, current research is still only beginning to understand the opportunities created by analyzing the huge volume of data.

This project is pushing forward the development of Artificial Intelligence methods that are inevitable in that regard. With iMonitor, we are focusing on algorithms that utilize the vast information behind multi-temporal data. The goal is to detect changes especially in agricultural areas at large spatial scales, unimpeded by cultural and geospatial differences of the land surface, only based on a few localized reference areas with well-known characteristics. Ultimately, we will be able to provide an important data source to quantify the fragility of the food supply dynamics and the impact of single-crop practices during increased pressure on soil, water availability, and drought.