Use Computer Vision to detect toxic clouds emitted by large factories and report the authorities on the sightings

Automated detection of atmospheric NO2 and CH4 plumes over Greater Kampala metropolitan area using high-resolution satellite observations from TROPOMI and Deep learning methods

Classify bank transactions and estimate their carbon impact to help people live more sustainably

Use drone & satellite imagery to develop a machine learning model to detect oil spills on open seas, in ports and in inland waterways.

Monitor tree growth on drone and satellite imagery from reforestation projects in Africa. Create an ML model for automated tree detection and segmentation.

Detect excess water in European forests endangering the fragile balance of one of the largest woodland ecosystems via open satellite data.

Combine hydrological sensor data with satellite images to predict flood propagation with machine learning.

Harvest driving video data from urban areas to get insight into the state of the environment of our cities. Use this data to come up with solutions to improve the sustainability of a city.

Developing high-precision agricultural robots that will increase land yield while reducing resources, with use of digital twins and reinforcement learning.

Automating coral analysis - detecting and classifying coral reef to aid conservation and research of life under water.

Using European Space Agency Earth observation data to show humanity’s impact on planet Earth.

Extend the lifetime of wind turbine blades by identifying the damage based on ambient vibration data.