By utilizing space assets and cutting-edge AI technology, the Digital Port Platform functions as a centralized marine hub with the goals of improving port operations and minimizing environmental consequences. This technology provides real-time situational awareness to address significant operational and environmental concerns by transforming a variety of data sources into important indicators.
StepUp Horse is a wearable training aid that combines accuracy and convenience for horses. It is mounted beneath the saddle or harness and measures a number of parameters, including distance traveled, speed, heart rate, and frequency of breathing. Trainers and riders can gain insightful information from the device's real-time data processing, which is powered by embedded machine learning algorithms. Through the phone network, data is sent to StepUp's cloud, where it is monitored and alerted in real time via an iOS and Android app.
The Nerthus Project uses Artificial Intelligence (AI) and satellite imagery to combat growing the phenomenon of cyanobacteria blooms in water bodies. These blooms provide serious health and financial problems because of the possibility of water contamination, which is made worse by climate change and human activities close to water sources. The research uses skills in data analytics, remote sensing, and limnology to suggest a novel way to forecast these blooms before they happen.
Innovative Forecasting for Water Resource Management
The Urban Green View project conducted a feasibility study on the application of Earth Observation (EO) based services to enhance sustainable urban development and avert climate change-related hazards. With an emphasis on organizations involved in urban projection and community well-being, such as municipal administrations and environmental agencies, the research looked at a variety of EO-based services, such as detecting heat zones, locating cool air corridors and monitoring vegetation variations.
Improvement of phenological models by means of time series of EO (Earth Observation) data for numerical pollen forecast
Suitability of mixed-satellite-sensor derived 3D data for rapid urban population estimation in crisis situations
In this project, we wanted to know if, in the absence of dedicated stereo pairs, cross-orbit, mixed-time and/or mixed sensor stereo pairs could be used to produce digital elevation models. These models would then be used for population estimation and damage assessment. The project was a cooperation with Uni Geneva/GRID and DLR.
Extended EO-based services for dynamic information needs in humanitarian action
EO-based services to support humanitarian operations: monitoring population and natural resources in refugee/IDP (internally displaced person) camps
Comprehensive And Rapid Information Provision for Efficient Disaster and Emergency Management
