We have developed open-source code for fire authorities around the world, including mobile apps for citizens and web apps for admin personnel, which can be used to model the propagation of wildfires in real-time, assisting users to safely evacuate the area using our mobile apps.
We have examined whether animal manure constitutes an effective strategy to increase soil organic carbon stocks in the Mediterranean as a mitigation climate change action.
We have proposed an effective solution to the contamination of soils and water due to animal manure, by suggesting to transfer this manure from livestock farms to crop fields, to be used as fertilizer. We used Catalonia, Spain as a case study, employing a nature-inspired algorithm based on ants' foraging behaviour to solve the manure distribution problem.
We have developed a contact tracing app to fight the COVID-19 pandemic, which became the official app used by the Government of Cyprus.
We have considered various algorithms to investigate how swarms of ground robots and/or unmanned aerial vehicles (drones) could collaborate together for solving various tasks in agricultural fields efficiently.
We have predicted parking occupancy in short-term (i.e. next 60 minutes) and in real-time at the central parking station of Arnhem, the Netherlands. Our results have beaten the state-of-art predictions for parking occupancy.
We have proposed the use of synthetic data for training deep learning models, in cases where real-world datasets are inexistent or difficult to prepare/create. We have applied this concept in aerial photography for identifying disasters (i.e. fire, smoke, collapsed buildings) and to count houses and buildings.