• We have developed an AI model for the scalable retrieval of similar landscapes based on optical satellite imagery using unsupervised representation learning, by breaking up the landscape similarity task into individual concepts closely related to remote sensing.
  • The FORBES magazine of the Phileleftheros local newspaper has created a reportage covering our projects related to environmental monitoring and modelling.
  • We have received funding and kick-started the InsectAI COST Action, supporting insect monitoring and conservation at the national and continental scale in order to understand and counteract widespread insect declines. The action received a 50 out of 50 evaluation score and our research group co-leads Working Group 3, which is about algorithms and techniques for insect monitoring.
  • We have created open-source code, 3D schematics and electronics' diagrams for supporting researchers to install and deploy smart traps for rodents, to monitor their behaviour and understand the effectiveness of different baits, as part of the MED4PEST EU project.
  • We have developed GAEA, which is a country-scale geospatial environmental modelling tool that aspires to become a digital twin for the real estate market of Cyprus. The tool has been presented at the EnviroInfo 2023 conference held in Munich, Germany.
  • We have organized bi-communal afforestation actions, bringing together tens of citizens from both sides of the island of Cyprus, planting more than 500 trees, as part of the CO-FOR-IT project.
  • We have harnessed GAEA to develop a data story which visualizes Natura 2000 areas in relation to land-use change (constructions) and road networks in Cyprus. Our story emphasized the undeniable strong human presence inside Natura 2000 sites in Cyprus. In addition, the concentration of new constructions within close proximity to Natura 2000 areas poses an additional threat to the resilience of these natural regions.
  • We have proposed a new algorithm for honey bees' population estimation based on consecutive image frames, using deep learning and pose estimation techniques. The algorithm was presented at the International Conference on Intelligent Systems (IntelliSys), held in Amsterdam, the Netherlands.
  • We have published open datasets to allow researchers to monitor and count honeybees in bee hives from consecutive frames, as part of the BE-HIVE project.
  • We have identified and listed the most important dissinfection by-products (DBPs) of chlorinated drinking water, examining and recording their eco-toxicity, regulatory aspects, popularity and occurrence statistics, plus the environmental conditions that favour their creation. This was part of the European H2OforAll project.
  • We have published our modeling approach on assisting people to escape wildfires in real-time based on mobile phones' location-based services.


  • The Insider magazine of the Phileleftheros local newspaper has published our research results on the CO-FOR-IT project.
  • We have developed a model-agnostic approach for generating Saliency Maps to explain inferred decisions of deep learning models, helping to understand how deep learning models take decisions in detection/classification problems when images are used as input.
  • We have mapped, detected and counted all trees around the island of Cyprus, using high resolution satellite imagery and deep learning. A total of 54,731,274 trees have been identified, with more than 90% accuracy.
  • We have developed smart beehives, installed in diferent rural and urban locations around Cyprus, having access to near real-time analytics about the populations of honeybees, their productivity and performance, as well as alerts about threats they might face, e.g. from wasps or varroa mite.
  • We have exploited Digital Surface Models for Inferring Super-Resolution for remotely sensed images, with better than state-of-the-art accuracy.
  • We have published the SuPerWorld Geo-API, which is an online API that consists of various geospatial and geo-analytics services which offer rich contextual information related to real estate properties, including environmental risks and risks related to climate change.
  • We have improved the operational efficiency of electric vehicle ridepooling fleets by predictive exploitation of idle times, exploiting these periods to harvest energy.
  • We have demonstrated a privacy-preserving presence tracing protocol and system for pandemics via machine-to-machine exposure notifications.
  • We have improved the annotation efficiency for animal activity recognition (AAR) using the Active Learning technique.
  • We have detected illegal dumping sites with high accuracy, by using high resolution aerial photography and the deep learning technique.


  • We have developed and released Mindgrate, a mobile app which allows legal migrants, refugees and asylum seekers to better integrate themselves to the Cyprus society, assisting them to locate a job. Mindgrate addresses numerous barriers which hindered migrants during their integration to the country in the past.
  • We have modelled, visualized and understood how land use changes in Cyprus, considering the area between Nicosia and Larnaka as our pilot site, tracking changes between February and March 2020.
  • We have visualized the domestic electricity consumption all around the country of Cyprus, performing big data analysis, revealing useful information and guidelines for policy-makers.
  • We have published open-source code for agtech, allowing farmers and developers to use various popular path planning algorithms to find the best solution for a swarm of agents (drones and/or ground robots) to cover an agricultural field for a range of operations (e.g. pruning, spraying, monitoring, crop collection, etc.).
  • We have created a computer vision model, based on Deep Siamese Neural Networks, for detecting land use change from satellite imagery, using a weakly supervised learning technique, for accelerating the training process without significant effort in data annotation/labelling.
  • We have identified, listed and visualized 63 important key performance indicators used by 193 countries around the world to measure climate change. To define goals for cities and countries in regards to mitigating climatic change, we first need to understand which the important KPIs are, how they can be measured and which values they take. Then, each country can calculate its performance based on these KPIs, setting realistic goals for better performance in the near future.
  • We have trained a computer vision model to take advantage of the shadow map of a remotely sensed monocular image to calculate its heightmap with high precision, creating digital surface models without the need of expensive equipment and large costs.
  • CovTracer-EN, which constitutes the official national mobile phone application for COVID-19 contact tracing in Cyprus based on the GAEN API, developed by our group in a joint effort with other MRG groups at CYENS, has been recently launched around the country, counting 25,000 users after one month from official release.
  • We have trained 200 migrants to serve as "ambassadors" of the CovTracer-EN tool at their communities, via the EduCovTracer project, funded by the Youth Board of Cyprus. Through the project, the mobile app was translated to languages spoken by migrants of Cyprus (Arabic, French, Russian, Pakistani).
  • The NewScientist magazine has reviewed and published our work on escaping wildfires in real-time by means of mobile apps.