Management of Forests and Urban Green Areas
The ForestVISION project aims to develop an innovative online platform for intelligent monitoring, analysis, and sustainable forest management using artificial intelligence (AI) and computer vision. The platform employs AI to classify flora (in this pilot, two selected species and their deviations from optimal conditions) within a forest ecosystem and to analyze various observable environmental factors. It provides timely alerts on aspects such as size, composition, health, and potential risks, including illegal deforestation. The project is primarily focused on the area of the Serra da Estrela Natural Park in Portugal.
The system leverages data captured by satellites and drones—including RGB and multispectral imagery—as well as ground-based sensors such as temperature and humidity sensors.
The platform aims to support sustainable forest management, including the comparison of different reforestation methods. ForestVISION helps prevent ecosystem risks, adapt to climate change, and efficiently manage ecosystem services in a more sustainable way, fostering social, environmental, and economic regeneration.
The project can also be adapted to urban green areas, where regular tree inventories and the identification of vital parameters and characteristics are essential—tasks that can be efficiently carried out using drones equipped with multispectral cameras, LiDAR, or other specialized instruments.
River Ecosystem Management
The RiparianEye – Biomass Project is an innovative Earth Observation initiative aimed at mitigating biodiversity loss in riverine environments by transforming the challenge of invasive plant species (IAS) into resources through a Circular Economy approach.
By leveraging artificial intelligence and aerial imagery, the project seeks to prototype an End-to-End digital platform to support decision-making processes. It conducts a quantitative analysis of the use of plant biomass derived from invasive aquatic species, promoting circular economy principles while focusing on the production of biohydrogen and high-value biomolecules for the pharmaceutical, nutraceutical, and cosmetic sectors.
The project is part of a broader corporate strategic initiative addressing various issues related to the river ecosystem, enabling the analysis of abnormal conditions in a river or its surrounding areas.
Invasive aquatic Plants detection
LudVision is a proof of concept project for Remote Detection of an Invasive Aquatic Floral Species using Drone-Captured Multispectral Data. The main targeted species was Ludwigia peploides, which is an invasive species that raises concern at the European level, due to its negative impact on the natural balance native ecosystems.
The project fits under the Remote Sensing umbrella, and relies on heavily modified state-of-the-art semantic segmentation models, to detect the presence of the invasive species in drone-captured multispectral images. The model is able to detect the species with high accuracy in a wide range of altitudes, and it is tolerant to variability in both atmospheric and light conditions.
This project was developed in partnership with UBI (Universidade da Beira Interior) and co-funded by NOVA LINCS (UIDB/04516/2020) with the financial support of FCT – Fundação para a Ciência e a Tecnologia, through portuguese national funds.
Biodiversity Artificial Intelligence Hub (BAIH)
In collaboration with the National Biodiversity Future Center (NBFC) and the Consiglio Nazionale delle Ricerche (CNR), we are developing a digital platform for biodiversity, designed to enable data sharing, analytical reporting, and citizen science tools. Leveraging AI models and Big Data processing, the platform supports research teams, universities, and professionals in biodiversity studies.
The portal serves as a central hub for AI-driven biodiversity research, integrating models, datasets, code, and publications. Designed to be modular, scalable, and interoperable, the platform provides templates, guides, and tutorials, streamlining workflows and accelerating innovation.
In parallel with the development of the BAIH Platform, a dedicated pilot project was carried out in collaboration with NBFC, focusing on the monitoring of Invasive Alien Species (IAS) in protected areas. Focused on monitoring Nitrofilo and Torbiera, the pilot is being deployed in the Parco delle Alpi Marittime, providing a real-world environment to test and validate data-driven approaches for ecological monitoring and management.
Precision Agriculture
ZIRAK participated in a European Precision Agriculture project called ATLAS, which focused on several aspects, including the interfacing of existing tractors with embedded hardware and the management of soil samples for laboratory analysis. ZIRAK also worked on prototypes for tractor geo-fencing, both on embedded hardware and mobile applications connected to the tractor, enabling simplified fleet management.
ZIRAK provides tailored services for analyzing large agricultural plots, vineyards, and orchards, where it can assess the specific needs of plants, issue alerts in case of significant variations in water, nutrients, or certain diseases detectable through multispectral cameras. The services can also include predictions of basic parameters, such as water stress, leveraging drones and/or satellites to accommodate different requirements.