In the heart of Albania, a groundbreaking approach to hazard monitoring is emerging, one that could redefine how we manage environmental risks and protect critical infrastructure, particularly in the energy sector. Medjon Hysenaj, a researcher from the University of Shkodër “Luigj Gurakuqi,” has pioneered a hybrid AI-GIS pipeline that promises to revolutionize the way we detect and manage floods, forest fires, and deforestation. This innovative system, detailed in a recent article published in *Geoinformatica Polonica* (which translates to *Polish Geoinformatics*), combines cutting-edge artificial intelligence with geospatial technologies to provide real-time, actionable insights.
Albania, like many regions, faces increasing threats from natural and human-induced hazards. Recurrent floods in the Shkodra basin, devastating forest fires in protected areas, and rampant deforestation linked to land-use changes pose significant challenges to both the environment and the energy sector. Traditional monitoring methods often fall short due to fragmented datasets, delayed reporting, and the lack of integration of advanced analytical tools. Hysenaj’s research addresses these gaps by proposing a seamless fusion of semantic image classification and quantitative geospatial analysis.
At the core of this system is the Contrastive Language–Image Pretraining (CLIP) model, which enables zero-shot classification of hazard-related imagery. This means that the system can automatically label images and tiles based on natural language prompts such as “flooded farmland,” “burned forest,” or “deforested hillside” without the need for retraining. “This approach not only enhances the interpretability of hazard assessments but also significantly reduces the time and resources required for data processing,” Hysenaj explains.
The system doesn’t stop at classification. It also employs pixel-level segmentation and spectral indices derived from remote sensing data to quantify the spatial extent of affected areas. For instance, the Normalized Difference Water Index (NDWI) is used to map flood extents, the Normalized Difference Vegetation Index (NDVI) to assess vegetation loss, and the differenced Normalized Burn Ratio (dNBR) to evaluate burn severity. These quantitative metrics provide a comprehensive picture of the hazards, enabling more informed decision-making.
The outputs are then structured in a PostGIS database, where hazard layers and attributes are stored and linked to spatial queries. A Web GIS environment built with Leaflet offers interactive visualization, dashboards, and temporal comparisons for end users. This user-friendly interface ensures that stakeholders, including energy companies, can access and utilize the data effectively.
The research presents three compelling scenarios: flood extent mapping in Shkodra, wildfire impact assessment in Lurë National Park, and deforestation monitoring in Tropoja. Each scenario demonstrates the system’s ability to enhance both the accuracy and interpretability of hazard assessments. “The integration of semantic classification and quantitative extraction has proven to be a game-changer,” Hysenaj notes. “It provides a scalable, reproducible, and policy-relevant framework for environmental risk monitoring.”
The implications for the energy sector are profound. Accurate and timely hazard monitoring can help energy companies mitigate risks, protect infrastructure, and ensure the continuity of operations. For example, knowing the extent of floodwaters can help in planning and maintaining critical energy facilities, while monitoring deforestation can aid in sustainable land-use planning. The system’s scalability and reproducibility make it a valuable tool for other regions facing similar challenges.
Hysenaj’s research, published in *Geoinformatica Polonica*, highlights the potential of combining AI and GIS technologies to create observatories for environmental risk monitoring. As the world grapples with increasing environmental threats, this innovative approach offers a beacon of hope, demonstrating how technology can be harnessed to safeguard our planet and its resources.
The future of hazard monitoring is here, and it’s driven by the fusion of artificial intelligence and geospatial technologies. As Hysenaj’s work continues to gain traction, it is poised to shape the next generation of environmental risk management, offering a blueprint for a safer, more resilient future.