Conference Agenda

This study presents an integrated remote sensing and deep learning approach for large-scale detection of asbestos-cement roofing in urban environments. Asbestos remains a major environmental health concern across Europe, where asbestos-cement materials persist in the built environment despite regulatory bans. Accurate identification and quantification of these materials are critical for effective remediation planning and equitable health protection.

The research focused on Poland’s two largest metropolitan areas—Warsaw and Kraków—which differ markedly in morphology and historical development, providing contrasting case studies for model validation. High-resolution orthophotomaps (5 cm and 25 cm) from 2023–2024, combined with national building footprint datasets and field-verified information, were used to train and validate a convolutional neural network (CNN) for binary classification of asbestos and non-asbestos roofs.

The highest producer accuracy (90.4%) and overall accuracy (92.9%) were achieved using 128×128-pixel image windows, confirming that broader spatial context enhances classification precision in dense urban settings. The CNN model demonstrated consistent performance across both cities, highlighting its robustness and scalability.

By integrating open orthophotos with open-source analytical frameworks, the method supports the creation of spatially detailed asbestos inventories aligned with the EU INSPIRE Directive and the 2023 Asbestos Directive (EU 2023/2668). The approach enables cost-effective, standardized monitoring applicable to metropolitan and smaller urban contexts alike.

This study advances data-driven environmental health management by demonstrating that deep learning applied to national aerial imagery can deliver operational tools for mapping asbestos exposure risks and informing sustainable, equitable remediation strategies across Europe.

Urban greenspace is essential for ecological resilience, climate regulation, and human well-being, yet long-term, fine-scale assessments of its spatiotemporal dynamics and the extent to which residents benefit from green exposure remain limited. This study develops a 30-m resolution Fractional Vegetation Cover (FVC) dataset to monitor interannual and seasonal variations in urban greenspace across twelve representative Chinese cities from 2000 to 2020. To capture temporal exposure, we introduce the “greendays” metric, defined as the number of days per year that residents experience visible greenery. A population-weighted exposure model was applied to quantify both the magnitude and equality of greenspace exposure. Results show that greenspace increased across all cities over the two decades, with peri-urban areas exhibiting the most substantial gains due to ecological restoration and park development, while core urban areas experienced moderate but consistent improvements linked to renewal and localized greening efforts. Greendays displayed a slight upward trend, indicating an extended duration of annual greenery exposure for residents. Exposure equality remained high and improved in most cities, suggesting that greening initiatives increasingly benefited diverse population groups. Overall, this study provides a robust and scalable remote-sensing-based framework for tracking urban greenspace and exposure equity, offering critical evidence to support nature-based solutions, environmental justice, and sustainable urban planning in alignment with global development goals.

This study develops a generalized method of moments (GMM) framework to quantitatively assess the integrated relationships among climate, vegetation, and lake water volume. Using GSOD precipitation data, SSEBop evapotranspiration, Nino3.4 and MEI indices, and NDVI, we analyzed monthly variations of climatic and vegetation conditions in the Lake Victoria basin from 2000 to 2020. The associations between these factors and lake water-volume changes were first examined, and dynamic GMM was then applied to remove mutual influences among climate variables, allowing for a more reliable attribution of dominant drivers.Results show that precipitation is the primary driver of seasonal to interannual water-volume variations, while evapotranspiration imposes a consistent negative effect on lake storage. ENSO significantly modulates multi-year water anomalies. Vegetation dynamics respond to both climatic variability and lake water-volume changes, with water-level fluctuations providing additional positive feedback after controlling for climate effects.

Posidonia oceanica (Linnaeus) Delile (referred from now on also as P. oceanica) is a marine flowering plant endemic to the Mediterranean Sea, forming extensive underwater meadows that play vital ecological roles, especially as blue carbon reservoirs. Its distribution spans from Gibraltar to Turkey and North Africa to the Adriatic down to 40-50 m of depth (Cocozza et al., 2024). Human impacts, such as pollution, urbanization, and global warming, have reduced its extent by up to 56% in some regions (Robello et al., 2024). Monitoring these meadows is essential, and remote sensing data such as Neo-Pléiades satellite imagery enable their accurate mapping and health assessment. This study applies object-based classification to orthorectified Neo-Pléiades images to evaluate Posidonia oceanica distribution along Sardinia’s eastern coast.

Soil is a crucial component of the ecosystem, affected by climate change, and is often overlooked by remote sensing experts and insufficiently considered while discussing sustainable development projects. To enhance the use of soil related datasets based on earth observation during the planning phase of participatory processes, a specific analysis workflow was piloted during community consultations in Dodoma, Central Tanzania. In order to enhance the integration of the soil conditions during the design of a new community development plan Landsat 8 data from 2023 and 2024 was processed and prepared to make soil information more accessible to non-technical staff and the local communities in Chamwino district. Results confirm the suitability of the SBI as soil indicator thanks to its high resolution, easy interpretability, and context specificity. Preprocessing through experts was identified as viable solution for preparing the data. In addition, field truthing exercises and conversations with the local community members further confirm the accuracy of this dataset for highlighting areas affected by soil salinity or fertility loss and for the final use during participatory planning processes.