Conference Agenda

The AQpictures project, conducted under the ISPRS Scientific Initiatives 2025, addresses the emerging field of image-based estimation of fine particulate matter (PM2.5) concentrations in urban areas. PM2.5 represents a major public health concern, yet existing ground-based monitoring networks offer limited spatial coverage and satellite-derived products struggle to capture surface-level variability. Recent studies have demonstrated that visual attributes in outdoor images, such as sky colour, haze, and visibility, can provide useful indicators of PM2.5 concentrations. Building upon this premise, AQpictures aims to develop an open, reproducible framework for benchmarking and validating image-based air quality estimation methods.

The project first conducts a comprehensive literature review to classify existing approaches into four methodological categories: physics-based, machine learning, deep learning, and hybrid models. Based on this synthesis, a benchmark experiment is implemented for the city of Milan, combining a ten-month dataset of webcam images with co-located PM2.5 ground measurements. The workflow involves image preprocessing, feature extraction, and model evaluation using standard statistical indicators (R², RMSE, MAE). Preliminary tests include physics-based visibility models, feature-based regressors, and convolutional deep learning architectures.

All codes, datasets, and documentation are consolidated in an open-access GitHub repository to ensure transparency, reproducibility, and adaptability of methods across different environmental contexts. Early results confirm the feasibility of PM2.5 estimation from RGB imagery, though further investigations on multi-city datasets are planned to evaluate model transferability and robustness under varying urban and climatic conditions.

This paper presents MAMAI (Maternal Assistance and Monitoring through Artificial Intelligence), a standards-based framework designed to enable interoperable postpartum well-being monitoring using multi-vendor wearable devices. The proposed system addresses a key limitation in digital maternal health: the fragmentation of wearable ecosystems and the lack of integration with clinical infrastructures. MAMAI introduces a federated, edge–cloud architecture that allows wearable data to be processed locally while transmitting only summarized to the cloud.

A core contribution of this work is the integration of two complementary interoperability standards: the OGC SensorThings API for structuring IoT-based sensor observations, and HL7 FHIR for representing well-being indicators in clinically compatible formats. Through this dual-standard approach, heterogeneous wearable data—such as sleep patterns, physical activity, and heart-rate variability—are harmonized into standardized, platform-independent representations.

The framework further introduces a composite well-being score derived from normalized physiological indicators, enabling continuous and interpretable assessment of maternal health. A prototype implementation demonstrates the feasibility of the architecture, supporting end-to-end data ingestion, transformation, interoperability mapping, and visualization. Experimental results show efficient system performance with low end-to-end latency.

Overall, MAMAI provides a scalable and interoperable solution for integrating consumer wearable data into healthcare ecosystems, offering a foundation for next-generation maternal digital health systems and continuous postpartum monitoring.

Achieving the United Nations Sustainable Development Goal (SDG) 11.7.1—“providing universal access to safe, inclusive, accessible, and green public spaces by 2030”—underscores the critical role of urban green space in advancing global sustainability.Although extensive research has examined urban greenery from a traditional planar perspective, green spaces inherently possess vertical structure. Currently, systematic quantitative assessments of urban vertical greenery, residents’ actual exposure to vertical green space, and the associated inequalities remain limited. To address these gaps, this study integrates global population data with vegetation height information to construct an exposure-based analytical framework.We quantify spatial patterns of vertical greenery, residents’ green exposure, and exposure inequality across global urban areas, and further examine the drivers of inequality. Our findings reveal pronounced spatial disparities in urban greenery worldwide. On average, cities in the Global North exhibit approximately three times greater vertical greenery and nearly four times higher green exposure than cities in the Global South. African urban areas possess only one-sixth of the average vertical greenery and one-seventh of the exposure level observed in North America, while displaying roughly twice the inequality in green exposure, indicating much more uneven access to green resources. We also find that cities with higher average vertical greenery tend to experience lower exposure inequality, suggesting that increasing overall greenery can help promote more equitable access. These results provide new theoretical insights and policy-relevant evidence for advancing sustainable and equitable urban green development, supporting global progress toward sustainable development goals.

Walkability assessments for elderly pedestrians are often based on static representations of the built environment, overlooking temporal variations that influence walking conditions throughout the day. This study develops a network-based dynamic walkability framework that integrates static infrastructural characteristics with time-dependent environmental factors to capture spatiotemporal variability in pedestrian suitability. The approach combines sidewalk and arcade-based pedestrian networks with dynamic variables, including traffic, air quality index (AQI), temperature, humidity, shade, and lighting, evaluated at two time periods (12:00 and 17:00) across weekdays and weekends in three urban contexts in Tainan, Taiwan: a hospital area, a university campus, and a residential neighborhood. Results indicate clear spatial differences, with hospital and campus areas showing higher baseline walkability than residential areas. Dynamic analysis reveals temporal variation, with improvements ranging from approximately 3–8% in institutional environments to over 10% in residential areas. Segment-level results further show that temporal factors can alter pedestrian suitability, particularly in areas with limited infrastructure. Route-based validation demonstrates that the model generates alternative paths that prioritize safety and environmental comfort over the shortest distance. Compared to Google Maps routes, the proposed approach achieves higher average walkability, with improvements ranging from approximately 5% to over 15%, particularly in residential areas. These findings highlight the limitations of static and shortest-path approaches and emphasize the importance of incorporating temporal dynamics. The proposed framework supports time-sensitive routing and age-friendly urban planning strategies.

Existing building digital twins treat indoor environments as static geometric containers, ignoring the dynamic coupling between ventilation structure states and indoor environmental quality. Furthermore, managing indoor and outdoor spaces as separate data silos prevents the continuous assessment of occupant exposure across building boundaries. This paper proposes an environment-aware, indoor-outdoor integrated digital twin framework coupling geometric entity states with physical environmental fields for healthy mobility assessment. The framework utilizes a three-layer architecture. First, the Geometric-Semantic Layer provides a seamless LOD4 model with topologically stitched spaces, modeling ventilation facilities as first-class entities with mutable state attributes (Full Closed, Half Open, Full Open). Second, the Physical Field Layer maps mobile sensing data (PM2.5, CO2) onto semantic entities using a semantic-constrained method, treating walls and closed windows as aggregation barriers. Finally, the Behavioral Response Layer combines entity-level pollution values with pedestrian counts to compute a cumulative Crowd Exposure Index (CEI). Implemented on a Cesium platform, the framework was validated through a week-long university building experiment. Results show indoor PM2.5 in a fully enclosed study room averaged 61.2 μg/m³—1.6 times the outdoor level and 4.1 times the WHO guideline. This resulted in a CEI 12 times higher than in outdoor transit areas. Semantic correlation confirms the "Full Closed" window state primarily drives pollutant accumulation. This validates the framework's core geometry-physics coupling, demonstrating its potential to guide intelligent ventilation interventions and healthy building management.

Rapid industrialization and unplanned urbanization have increased air pollution levels across Indian cities, posing serious environmental and health challenges. This research presents a geospatial assessment of air pollutant behaviour across Nashik city by integrating multi-source remote sensing datasets and real observation datasets from Sentinel-5P, NASA POWER, and CPCB ground observations within a GIS-based analytical framework. Using ward-level mapping and spatial overlays, the study examines the distribution of key pollutants—PM2.5, PM10, NO2, SO2, and CO—and their relationship with environmental and anthropogenic parameters, including land use, road networks, wind direction, temperature, and vegetation density. The results consistently reveal high concentrations of PM2.5, ranging from a minimum of 52.4 µg/m³ to a maximum of 73 µg/m³, and PM10, a minimum of 87.3 µg/m³ and a maximum of 121.5 µg/m³, particularly along high-traffic corridors and industrial zones, which exceed the WHO standards. Correlations with meteorological and vegetative factors further highlight the influence of urban form and climatic conditions on pollutant dispersion. This integrated approach demonstrates how multi-source remote sensing and GIS tools can be effectively employed to identify emission hotspots, support evidence-based policy formulation, and strengthen urban environmental management strategies for sustainable development.

Air pollution is a critical environmental challenge, with nitrogen dioxide (NO₂) from vehicles and industries posing serious health and atmospheric risks. Traditional monitoring is limited, making satellite-based methods essential for large-scale assessment. Korba, Chhattisgarh is an industrial hub of coal mining and thermal power plants is a major pollution contributor. This study investigates the spatiotemporal dynamics, statistical behavior, and long-term trends of NO₂ concentrations over the Korba region from 2019 to 2024, utilizing Sentinel-5P TROPOMI-derived NO₂ column density and the Normalized Difference Pollution Index (NDPI). Year-wise NDPI patterns revealed a consistent pollution hotspot in the central-southern region, with the annual mean NDPI gradually increasing from 0.175 in 2019 to 0.191 in 2023. The monthly NDPI peaked in December-2024 at 0.525, indicating severe winter pollution. Statistical analysis showed moderate variability and a near-symmetric NDPI distribution with occasional spikes near industrial zones. Trend analysis identified a marginal but steady increase in pollution. Autocorrelation analysis revealed strong short-term persistence (lag-1 = 0.594), while spectral analysis identified a dominant annual frequency (0.083 cycles/month) with a peak power of 0.107, confirming the presence of strong seasonal variation and short-term persistence in NO₂ concentration. These results underscore the cyclic yet escalating nature of NO₂ pollution, with notable winter intensification. The findings emphasize the need for targeted emission control strategies and policy-level interventions to manage regional air quality. Future work should integrate ground-based validation and explore meteorological influences to improve predictive accuracy and guide sustainable environmental management.