Estimating land surface temperature (LST) in urban environments remains a complex task due to high surface heterogeneity and variations in material emissivity. This study compares LST products from four satellite sensors (Landsat 8 TIRS, ECOSTRESS, MODIS Terra, and Sentinel-3 SLSTR) with in-situ temperature measurements recorded by 27 ground-based sensors placed across diverse urban surface types in Kraków, Poland.To minimize cross-platform discrepancies, a harmonization workflow was applied, including spatial reprojection, temporal alignment within a ±15-minute window, and emissivity normalization. Residuals were assessed using standard validation metrics: RMSE, MAE, bias, and Pearson’s correlation coefficient. Among the examined datasets, Landsat 8 delivered the most consistent results (RMSE ≈ 7.7°C), while ECOSTRESS exhibited the highest positive bias and spatial variability. Residual errors were notably larger over vegetated areas, reflecting the effects of variable emissivity and canopy shading.Despite the observed errors, the harmonized dataset enables consistent comparisons between sensors and an effective thermal assessment of urban areas. The results highlight the methodological relevance of harmonization in thermal remote sensing, particularly in heterogeneous urban areas.

The amount of damage to cultural heritage sites is increasing rapidly every year. This is due to inadequate heritage management and uncontrolled urban growth as well as unpredictable seismic and atmospheric events that manifest themselves in a continuously deteriorating ecosystem. Thus, applications of artificial intelligence (AI) in remote-sensing (RS) techniques (machine-learning and deep-learning algorithms) for monitoring archaeological sites have increased in recent years. This research involves the surrounding area of the archaeological site of Chan Chan in Peru in particular. An approach that is based on the use of AI algorithms for building footprint segmentation and changedetection analysis by means of RS images is proposed. It involves a UNet convolutional network based on an EfficientNet B0 to B7 encoder. The network was trained on two public data sets from SpaceNet that were based on WV2 and WV3 satellite images: SpaceNet V1 (Rio), and SpaceNet V2 (Shanghai). In the pre-processing phase, the images from the two data sets have been equalized in order to improve their quality and avoid overfitting. The building segmentation has been performed on HRV images of the study area that were downloaded from Google Earth Pro. The value that was achieved in the IoU metric was around 70% in both experiments. The purpose of this proposed methodology is to assist scientists in drafting monitoring and conservation protocols based on already-recorded data in order to prevent future disasters and hazards.

Urban Heat Island (UHI) is a significant environmental phenomenon that exacerbates rising temperatures in urban areas, affecting human health, energy consumption, and urban sustainability. This study analyzes UHI intensity in four selected cities—Kraków and Gdańsk (Poland) and Ancona and Termoli (Italy)—using Landsat-8 and Landsat-9 thermal infrared imagery combined with ground-based in situ temperature measurements. The study employs object-based classification using a Random Forest (RF) algorithm, enabling a detailed assessment of land surface temperature (LST) variations across different land cover types.The results indicate that built-up areas and impervious surfaces exhibit significantly higher temperatures, with LST values reaching 36.5°C in Ancona and 32.2°C in Kraków, whereas vegetated areas show a cooling effect, reducing temperatures by up to 7°C. Coastal cities, particularly Gdańsk and Termoli, display lower UHI intensity but greater LST variability due to maritime cooling and cloud cover effects.Statistical validation comparing satellite-derived LST with in situ measurements demonstrated a strong correlation in Kraków (r = 0.73) and moderate agreement in Ancona (r = 0.62), while Gdańsk and Termoli exhibited lower correlation values (r = 0.09 and r = 0.003, respectively), attributed to cloud interference and coastal microclimatic effects. The study highlights the importance of integrating remote sensing with ground-based data to enhance UHI monitoring accuracy and proposes urban planning strategies, including increased vegetation cover and heat-resilient urban materials, to mitigate UHI effects.These findings contribute to the scientific understanding of UHI dynamics in different climatic contexts and provide actionable insights for sustainable urban development. Future research should explore high-resolution thermal datasets and advanced machine learning models for improved urban temperature monitoring.

The watermills, due to their technological innovation, represent a clear example of historical industrial heritage. A census campaign of water-powered grain mills was conducted in the Marche region (Italy). Almost 1,000 of them were counted, distributed throughout the regional territory in several water catchment areas. After a long multidisciplinary activity conducted among the Universities of Marche region, a geodatabase was implemented by GIS to map and register the historical watermills. The geodatabase is used to compose and manage an overall, uniform, homogeneous and expeditious cataloguing of the historical watermills distributed throughout the Marche region. In the second step, to get precise geospatial information, to obtain dimensional data, achieving metric and georeferenced 3D models, and to verify the real condition and state of preservation of them, 3D survey campaigns were conducted. In this phase, for some selected watermills the survey was carried out using geomatic sensors, such as photogrammetry and 3D LiDAR scanner. The final step of this research was to connect the 3D digital models from geomatics surveys in the GIS geodatabase, to enrich the collected historical documentation of the watermills, to improve their knowledge and valorization.

Augmented Reality (AR) allows the real world to be enhanced through the overlapping of digital information generated by an algorithm or software amplifying the user's perception. In this research, the potential of AR is exploited by applying it to the problem of air pollution visualization to raise awareness of the issue. It is well known that the principal sources of air pollution include industry, domestic heating, traffic, production processes and more others. According to European Environment Agency, air pollution is the largest environmental health risk in Europe in 2022 affecting respiratory and cardiovascular diseases and premature deaths. A suitable combination of GIS-based procedures for data analysis and georeferencing, AR systems for the visualization and exploitation of the results, can be a strategic driver. The research presented in this paper has been experimented in the Marche Region, in the central part of Italy, and in particular the city of Ancona. The pollutant data collected come from 17 Agenzia Regionale per la Protezione Ambientale delle Marche (ARPAM) meteorological ground-based stations located in Marche Region. The data processing have been executed using the software ArcGis Pro instead of a 3D City Model has been acquired from the online 3D ArcGIS platform. According to the Dlgs.155/2010 regulation, the AQI index (Air Quality Index) has been provided by the interpolation of the meteorological ground stations data. The visualization phase has been tackled exploiting in a WebGIS platform and an APP by AR. A 3D immersive visualization is made using five AQI level of air quality (excellent, good, fair, poor, bad) in order to increase the urban sensing.

Hydrogeological risks that are associated with rivers have emerged as a significant concern worldwide, impacting both natural ecosystems and human settlements. This contribution presents an interdisciplinary project that leverages many technologies for data-acquisition and modeling to comprehensively analyze and manage risks in riverine environments. The project integrates geomatics, geological, and hydrological techniques to provide a holistic understanding of river dynamics and their associated hazards. As a central component of this project, geomatics plays a pivotal role in instrumental field surveying through the deployment of photogrammetry and LiDAR instruments. Remote-sensing data from satellite imagery further enriches the project’s temporal analysis capabilities. By analyzing this data over time, researchers can monitor changes in river patterns, land use, and climate-related variables; this helps identify trends and potential triggers for hydrological events. To manage and integrate the vast amount of geospatial information that is generated, a geodatabase within a geographic information system (GIS) has been established. It enables efficient data storage, retrieval, and analysis, fostering collaboration among multidisciplinary researcher teams. This system offers tools for risk-assessment, modeling, and scenario planning; these allow for proactive measures for mitigating hydrological risks.