The paper describes a new tool called JupyTEP integrated development environment (IDE), which is an online integrated development environment for earth observation data processing available in the cloud. This work is a result of the project entitled “JupyTEP IDE—Jupyter-based IDE as an interactive and collaborative environment for the development of notebook style EO algorithms on network of exploitation platforms infrastructure” carried out in cooperation with European Space Agency. The main goal of this project was to provide a universal earth observation data processing tool to the community. JupyTEP IDE is an extension of Jupyter software ecosystem with customization of existing components for the needs of earth observation scientists and other professional and non-professional users. The approach is based on configuration, customization, adaptation, and extension of Jupyter, Jupyter Hub, and Docker components on earth observation data cloud infrastructure in the most flexible way; integration with accessible libraries and earth observation data tools (sentinel application platform (SNAP), geospatial data abstraction library (GDAL), etc.); adaptation of existing web processing service (WPS)-oriented earth observation services. The user-oriented product is based on a web-related user interface in the form of extended and modified Jupyter user interface (frontend) with customized layout, earth observation data processing extension, and a set of predefined notebooks, widgets, and tools. The final IDE is addressed to the remote sensing experts and other users who intend to develop Jupyter notebooks with the reuse of embedded tools, common WPS interfaces, and existing notebooks. The paper describes the background of the system, its architecture, and possible use cases.

The presence of terrain obstacles, for instance in urban, mountain or forest environments, significantly affects the accuracy of global navigation satellite system (GNSS) measurements, due to signal reception blockage and the multipath effect. The multipath remains a domain source of ranging errors in satellite positioning. When considering a session of a few hours, this type of error cannot be eliminated by differential techniques because its influence is uncorrelated with time and space. In order to minimize the influence of multipath, various methods can be applied. We present a data preprocessing tool for GPS multipath detection and mitigation based on GNSS obstacle mapping. Because reception of non-line-of-sight signals is closely connected with the place of observation, the maps of obstacles derived from on-point hemispherical images were applied. The main assumption of the proposed method is to remove from observations those received from satellites, which according to the map were behind the obstacle. As a reference two data sets were used. The first one was a set of unfiltered observations. In the second data set used for comparison there were measurements for which the C / N ₀ value was greater than 40 dB Hz ⁻¹ . The research showed that if the data registered from obstructed signals is deleted, the positioning accuracy will be better, despite the decreasing visible satellites number and the dilution of precision coefficient devaluation.

Data acquisition and simultaneous generation of real-time digital terrain models (DTMs) is a demanding task, due to the vast amounts of observations collected by modern technologies and measurement instruments in a short time. Existing methods for generating DTMs with large datasets require significant time and high computing power. Furthermore, these methods often fail to consider fragmentary DTM generation to maintain model continuity by addressing overlaps. Additionally, storing the resulting datasets, generated 3D models, and backup copies consumes excessive memory on computer and server disks. In this study, a novel concept of generating DTMs based on real-time data acquisition using the principles of sequential estimation is proposed. Since DTM generation occurs simultaneously with data acquisition, the proposed algorithm also incorporates data reduction techniques to manage the large dataset. The reduction is achieved using the Optimum Dataset Method (OptD). The effect of the research is the characteristics file that stores information about the DTM. The results demonstrate that the proposed methodology enables the creation of 3D models described by mathematical functions in each sequence and allows for determining the height of any terrain point efficiently. Experimental validation was conducted using airborne LiDAR data. The results demonstrate that data reduction using OptD retains critical terrain features while reducing dataset size by up to 98%, significantly improving computational efficiency. The accuracy of the generated DTM was assessed using root mean square error (RMSE) metrics, with values ranging from 0.041 m to 0.121 m, depending on the reduction level. Additionally, statistical analysis of height differences (ΔZ) between the proposed method and conventional interpolation techniques confirmed the reliability of the new approach. Compared to existing DTM generation methods, the proposed approach offers real-time adaptability, improved accuracy representation per model fragment, and reduced computational overhead.

Climate change increasingly affects the sustainability and reliability of urban water and wastewater infrastructure. This study analyzes the relationship between climatic variables and the frequency of failures in water and sewage networks in northeastern Poland, using operational data from the Mrągowo system (2020–2023) and meteorological records from 1966 to 2023. Statistical analyses and trend assessments were employed to identify climate-related failure patterns and infrastructure vulnerabilities. Climatic parameters—including temperature extremes, precipitation, snow cover, and sunshine duration—were analyzed in relation to infrastructure reliability. The results indicate rising temperatures, reduced snowfall, and altered precipitation regimes. Although extreme cold corresponded with increased sewage network failures, no significant association was found for high temperatures. Precipitation and snow cover showed weak correlations, except during heavy rainfall events. The study highlights the need to integrate climate resilience into water infrastructure management through preventive maintenance, smart monitoring, and nature-based solutions. Findings contribute to sustainable urban development strategies by demonstrating how climate variability directly affects service reliability. By identifying climate-sensitive failure thresholds, the study supports sustainable infrastructure management by enabling risk-informed adaptation strategies that reduce service disruptions, resource losses, and environmental impacts. This case study offers methodological insights and empirical evidence that may support the assessment of climate-related vulnerability of water and wastewater infrastructure in similar urban contexts.

This paper describes JupyQgis – a new Python library for Jupyteo IDE enabling interoperability with the QGIS system. Jupyteo is an online integrated development environment for earth observation data processing and is available on a cloud platform. It is targeted at remote sensing experts, scientists and users who can develop the Jupyter notebook by reusing embedded open-source tools, WPS interfaces and existing notebooks. In recent years, there has been an increasing popularity of data science methods that have become the focus of many organizations. Many scientific disciplines are facing a significant transformation due to data-driven solutions. This is especially true of geodesy, environmental sciences, and Earth sciences, where large data sets, such as Earth observation satellite data (EO data) and GIS data are used. The previous experience in using Jupyteo, both among the users of this platform and its creators, indicates the need to supplement its functionality with GIS analytical tools. This study analyzed the most efficient way to combine the functionality of the QGIS system with the functionality of the Jupyteo platform in one tool. It was found that the most suitable solution is to create a custom library providing an API for collaboration between both environments. The resulting library makes the work much easier and simplifies the source code of the created Python scripts. The functionality of the developed solution was illustrated with a test use case.

This paper presents the concept, implementation, and evaluation of FastFoam—a web-based inspection system designed for the technical assessment of buildings. Developed through international collaboration, FastFoam supports flexible inspection workflows, structured data collection, and integration with classification systems and geospatial data. The system enables civil engineers to create, customize, and manage inspection templates, store inspection results in a centralized database, and analyze inspection data using both descriptive and extensible analytical tools.To assess user needs and guide system development, a nationwide survey was conducted among Polish civil engineering professionals. The results confirmed strong interest in mobile and web-based inspection tools, as well as specific functional expectations regarding template customization, defect documentation, and automated reporting. The system architecture follows a multi-layered design with separate user, server, and external service layers. It supports modular data structures, role-based access, and integration with external platforms such as OpenStreetMap and BIM systems. A key innovation of FastFoam is its implementation of the FOAM (Function-Oriented Assessment Methodology), which enables temporal analysis and prediction of building condition over various timeframes. A case study demonstrates the application of FastFoam in a real-world building inspection in Poland. The evaluation confirmed the system’s practical usability while also revealing opportunities for future enhancements including AI-based defect detection, IoT integration, offline mobile functionality, and open data export.