New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people’s movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system.

As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this “land gap” and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle.

In May 2007, the INSPIRE directive established the path towards creating the European Spatial Data Infrastructure (ESDI). While the Joint Research Centre (JRC) defined a set of detailed implementation guidelines, the European member states determined the agencies responsible for delivering the different topics specified in the directive’s annexes. INSPIRE’s goal was - and still is - to organize and share Europe’s data supporting environmental policies and actions. However, the way that INSPIRE was defined limited contributions to the public sector, and limited topics to those specifically listed in its annexes. Technical challenges and a lack of appropriate tools have impeded INSPIRE from implementing its own guidelines, and even after 15 years, the dream of a continuous, consistent description of Europe’s environment has still not completely materialized. We should apply the lessons learnt in INSPIRE when we build the Green Deal Data Space (GDDS). To create the GDDS, we should start with ESDI (the European Spatial Data Infrastructure), but also engage and align with the ongoing preparatory actions for data spaces (e.g., for green deal and agriculture) as well as include actors and networks that have emerged or been organized in the recent years. These include: networks of in situ observations (e.g. the Environmental Research Infrastructures (ENVRI) community); Citizen Science initiatives (such as the biodiversity observations integrated in the Global Biodiversity Information Facility (GBIF), or sensor communities for e.g. air quality); predictive algorithms and machine learning models and simulations based on artificial intelligence (such as the ones deployed in the European Open Science Cloud, International Data Space Association and Gaia-X; services driven both by the scientific community and the private sector); remote sensing derived products developed by the Copernicus Services. Most of these data providers have already embraced the FAIR principles and open data, providing many examples of best practice which can assist newer adopters on the path to open science. In the Horizon Europe project AD4GD (AllData4GreenDeal), we believe that, instead of trying to force data producers to adopt cumbersome new protocols, we should take advantage of the latest developments in geospatial standards and APIs. These allow loosely coupled but well documented and interlinked data sources and models in the GDDS while achieving scientifically robust integration and easy access to data in the resulting workflows. Another fundamental element will be the adoption of a common and extensible information model enabling the representation and exchange of Green Deal related data in an unambiguous manner, including vocabularies for Essential Variables to organize the observable measurements and increase the level of semantic interoperability. This will allow systems and components from different technology providers to seamless interoperate and exchange data, and to have an integrated view and access to exploit the full value of the available data. The project will validate the approach in three pilot cases: water quality and availability of Berlin lakes, biodiversity corridors in the metropolitan area of Barcelona and low cost air quality sensors in Europe. The AD4GD project is funded by the European Union under the Horizon Europe program.

Smart City logistics increasingly rely on secure, low-maintenance, and energy-efficient infrastructures for unattended parcel delivery and asset monitoring. Conventional smart mailbox solutions often require frequent maintenance, highpower communication modules, or complex installation procedures, limiting their suitability for large-scale urban deployment. This paper presents a novel integrated mechanical–electronic–software architecture for battery-powered smart mailboxes designed for long-term autonomous operation and seamless scalability. The system incorporates an ultra-low-power STM32L432KC microcontroller, a Sigfox ON-Semi DVK-SFEU-1-GEVK communication module, and an anti-vandal PUKDB antenna, combined with redundant sensing elements—including dual-contact microswitches (NC/NO) and magnetic switches (NC/NO)—to detect door state changes and intrusion attempts. A buzzer provides local audible signaling, while all components are optimized for minimal power consumption. To extend battery life, the system employs a hybrid event-logging strategy with a single daily Sigfox transmission, reporting all events accumulated over 24 hours. This drastically reduces uplink activity compared with conventional real-time systems, enabling multi-year battery autonomy using only standard AA cells. A lightweight backend service processes incoming messages, manages device state, and integrates with existing operational tools. The proposed solution has been validated on real postal mailboxes provided by Correos, demonstrating robustness, ease of integration, and suitability for deployment in un-managed outdoor environments. Although large-scale performance metrics are still under evaluation, the architecture showcases strong potential for city-wide, cost-effective, and energy-efficient smart infrastructure deployments. The system contributes a practical and sustainable approach to smart mailbox design, aligning with the long-term operational requirements of next-generation Smart Cities.