L’Intelligenza Artificiale per la gestione sostenibile delle risorse idriche. Caso di studio: Gorgovivo, Ancona.

Artificial Intelligence for Sustainable Water Resources Management. Case Study: Gorgovivo, Ancona. – The urgent need to address challenges related to the sustainable management of water resources has prompted the scientific and technological community to explore innovative solutions that can make a significant contribution to achieving the goals and sustainability strategies of the UN’s 2030 Agenda. In this context, Artificial Intelligence (AI) emerges as a powerful ally, offering advanced tools for the collection, analysis, and management of water data efficiently and effectively. Using machine learning algorithms and neural networks, AI enables accurate prediction of water demand, facilitating optimal planning and allocation of resources. Moreover, the integration of smart sensors and monitoring systems allows real-time control of water resources, improving the response capacity to environmental variations and emergencies. The purpose of the research is to examine how AI can optimize water treatment and distribution processes, reducing waste and enhancing the energy efficiency of water facilities; the use of predictive models based on historical data and environmental variables allows for proactive management of resources, thus contributing to their conservation and long-term sustainability. In this perspective, an AI-based system will be adopted to predict the groundwater levels of the Gorgovivo spring (AN), using historical data from the spring itself, the level of the Esino river, and rainfall stations. The proposed AI model is based on the Prophet predictive algorithm, specifically designed to manage time series forecasting applications; as adapted by us, the predictive model was evaluated using the mean absolute error (MAE), mean squared error (MSE), and correlation criteria

A Feature Encoding Approach and a Cloud Computing Architecture to Map Fishing Activities

Monitoring fish stocks and fleets’ activities is key for Marine Spatial Planning. In recent years Vessel Monitoring System and Automatic Identification System have been developed for vessels longer than 12 and 15m in length, respectively, while small scale vessels (< 12m in length) remain untracked and largely unregulated, even though they account for 83% of all fishing activity in the Mediterranean Sea. In this paper we present an architecture that makes use of a low-cost LoRa/cellular network to acquire and process positioning data from small scale vessels, and a feature encoding approach that can be easily extended to process and map small scale fisheries. The feature encoding method uses a Markov chain to model transitions between successive behavioural states (e.g., fishing, steaming) of each vessel and classify its activity. The approach is evaluated using k-fold and Leave One Boat Out cross-validations and, in both cases, it results in significant improvements in the classification of fishing activities. The use of a such low-cost and open source technology coupled to artificial intelligence could open up potential for more integrated and transparent platforms to inform coastal resource and fisheries management, and cross-border marine spatial planning. It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to the optimal use of marine resources

A deep-learning framework running on edge devices for handgun and knife detection from indoor video-surveillance cameras

The early detection of handguns and knives from surveillance videos is crucial to enhance people’s safety. Despite the increasing development of Deep Learning (DL) methods for general object detection, weapon detection from surveillance videos still presents open challenges. Among these, the most significant are: (i) the very small size of the weapons with respect to the camera field of view and (ii) the need of a real-time feedback, even when using low-cost edge devices for computation. Complex and recently-developed DL architectures could mitigate the former challenge but do not satisfy the latter one. To tackle such limitation, the proposed work addresses the weapon-detection task from an edge perspective. A double-step DL approach was developed and evaluated against other state-of-the-art methods on a custom indoor surveillance dataset. The approach is based on a first Convolutional Neural Network (CNN) for people detection which guides a second CNN to identify handguns and knives. To evaluate the performance in a real-world indoor environment, the approach was deployed on a NVIDIA Jetson Nano edge device which was connected to an IP camera. The system achieved near real-time performance without relying on expensive hardware. The results in terms of both COCO Average Precision (AP = 79.30) and Frames per Second (FPS = 5.10) on the low-power NVIDIA Jetson Nano pointed out the goodness of the proposed approach compared with the others, encouraging the spread of automated video surveillance systems affordable to everyone

Empowered Optical Inspection by Using Robotic Manipulator in Industrial Applications

Nowadays the inspection of products at the end of line represents a critical phase. At this stage, it is necessary to look for defects in order to prevent the quality check to fail, and to provide information for improving the production as well. This task can be performed by using several sensing technologies, and the contactless optical inspection plays a key role. In this regard, the use of advanced robotic manipulators offers the capability to change the viewpoint of a given object and to inspect its multiple faces. We propose an approach that combines the use of photometric stereo to derive a 3D model of objects, empowered by the super-resolution that is applied on the original dataset (upstream) or on the normal images (downstream) in order to increase the quality of the final 3D model. The vision system is mounted on a robotic manipulator, able to grasp and change the viewpoint, thus offering a more complete view of the object to be inspected. The obtained results show that the developed solution increases the quality of the derived 3D models used for inspection tasks on different faces of the objects; this is achieved by using the manipulation ability offered by the adopted robotic platform

Using AIS to Attempt a Quantitative Evaluation of Unobserved Trawling Activity in the Mediterranean Sea

In the past decades, the Automatic Identification System (AIS) has been employed in numerous research fields as a valuable tool for, among other things, Maritime Domain Awareness and Maritime Spatial Planning. In contrast, its use in fisheries management is hampered by coverage and transmission gaps. Transmission gaps may be due to technical limitations (e.g., weak signal or interference with other signals) or to deliberate switching off of the system, to conceal fishing activities. In either case such gaps may result in underestimating fishing effort and pressure. This study was undertaken to map and analyze bottom trawler transmission gaps in terms of duration and distance from the harbor with a view to quantifying unobserved fishing and its effects on overall trawling pressure. Here we present the first map of bottom trawler AIS transmission gaps in the Mediterranean Sea and a revised estimate of fishing effort if some gaps are considered as actual fishing

CREATEFORUAS: Developing Innovative Technologies for Autonomous UAS

The research project named "CREATEFORUAS"aims at enhancing "trustable"flight autonomy for small Unmanned Aircraft Systems (UAS), by tackling different enabling technologies. The paper briefly describes the main research areas of the project which are relevant to vision-based environment understanding, sense and avoid, robust control, and multi-UAV cooperation. Then, it also provides a snapshot of current developments presenting simulation and experimental results

A cloud computing architecture to map trawling activities using positioning data

Descriptive and spatially-explicit information on fisheries plays a key role for an efficient integrated management of the maritime activities and the sustainable use of marine resources. However, this information is today still hard to obtain and, consequently, is a major issue for implementing Marine Spatial Planning (MSP). Since 2002, the Automatic Identification System (AIS) has been undergoing a major development allowing now for a real time geo-tracking and identification of equipped vessels of more than 15m in length overall (LOA) and, if properly processed, for the production of adequate information for MSP. Such monitoring systems or other low-cost and low-burden solutions are still missing for small vessels (LOA&lt; 12m), whose catches and fishing effort remain spatially unassessed and, hence, unregulated. In this context, we propose an architecture to process vessel tracking data, understand the behaviour of trawling fleets and map related fishing activities. It could be used to process not only AIS data but also positioning data from other low cost systems as IoT sensors that share their position over LoRa and 2G/3G/4G links. Analysis gives back important and verified data (overall accuracy of 92% for trawlers) and opens up development perspectives for monitoring small scale fisheries, helping hence to fill fishery data gaps and obtain a clearer picture of the fishing grounds as a whole

A Novel Remote Visual Inspection System for Bridge Predictive Maintenance

Predictive maintenance on infrastructures is currently a hot topic. Its importance is proportional to the damages resulting from the collapse of the infrastructure. Bridges, dams and tunnels are placed on top on the scale of severity of potential damages due to the fact that they can cause loss of lives. Traditional inspection methods are not objective, tied to the inspector’s experience and require human presence on site. To overpass the limits of the current technologies and methods, the authors of this paper developed a unique new concept: a remote visual inspection system to perform predictive maintenance on infrastructures such as bridges. This is based on the fusion between advanced robotic technologies and the Automated Visual Inspection that guarantees objective results, high-level of safety and low processing time of the results

A cloud computing architecture to map trawling activities using positioning data

none8noDescriptive and spatially-explicit information on fisheries plays a key role for an efficient integrated management of the maritime activities and the sustainable use of marine resources. However, this information is today still hard to obtain and, consequently, is a major issue for implementing Marine Spatial Planning (MSP). Since 2002, the Automatic Identification System (AIS) has been undergoing a major development allowing now for a real time geo-tracking and identification of equipped vessels of more than 15m in length overall (LOA) and, if properly processed, for the production of adequate information for MSP. Such monitoring systems or other low-cost and low-burden solutions are still missing for small vessels (LOA&lt; 12m), whose catches and fishing effort remain spatially unassessed and, hence, unregulated. In this context, we propose an architecture to process vessel tracking data, understand the behaviour of trawling fleets and map related fishing activities. It could be used to process not only AIS data but also positioning data from other low cost systems as IoT sensors that share their position over LoRa and 2G/3G/4G links. Analysis gives back important and verified data (overall accuracy of 92% for trawlers) and opens up development perspectives for monitoring small scale fisheries, helping hence to fill fishery data gaps and obtain a clearer picture of the fishing grounds as a whole.noneGaldelli A.; Mancini A.; Tassetti A.N.; Ferra Vega C.; Armelloni E.; Scarcella G.; Fabi G.; Zingaretti P.Galdelli A.; Mancini A.; Tassetti A.N.; Ferra Vega C.; Armelloni E.; Scarcella G.; Fabi G.; Zingaretti P