Service robotics and machine learning for close-range remote sensing

L'abstract è presente nell'allegato / the abstract is in the attachmen

Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images

Aircraft ground de-icing operations play a critical role in flight safety. However, to handle the aircraft de-icing, a considerable quantity of de-icing fluids is commonly employed. Moreover, some pre-flight inspections are carried out with engines running; thus, a large amount of fuel is wasted, and CO2 is emitted. This implies substantial economic and environmental impacts. In this context, the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of Ice) aims to provide innovative tools to identify the ice on aircraft and improve the efficiency of the de-icing process. The project includes the design of a low-cost UAV (uncrewed aerial vehicle) platform and the development of a quasi-real-time ice detection methodology to ensure a faster and semi-automatic activity with a reduction of applied operating time and de-icing fluids. The purpose of this work, developed within the activities of the project, is defining and testing the most suitable sensor using a radiometric approach and machine learning algorithms. The adopted methodology consists of classifying ice through spectral imagery collected by two different sensors: multispectral and hyperspectral camera. Since the UAV prototype is under construction, the experimental analysis was performed with a simulation dataset acquired on the ground. The comparison among the two approaches, and their related algorithms (random forest and support vector machine) for image processing, was presented: practical results show that it is possible to identify the ice in both cases. Nonetheless, the hyperspectral camera guarantees a more reliable solution reaching a higher level of accuracy of classified iced surfaces

Precision Agriculture Workflow, from Data Collection to Data Management Using FOSS Tools: An Application in Northern Italy Vineyard

In the past decades, technology-based agriculture, also known as Precision Agriculture (PA) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as UAVs (Unmanned Aerial Vehicles) and multispectral optical sensors play a crucial role in the geomatics as support techniques. PA needs software to store and process spatial data and the Free and Open Software System (FOSS) community kept pace with PA’s needs: several FOSS software tools have been developed for data gathering, analysis, and restitution. The adoption of FOSS solutions, WebGIS platforms, open databases, and spatial data infrastructure to process and store spatial and nonspatial acquired data helps to share information among different actors with user-friendly solutions. Nevertheless, a comprehensive open-source platform that, besides processing UAV data, allows directly storing, visualising, sharing, and querying the final results and the related information does not exist. Indeed, today, the PA’s data elaboration and management with a FOSS approach still require several different software tools. Moreover, although some commercial solutions presented platforms to support management in PA activities, none of these present a complete workflow including data from acquisition phase to processed and stored information. In this scenario, the paper aims to provide UAV and PA users with a FOSS-replicable methodology that can fit farming activities’ operational and management needs. Therefore, this work focuses on developing a totally FOSS workflow to visualise, process, analyse, and manage PA data. In detail, a multidisciplinary approach is adopted for creating an operative web-sharing tool able to manage Very High Resolution (VHR) agricultural multispectral-derived information gathered by UAV systems. A vineyard in Northern Italy is used as an example to show the workflow of data generation and the data structure of the web tool. A UAV survey was carried out using a six-band multispectral camera and the data were elaborated through the Structure from Motion (SfM) technique, resulting in 3 cm resolution orthophoto. A supervised classifier identified the phenological stage of under-row weeds and the rows with a 95% overall accuracy. Then, a set of GIS-developed algorithms allowed Individual Tree Detection (ITD) and spectral indices for monitoring the plant-based phytosanitary conditions. A spatial data structure was implemented to gather the data at canopy scale. The last step of the workflow concerned publishing data in an interactive 3D webGIS, allowing users to update the spatial database. The webGIS can be operated from web browsers and desktop GIS. The final result is a shared open platform obtained with nonproprietary software that can store data of different sources and scales

Study and development of a GIS for fire-fighting activities based on INSPIRE directive

According to the Annual Fire Report 2013 (European Commission-Joint Research Centre, 2014), there have been 873 forest fires in Europe, in 2013, for a total of 340559 ha of territory. A comparison of this data to that of the previous years, highlights that, when the intended goal is that of preserving the environment and saving human lives, the importance of the correct management of forest fires can not be underestimated. In the past years, the European Union has invested in the development of the INSPIRE Directive (Infrastructure for Spatial Information in Europe) to support environmental policies. Furthermore, the EU is currently working on developing ad hoc infrastructures for the safe management of forests and fires. The AF3 EU project (Advanced Forest Fire Fighting), financed by the FP7, addresses the issue of developing innovative tools to handle all stages of forest fires. The project develops a single control center for the coordination of monitoring, manoeuvring, and post-fire operations. The SDI platform (Spatial Data Infrastructure) represents another component which was designed in the context of this project. It is based on a GIS (Geographic Information System) which is able to efficiently integrate multi-modal data. Following an analysis of the state of the art of information systems for forest fire-fighting, and in light of the end-user requirements analyzed within the AF3 project, we propose a geo-topographic database based on the INSPIRE Directive and developed on opensource platforms, which provides interoperability of the data and allows forecasting and monitoring of high-risk areas, decision making, damage estimation, and post-fire management

Spectral Evidence of Ice on Aircrafts – The SEI Project

The general objective of the SEI project (reference call: MANUNET III 2018) is to experiment spectral sensor fusion techniques of data acquired by UAVs (Unmanned Aerial Vehicles) during aircraft preflight inspection. In General Aviation, an important safety issue regards the presence of ice on the aircraft's fuselage and wings. Ice on the wings can cause loss of lift and stalling, which in most cases would result, during take-off phase, in airplane’s fatal crash. Another important aspect to consider is the high amount of de-icing liquid usually involved in the process incurring in high costs and considerable damage to the environment. This is not a typical industrial scenario, but the project addresses important aspects related to the manufacturing sector: it has to accomplish specific tasks following a design process; it involves human operators (labour) collaborating with machines and tools; it includes the usage of semi-raw materials (glycol fluids) to accomplish a service; it sees serialization of the process that could be invoked at any needed time. Moreover, it targets safety issues both for operators and machines regarding reliability of results at the end of the process, it is order-based and comparable to a discrete manufacturing process

Cerebellar mutism syndrome: from pathophysiology to rehabilitation

Cerebellar mutism syndrome (CMS) is a common complication following surgical resection of childhood tumors arising in the posterior fossa. Alteration of linguistic production, up to muteness and emotional lability, generally reported at least 24 h after the intervention, is the hallmark of post-operative CMS. Other associated traits include hypotonia and other cerebellar motor signs, cerebellar cognitive-affective syndrome, motor deficits from the involvement of the long pathways, and cranial neuropathies. Recovery usually takes 6 months, but most children are burdened with long-term residual deficits. The pathogenic mechanism is likely due to the damage occurring to the proximal efferent cerebellar pathway, including the dentate nucleus, the superior cerebellar peduncle, and its decussation in the mesencephalic tegmentum. Proven risk factors include brain stem invasion, diagnosis of medulloblastoma, midline localization, tumor size, invasion of the fourth ventricle, invasion of the superior cerebellar peduncle, left-handedness, and incision of the vermis. Currently, rehabilitation is the cornerstone of the treatment of patients with cerebellar mutism syndrome, and it must consider the three main impaired domains, namely speech, cognition/behavior, and movement

Studio e sviluppo di un GIS per la gestione della lotta dei grandi incendi secondo la direttiva INSPIRE

Secondo l’“Annual Fire Report 2013” (European Commission-Joint Research Centre, 2014), nel 2013 ci sono stati 873 incendi boschivi in Europa per un totale di 340.559 ha bruciati. Confrontando questi dati con quelli relativi agli anni precedenti si rileva che la gestione di tali incendi è una questione che non va sottovalutata se si desidera preservare il patrimonio naturale e salvaguardare vite umane. A tal proposito, negli ultimi anni, l’Europa sta investendo nello sviluppo della direttiva INSPIRE (Infrastucture For Spatial Information in Europe) per il supporto delle politiche comunitarie ambientali. Allo stesso tempo si è impegnata nella creazione di infrastrutture ad hoc per la gestione di foreste e incendi ancora in fase di costruzione. Il progetto europeo “AF3” (Advanced Forest Fire Fighting), finanziato con il FP7, si propone di fornire degli strumenti innovativi per la gestione e la lotta degli incendi boschivi in tutte le loro fasi. Partendo dallo studio dello stato dell’arte dei sistemi informativi utilizzati per il “forest firefighting” e dall’ analisi degli end-user requirement svolta all’interno del progetto AF3, si è arrivati ad una proposta di database geo-topografico (implementato su una piattaforma open source) che si riveli strumento per: previsione e monitoraggio aree a rischio, decision maker, stima del danno e gestione del post-incendio. In questo lavoro si propone il GIS studiato per una parte dell’architettura del sistema sviluppata. In particolare è stata considerata la direttiva europea INSPIRE per la strutturazione dei dati cartografici affinché venissero rispettati i principi di interoperabilità per la gestione e prevenzione degli incendi boschivi per mezzo di SDI (Spatial Data Infrastructure) a livello euopeo

Study and development of a GIS for fire-fighting activities based on INSPIRE directive

According to the Annual Fire Report 2013 (European Commission-Joint Research Centre, 2014), there have been 873 forest fires in Europe, in 2013, for a total of 340559 ha of territory. A comparison of this data to that of the previous years, highlights that, when the intended goal is that of preserving the environment and saving human lives, the importance of the correct management of forest fires can not be underestimated. In the past years, the European Union has invested in the development of the INSPIRE Directive (Infrastructure for Spatial Information in Europe) to support environmental policies. Furthermore, the EU is currently working on developing "ad hoc" infrastructures for the safe management of forests and fires. The AF3 EU project (Advanced Forest Fire Fighting), financed by the FP7, addresses the issue of developing innovative tools to handle all stages of forest fires. The project develops a single control center for the coordination of monitoring, manoeuvring, and post-fire operations. The SDI platform (Spatial Data Infrastructure) represents another component which was designed in the context of this project. It is based on a GIS (Geographic Information System) which is able to efficiently integrate multi-modal data. Following an analysis of the state of the art of information systems for forest fire-fighting, and in light of the end-user requirements analyzed within the AF3 project, we propose a geo-topographic database based on the INSPIRE Directive and developed on opensource platforms, which provides interoperability of the data and allows forecasting and monitoring of high-risk areas, decision making, damage estimation, and post-fire management