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UP4DREAM (UAV Photogrammetry for Developing Resilience and Educational Activities in Malawi) is a cooperative project cofounded by ISPRS between the Polytechnic University of Turin and the United Nations Children Fund (UNICEF) Malawi, with the support of two local Universities (Lilongwe University of Agriculture and Natural Resources, and Mzuzu University), and Agisoft LLC (for the use of their photogrammetry and computer vision software suite). Malawi is a flood-prone landlocked country constantly facing natural and health challenges, which prevent the country's sustainable socio-economic development. Frequent naturals shocks leave vulnerable communities food insecure. Moreover, Malawi suffers from high rates of HIV, as well as it has endemic malaria. The UP4DREAM project focuses on one of the drone project's critical priorities in Malawi (Imagery). It aims to start a capacity-building initiative in line with other mapping missions in developing countries, focusing on the realization and management of large-scale cartography (using GIS - Geographic Information Systems) and on the generation of 3D products based on the UAV-acquired data. The principal aim of UP4DREAM is to ensure that local institutions, universities, researchers, service companies, and manufacturers operating in the humanitarian drone corridor, established by UNICEF in 2017, will have the proper knowledge and understanding of the photogrammetry and spatial information best practices, to perform large-scale aerial data acquisition, processing, share and manage in the most efficient, cost-effective and scientifically rigorous way.

In this paper, we present a publicly available benchmark dataset on multisensorial indoor mapping and positioning (MiMAP), which is sponsored by ISPRS scientific initiatives. The benchmark dataset includes point clouds captured by an indoor mobile laser scanning system in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) LiDAR-based Simultaneous Localization and Mapping (SLAM); (2) automated Building Information Model (BIM) feature extraction; and (3) multisensory indoor positioning. The MiMAP project provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone-based indoor positioning methods. This paper describes the multisensory setup, data acquisition process, data description, challenges, and evaluation metrics included in the MiMAP project.

Geostructural rock mass surveys and the collection of data related to discontinues provide the basis for the characterization of rock masses and the study of their stability conditions. This paper describes a multiscale approach that was carried out using both non-contact techniques and traditional support techniques to survey certain geometrical features of discontinuities, such as their orientation, spacing, and useful persistence. This information is useful in identifying the possible kinematics and stability conditions. These techniques are extremely useful in the case study of the Elva valley road (Northern Italy), in which instability phenomena are spread across 9 km in an overhanging rocky mass. A multiscale approach was applied, obtaining digital surface models (DSMs) at three different scales: large-scale DSM of the entire road, a medium-scale DSM to assess portions of the slope, and a small-scale DSM to assess single discontinuities. The georeferenced point cloud and consequent DSMs of the slopes were obtained using an unmanned aerial vehicle (UAV) and terrestrial photogrammetric technique, allowing topographic and rapid traditional geostructural surveys. This technique allowed us to take measurements along the entire road, obtaining geometrical data for the discontinuities that are statistically representative of the rock mass and useful in defining the possible kinematic mechanisms and volumes of potentially detachable blocks. The main purpose of this study was to analyse how the geostructural features of a rock mass can affect the stability slope conditions at different scales in order to identify road sectors susceptible to different potential failure mechanisms using only kinematic analysis.

This paper presents the design of the benchmark dataset on multisensory indoor mapping and position (MIMAP) which is sponsored by ISPRS scientific initiatives. The benchmark dataset including point clouds captured by indoor mobile laser scanning system (IMLS) in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) SLAM-based indoor point cloud generation; (2) automated BIM feature extraction from point clouds, with an emphasis on the elements, such as floors, walls, ceilings, doors, windows, stairs, lamps, switches, air outlets, that are involved in building management and navigation tasks ; and (3) low-cost multisensory indoor positioning, focusing on the smartphone platform solution. MIMAP provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone indoor positioning methods.

Despite photogrammetry has been longly used for 3D model production, the recent development of structure from motion based reconstructions and the spread of unmanned aerial vehicles (UAV) are increasing its usage even further. Actually, the combined use of nadir and oblique UAV photogrammetry allows to cross the bridge between close range and aerial photogrammetry, enabling the quick acquisition of images for detailed 3D model production. This paper assesses the accuracy of UAV photogrammetry in the reconstruction of a cultural heritage building, namely the Girifalco fortress (Cortona, Italy), and it evaluates advantages and issues of this kind of 3D model generation. Furthermore, this work aims also at investigating the possible use of the generated model in order to extract geometric information about the building. First, elementary geometric shapes are extracted from the photogrammetric point cloud: the results of this step can be exploited for enabling the automatic generation of compact models of the building, which can be useful for instance in 3D city modeling applications. Then, a proper analysis of the geometric information included in the extracted shapes can be used in order to extract semantic information (e.g. to classify windows and doors) and to detect damaged areas on the building walls.

Riparian habitats provide a series of ecological services vital for the balance of the environment, and are niches and resources for a wide variety of species. Monitoring riparian environments at the intra-habitat level is crucial for assessing and preserving their conservation status, although it is challenging due to their landscape complexity. Unmanned aerial vehicles (UAV) and multi-spectral optical sensors can be used for very high resolution (VHR) monitoring in terms of spectral, spatial, and temporal resolutions. In this contribution, the vegetation species of the riparian habitat (91E0*, 3240 of Natura 2000 network) of North-West Italy were mapped at individual tree (ITD) level using machine learning and a multi-temporal phenology-based approach. Three UAV flights were conducted at the phenological-relevant time of the year (epochs). The data were analyzed using a structure from motion (SfM) approach. The resulting orthomosaics were segmented and classified using a random forest (RF) algorithm. The training dataset was composed of field-collected data, and was oversampled to reduce the effects of unbalancing and size. Three-hundred features were computed considering spectral, textural, and geometric information. Finally, the RF model was cross-validated (leave-one-out). This model was applied to eight scenarios that differed in temporal resolution to assess the role of multi-temporality over the UAV’s VHR optical data. Results showed better performances in multi-epoch phenology-based classification than single-epochs ones, with 0.71 overall accuracy compared to 0.61. Some classes, such as Pinus sylvestris and Betula pendula, are remarkably influenced by the phenology-based multi-temporality: the F1-score increased by 0.3 points by considering three epochs instead of two.

The Main10ance project aims to implement a plan of maintenance and conservation of the historical cultural heritage. This is an INTERREG project. The V-A Cooperation Programme Italy-Switzerland 2014-2020 contributes to the objectives of the Europe 2020 Strategy and the New Swiss Regional Policy (NRP)addressing the needs common to both sides of the border and aiming to generate significant change in the area of cooperation, both in terms of increasing competitiveness and strengthening economic and social cohesion. The case study is the system of the Sacri Monti of northern Italy and Switzerland, groups of chapels and other architectural artifacts. The design phases are divided into: survey of the historical architectural heritage present; data processing and realization of three-dimensional models with the help of BIM software; integration of the same in the geographical context through GIS support; creation of a database which creates interoperability between the various domains and which collects information on the characteristics of the goods for maintenance and conservation purposes; possibility to make the information associated with 3D models accessible through demonstrators that allow interrogation of the DB and the models themselves. The geometric representation respects the subdivision of the levels of detail (LOD) for GIS with the standard CityGML and the levels of development (LOD) for BIM with the UNI 11337/4.

Heavy rain between the 2nd and 3rd of October 2020 severely affected the area of Limone Piemonte, Piemonte Region (Italy). The consequence of those two days of rain was a flood that, starting from the hamlet of Limonetto severely damaged the areas close to the riverbed of the Vermegnana river and the related hydrographyc network. A synergistic multi-sensor and multi-scale approach for documenting the affected areas using VHR satellite images and UAVs (Uncrewed Aerial Vehicles) is presented. The pro and cons in terms of level of detail and processing strategies are reviewed with a focus on the workflows adopted for processing large UAV datasets. A thorough analysis of the 3D positional accuracy achievable with different georeferentation strategies for UAVs data processing is carried out, confirming that if an RTK (Reale Time Kinematic)-enabled GNSS (Global Navigation Satellite System) receiver is available on the UAV platform and proper acquisition guidelines are followed, the use of GCPs (Ground Control Points) is not impacting significantly on the overall positional accuracy. Satellite data processing is also presented, confirming the suitability for large scale mapping.

In the last decades, the development of geomatics and geomechanics techniques integration in the environmental field permits to obtain more detailed and accurate results, reducing the survey costs. The aim of the present work was aimed to apply these innovative combined methods and techniques in order to gain a detailed analysis of landslide hazard and on the stability condition of rocky slopes, to get useful information for subsequent design and feasibility planning of Vallone d’Elva road. During two different surveys period, geostructural surveys were carried out in situ (i.e. spatial orientation of discontinuity planes, their spacing and persistence), associated with geomatics surveys using drones (UAV technique – Unmanned Aerial Vehicle) and terrestrial photogrammetric technique to get high-resolution images of the rockwalls along the road in areas with complex orography and inaccessible. Their data processing has allowed the generation of different kind of data at different scales, like some 3D dense point clouds with a huge definition, which have been used to generate three-dimensional surfaces models. This procedure has allowed obtaining DSM (Digital Surface Model), DTMs (Digital Terrain Models) and orthophotos with centimetre resolution (mean 4 cm). Moreover, to identify the geomechanical rockmasses features, have been computed a new photogrammetric product on 16 specific rockwall sites along the road: “vertical orthophotos”, with details of few mm. Besides, over then twenty detailed DTMs of rockwalls along the entire road have been generated to measure plane orientation, spacing and other geometrical characteristics of outcropping rock masses, which have been statistically collected and analysed.

In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.

In this paper, the authors describe a Python customized code based on Agisoft MetaShape processing engine that permits the automatic solution of a complete photogrammetric process from acquisition of the image block by Unmanned Aerial Vehicle (UAV) to final results: Dense Digital Surface Model (DDSM), Digital Terrain Model (DTM) and orthophoto.

The need to share information about architectural heritage effectively after a disaster event, in order to foster its preservation, requires the use of a common language between the involved actors and stakeholders. A database able to connect the architectural heritage representation with the data useful for hazard and risk analysis can thus be a powerful instrument. This paper outlines a methodology to represent 3D models of the architectural heritage, according to some existing standards data models, and relate their geometric features to the damage mechanisms that could occur after an earthquake. Among all the existing standard to represent cartographic, cultural heritage and hazard/risk information, respectively INSPIRE, CityGML, UNESCO, CIDOC-CRM, its extension MONDIS and the Getty Institute vocabularies, compliant to the CIDOC-CRM, have been taken into account. An INSPIRE extension has been proposed for increasing the level of detail (LoD) of the representation and improving the description of heritage buildings, adding some macro-elements and elements “feature types” connected with the damage mechanisms, identified in structural studies. The suggested method allows to archive, in a multi-scale database, 3D information with a very high level of detail about architectural heritage and can help structural engineers and conservator-restorers in preventing further damages through individuating useful targeted actions.

Front-end amplifiers with high input impedance are exploited to build active array elements for modern low-frequency radio telescopes. The combination of such loading and mutual coupling between adjacent elements is responsible for the deep nulls that are present in some embedded element patterns (EEPs) of the Low-Frequency Array. This paper quantifies this phenomenon with both models and experimental data. The full-wave array simulations and the measured amplifier scattering parameters are combined to obtain the EEP models. These models are in good agreement with the in situ measurements that have been performed with a micro unmanned aerial vehicle carrying a field source.

The set of laws, actions and organizations for Cultural Heritage (CH) protection is born in the different countries of the European Union from local cultural situations, so the ability to cope with the emergency is certainly different. In addition to the damages that can occur to cultural assets after a disaster, an inadequate emergency intervention can sometimes cause further losses to the CH. The effectiveness of response depends on the adequacy of advanced planning. Some countries have designed emergency plans but their databases (DBs) are fragmented, incomplete and not standardized. It is thus necessary to establish a DB for emergency assistance and maps of CH at risk to be compared with maps of natural hazards and risks, in order to take preventive and operational measures, as well as agree on a common terminology and international standards. The project aims to enhance the capability of Civil Protection to prevent disasters impacts on CH by implementing a European Interoperable Database (EID) as supporting decision tool to understand the risk of damage to cultural assets. The EID, starting from the international standards to represent the map objects (CityGML, INSPIRE), the classification of CH in Europe (UNESCO), in Italy (MiBACT), in Germany and in France and from risks and disasters analysis, will design, with its Conceptual Data Model, an extension of the INSPIRE UML model. This DB will also support 3D models to help finding and recognizing dispersed artworks and facilitate a post-emergency restoration, preserving thus a digital memory in case of destruction.

Every year, there are almost 50,000 forest fires in Europe (127/day), which have burned an area equal to more than 450,000 ha. An effective management of forest fires is therefore fundamental in order to reduce the number of the fires and, especially, the related burned areas, preserving the environment and saving human lives. However, some problems still exist in the structure of information and in the harmonization of data and fire management procedures among different European countries. Pursuing the same interoperability aims, 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 (European Union) is currently working on developing ad hoc infrastructures for the safe management of forests and fires. Moving from this premises and following an analysis of the state of the art of information systems for forest fire-fighting, in the light of the end-user requirements, the paper presents the INSPIRE—compliant design of a geographical information system, implemented using open-source platforms.

Forests are significant resources from an ecological, economic and social point of view. Their protection and management could greatly benefit from a complete knowledge of the shape and distribution of trees in forest stands. To this purpose, aerial surveys, especially through Airborne Laser Scanning (ALS), were carried out in the last years to acquire point clouds to be used in 3D models aimed at achieving an accurate description of tree crowns and terrain. However, airborne data acquisition is expensive and may provide poor results in case of dense foliage. Further, point cloud resolution is not very high, as models with a grid of 2-3 m are usually obtained. In order to implement more accurate 3D forest models, a feasible solution is the integration of point clouds obtained by aerial acquisition (ALS or photogrammetry) for the treetops and the terrain description, with information from terrestrial surveys. In this paper, we investigated the possible integration of point clouds obtained by Terrestrial Laser Scanner (TLS) with those collected by photogrammetric 3D models based on images captured by Unmanned Aerial Vehicle (UAV) in a test site located in northern Italy, with the aim of creating an accurate dataset of the forest site with high resolution and precision. The limits of ALS and TLS were bridged by aerial photogrammetry at low altitude (and vice versa). A 3D model of the study area was obtained with a resolution of 5 cm and a precision of 3 cm. Such model may be used in a wide range of applications in forestry studies, e.g., the reconstruction of 3D shapes of trees or the analysis of tree growth throught time. The implications of the use of such integrate approach as a support tool for decision-making in forest management are discussed.

UAVs platform are increasingly deployed by first responders and local stakeholders to get a first overview of disaster affected areas with a high level of detail and different off-nadir angle configurations. Through a rapid mapping approach, the acquired data (video sequences or pictures) are analysed to extract information on damages to buildings and infrastructures with the goal to support the Search and Rescue operations. The specific focus of the paper is on evaluating the expected benefits (from the rapid mapping perspective) deriving from a direct georeferencing approach when using UAV with RTK capabilities. Specifically, data acquired by a fixed wing eBee RTK platform by SenseFly over the areas affected by the earthquake that hit central Italy in 2016 have been processed to compare the positional accuracies of orthoimagery generated by means of a direct georeferencing approach (without any GPC) with and without a post-processing kinematic solution. The results highlight that an RTK-enabled platform allows to achieve orthoimagery positioning accuracy values up to few centimeters without the need of any control point. In the conclusion session the operational implications of a PPK-based approach versus a standard direct georeferencing are critically discussed.

This work describes the different attempts and the consequent results derived from the integration of an HBIM model into an already structured spatial database (DB) and its 3D visualisation in a GIS project.This study is connected to the European ResCult (Increasing Resilience of Cultural Heritage) project where a DB for multiscale analyses was defined. To test the methodology proposed, the case study of Santa Maria dei Miracoli church in Venice was chosen since it represents a complex architectural heritage piece in a risk zone, it has been subject to a vast restoration intervention in the recent past but a digital documentation and model concerning it was missing.The 3D model of the church was structured in Revit as a HBIM, with the association of different kind of information and data related to the architectural elements by means of ‘shared parameters’ and ‘system families’. This procedure allows to reach an even higher Level of Detail (LOD4), but lead to some issues related to the semantic and software interoperability. To solve these problems the existing DB for the resilience of cultural heritage was extended adding a new entity representing the architectural elements designed in the BIM project.The aim of the test is to understand how the data and attributes inserted in the HBIM are converted and handled when dealing with a GIS DB, stepping from the IFC to the CityGML standard, through the FME software.

Thanks to the recent worldwide spread of drones and to the development of structure from motion photogrammetric software, UAV photogrammetry is becoming a convenient and reliable way for the 3D documentation of built heritage. Hence, nowadays, UAV photogrammetric surveying is a common and quite standard tool for producing 3D models of relatively large areas. However, when such areas are large, then a significant part of the generated point cloud is often of minor interest. Given the necessity of efficiently dealing with storing, processing and analyzing the produced point cloud, some optimization step should be considered in order to reduce the amount of redundancy, in particular in the parts of the model that are of minor interest. Despite this can be done by means of a manual selection of such parts, an automatic selection is clearly much more viable way to speed up the final model generation. Motivated by the recent development of many semantic classification techniques, the aim of this work is investigating the use of point cloud optimization based on semantic recognition of different components in the photogrammetric 3D model. The Girifalco Fortress (Cortona, Italy) is used as case study for such investigation. The rationale of the proposed methodology is clearly that of preserving high point density in the model in the areas that describe the fortress, whereas point cloud density is dramatically reduced in vegetated and soil areas. Thanks to the implemented automatic procedure, in the considered case study, the size of the point cloud has been reduced by a factor five, approximately. It is worth to notice that such result has been obtained preserving the original point density on the fortress surfaces, hence ensuring the same capabilities of geometric analysis of the original photogrammetric model.

The Aperture Array Verification System stimulated extensive test activities towards the development of the low-frequency instrument (50-350 MHz) of the Square Kilometer Array. This paper discusses radiation patterns measurements carried out on a 16-element array prototype with full digital beam-forming. A micro Unmanned Aerial Vehicle was adopted as the automatic positioner for the test source.

Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great variety in their nature, size and complexity; from small artefacts and museum items to cultural landscapes, from historical building and ancient monuments to city centers and archaeological sites. Cultural Heritage around the globe suffers from wars, natural disasters and human negligence. The importance of digital documentation is well recognized and there is an increasing pressure to document our heritage both nationally and internationally. For this reason, the three-dimensional scanning and modeling of sites and artifacts of cultural heritage have remarkably increased in recent years. The semantic segmentation of point clouds is an essential step of the entire pipeline; in fact, it allows to decompose complex architectures in single elements, which are then enriched with meaningful information within Building Information Modelling software. Notwithstanding, this step is very time consuming and completely entrusted on the manual work of domain experts, far from being automatized. This work describes a method to label and cluster automatically a point cloud based on a supervised Deep Learning approach, using a state-of-the-art Neural Network called PointNet++. Despite other methods are known, we have choose PointNet++ as it reached significant results for classifying and segmenting 3D point clouds. PointNet++ has been tested and improved, by training the network with annotated point clouds coming from a real survey and to evaluate how performance changes according to the input training data. It can result of great interest for the research community dealing with the point cloud semantic segmentation, since it makes public a labelled dataset of CH elements for further tests.

In recent years, many studies revealed the advantages of using airborne oblique images for obtaining improved 3D city models (e.g. including façades and building footprints). Expensive airborne cameras, installed on traditional aerial platforms, usually acquired the data. The purpose of this paper is to evaluate the possibility of acquire and use oblique images for the 3D reconstruction of a historical building, obtained by UAV (Unmanned Aerial Vehicle) and traditional COTS (Commercial Off-the-Shelf) digital cameras (more compact and lighter than generally used devices), for the realization of high-level-of-detail architectural survey. The critical issues of the acquisitions from a common UAV (flight planning strategies, ground control points, check points distribution and measurement, etc.) are described. Another important considered aspect was the evaluation of the possibility to use such systems as low cost methods for obtaining complete information from an aerial point of view in case of emergency problems or, as in the present paper, in the cultural heritage application field. The data processing was realized using SfM-based approach for point cloud generation: different dense image-matching algorithms implemented in some commercial and open source software were tested. The achieved results are analysed and the discrepancies from some reference LiDAR data are computed for a final evaluation. The system was tested on the S. Maria Chapel, a part of the Novalesa Abbey (Italy).

In recent years the term “industry 4.0” has been consolidated, indicating a trend towards the automation of industrial processes with the introduction of new technologies to support the production of goods and services. Connected to this new type of industry, in this paper, the authors describe the implementation of a solution based on the automatic analysis of data extracted from a photogrammetric survey. In particular, a low-cost UAV system has been used and, in this paper, all the analyses involved in the planning of the data acquisition phase, their management and processing within a GIS system are described. During the acquisition phase, different data sets were acquired using different techniques, recording 4 k video and acquiring high resolution images. The place chosen to perform this test was the Serru dam, a plant located in Ceresole Reale, near Turin, Piedmont (Italy). A 3D model was made of this structure on which a wall integrity check was carried out.

Nowadays, there are several strong motivation to replace some dangerous human activities with the use of small unmanned system, both terrestrial and aerial. An interesting example is the use of ground unmanned rover in emergency, to realize an early mapping or to distribute the help. In this context, the Authors have investigated and developed an innovative method for terrestrial rover navigation considering low-cost sensors. The algorithms, implemented in Matlab, start to the acquisition up to the processing phase. The results have shown an impressive performance in terms of positioning accuracies: the maximum difference between the developed solution with respect to the reference coordinates is 60 cm, with a mean value of 38 cm. These results will have a great impact especially for autonomous navigation solutions.

During archaeological excavations it is important to monitor the new excavated areas and findings day by day in order to be able to plan future excavation activities. At present, this daily activity is usually performed by using total stations, which survey the changes of the archaeological site: the surveyors are asked to produce day by day draft plans and sections which allow archaeologists to plan their future activities. The survey is realized during the excavations or just at the end of every working day and drawings have to be produced as soon as possible in order to allow the comprehension of the work done and to plan the activities for the following day. By using this technique, all the measurements, even those not necessary for the day after, have to be acquired in order to avoid a ‘loss of memory’. A possible alternative to this traditional approach is aerial photogrammetry, if the images can be acquired quickly and at a taken distance able to guarantee the necessary accuracy of a few centimeters. Today the use of UAVs (Unmanned Aerial Vehicles) can be considered a proven technology able to acquire images at distances ranging from 4 m up to 20 m: and therefore as a possible monitoring system to provide the necessary information to the archaeologists day by day. The control network, usually present at each archaeological site, can give the stable control points useful for orienting a photogrammetric block acquired by using an UAV equipped with a calibrated digital camera and a navigation control system able to drive the aircraft following a pre-planned flight scheme. Modern digital photogrammetric software can solve for the block orientation and generate a DSM automatically, allowing rapid orthophoto generation and the possibility of producing sections and plans. The present paper describes a low cost UAV system realized by the research group of the Politecnico di Torino and tested on a Roman villa archaeological site located in Aquileia (Italy), a well-known UNESCO WHL site. The results of automatic orientation and orthophoto production are described in terms of their accuracy and the completeness of information guaranteed for archaeological site excavation management.

The purpose of this paper is to discuss how much the phases of flight planning and the setting of the camera orientation can affect a UAVs photogrammetric survey. The test site chosen for these evaluations was the Rocca of San Silvestro, a medieval monumental castle near Livorno, Tuscany (Italy). During the fieldwork, different sets of data have been acquired using different parameters for the camera orientation and for the set up of flight plans. Acquisition with both nadiral and oblique orientation of the camera have been performed, as well as flights with different direction of the flight lines (related with the shape of the object of the survey). The different datasets were then processed in several blocks using Pix4D software and the results of the processing were analysed and compared. Our aim was to evaluate how much the parameters described above can affect the generation of the final products of the survey, in particular the product chosen for this evaluation was the point cloud.

The techniques of measuring and 3D modelling based on images, as is typical in photogrammetry, grew in interest again in recent years, since a new generation of software tools has spread. These ones implement in different measure the algorithms developed by computer vision, increasing the automation of the standard photogrammetric process. This made the use of image-based approaches for 3D models reconstruction enormously increase, which is an essential part of the Cultural Heritage documentation and analysis processes. Starting from these assumptions, the aim of the paper is to evaluate what and where it is possible nowadays to find the main differences between photogrammetry and computer vision approaches and how these have to be considered in the choice of the processing technique. The analysis has been performed starting from a theoretical point of view in order to trace the main characteristics of the two methods. Moreover, in order to complete the investigation, an experimental part is reported on two particular cases study, considered as representative of two types of usually surveyed objects. The results allow to enlighten some differences between the two image processing approaches, in terms of accuracy and achieved products.

In recent years, numerous natural disaster affected the Italian territory, growing the demand of secure on-field procedures able to accomplish the task of reconnaissance, inspection and survey of instable infrastructure and cultural heritage. During the early-impact there are numerous first response figures, like firefighters, civil protection, geotechnical inspectors and surveyors, that have to operate nearby damaged structure putting at risk their lives in case of possible collapses. Most of these operators work in heavy conditions, so they need tools and procedures that allow them to rush into decisions. These procedures must be rapid, easy to use and must allow the possibility to conduct the task with a certain distance of the instable or not easily accessible structures. This paper deals with the use of tablet devices for rapid close range photogrammetry in post-earthquake scenario, following the close range photogrammetry approach. Starting from images acquired by tablets during speditive surveys, the authors show how is possible to reconstruct the environment starting from data acquired by low cost devices. The case-study considered in this work is the Sant’Agostino Church in Amatrice (Italy), a cultural heritage highly damaged by the 6.0 magnitude earthquake occurred on 24 August 2016 in the central part of Italy.

Modern radio astronomical facilities in the Very High-Frequency band (VHF, 30-300 MHz) consist of large antenna arrays with digital beamforming. The calibration of these instruments requires accurate models of their electromagnetic behavior. Numerical models covering the full telescope field-of-view are nowadays effectively computed by means of full-wave solvers. However, their experimental verification is still an open and challenging task. This letter proposes a verification strategy based on near-field scans. An unmanned aerial vehicle carrying a test source is used to excite the array from a limited set of spatial points, located in the radiating near-field region. The collected data are directly used to verify the telescope electromagnetic model without additional postprocessing. This exploits the capability of full-wave solvers to predict near-field patterns from the same geometrical and electrical model used to compute far-field patterns. The application of the proposed technique to the Low Frequency Array (LOFAR) radio telescope verified the predictions with residuals below 0.5 dB.

In recent years natural- and human-induced hazards have turned into disasters with rising frequency and intensity. These disasters pose threats to prominent cultural and natural heritage sites of the world and therefore require the intervention of skilled technicians and staff with specific scientific and cultural training. It is necessary to contribute toward reducing slow as well as catastrophic risks in the short and long terms through the training of staff for intervention aimed at emergency management and mitigation of the impact and development of solutions with the collaboration of professionals working in this area. This paper presents a project realized by a multidisciplinary team of engineers, architect, hydrologists, and geologists, carried out through the cooperation between Politecnico di Torino and the Information Technology for Humanitarian Assistance, Cooperation and Action Association (ITHACA). The main goal was to provide a first training in geospatial data acquisition and processing for students attending the bachelor and master degree in architecture and engineering in order to start up a team of “volunteer mappers.” Indeed, the project is aimed at documenting the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration, and sharing using modern geomatic methodology and techniques (terrestrial and aerial LiDAR, close-range and aerial photogrammetry, topographic and Global Navigation Satellite System (GNSS) instruments, etc.). All the acquired data and processing results have been implemented in a WebGIS platform to share information with local authorities and organizations, such as Protezione Civile (Italian Civil Protection). The proposed area for testing the training activities is the village of Vernazza inside the Cinque Terre National Park in Liguria (Italy). The park is registered on the World Heritage list since 1997. The area was affected by an important flood on 25 October 2011.

Low Frequency Aperture Array (LFAA) systems are opening up new frontiers for ground-based radio astronomical instruments. These instruments require advanced measurement procedures and systems for characterization and calibration of their front-end. For this purpose, a test source mounted on an Unmanned Aerial Vehicle (UAV) has been developed in Italy. This system has proven to be a reliable, accurate, portable and effective tool to characterize LFAA systems as demonstrated during measurement campaigns on two Aperture Array Verification Systems for the LFAA of the SKA in the UK and on a LOFAR station in The Netherlands. This contribution gives an overview of these campaigns, the continuous development of the UAV-based measurement system and the envisaged future improvements.