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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.

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.

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.

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.

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).

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.

This paper describes the first results of an image recognition based location (IRBL) for a mobile application focusing on the procedure to generate a database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surroundings is needed. To achieve this objective, a complete 3D survey of two different environments (Bangbae metro station of Seoul and the Electronic and Telecommunications Research Institute (ETRI) building in Daejeon, Republic of Korea) was performed using a LiDAR (Light Detection and Ranging) instrument, and the obtained scans were processed to obtain a spatial model of the environments. From this, two databases of reference images were generated using specific software realised by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allows us to generate synthetically different RGB-D images centred in each scan position in the environment. Later, the external parameters (X, Y, Z, ω, ϕ , and κ) and the range information extracted from the retrieved database images are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper, the survey operations, the approach for generating the RGB-D images, and the IRB strategy are reported. Finally, the analysis of the results and the validation test are described.

The monitoring of air quality parameters is a fundamental requirement for smart cities development and it is of primary importance for the quality of human life. In fact, the knowledge of air quality parameters along the days and in different areas of the city is essential to monitor its behavior and to take some preventive measurements to limit the concentration. Especially in big cities, it is very hard to have widespread updated data about pollutants and air quality parameters since normally only few air quality monitoring stations are available. In Piedmont (Italy), the reference public body for this kind of information is the ARPA (Regional Agency for the Protection of the Environment) which is responsible for the collection and the disclosure of environmental data. However, the number of monitoring stations along the city is limited and they have fixed positions. A solution through mobile sensors would be preferable to have a more comprehensive description of the phenomenon. In this paper, the authors describe the implementation of a new solution based on a mobile system to house environmental air quality and imaging sensors (since also the knowledge of the shape of the environment is a fundamental aspect). In particular, a bicycle is adopted and the paper describes all the analyses involved in the choice of the more appropriate sensors and their evaluation and behavior in a real environment. Finally, the collection and management of the acquired data is analyzed through the implementation of a dedicated GIS (Geographical Information System).

The main aim of the UAV-based measurement campaign conducted at a station of the LOw Frequency ARray (LOFAR) in 2016 was to validate the beam models for the LOFAR stations including mutual coupling, i.e., without assuming identical array elements. Such a validation can be performed as a verification of all the embedded-element patterns over the field-of view and the operative bandwidth. In this paper, the most significant measured results on the low-band elements are reported and discussed.

The horizontal alignment of existing highways may be identified by using several terrestrial or aerial geomatics technologies. Such technologies involve different levels of precision and accuracy; hence, different results can be expected. At present, there are no comparisons available between the solutions resulting from the use of different technologies and data sources for the same road alignment. In this investigation, a number of terrestrial mobile mapping techniques and data collection strategies were evaluated. The centerline of a 3.6 km section of a highway was used to estimate radii, centers of curvature and orientation of tangents. Two statistical fitting methods were used to back calculate these parameters, and the results were then compared with as built alignment data. Terrestrial images from a mobile mapping vehicle were used to determine the centerline, which was also estimated as the average line of the carriageway and pavement edges, and as the average line of the two driving trajectories. Positions were surveyed using low-cost sensors (an integrated GPS-IMU platform, HD webcam). For comparison purposes, aerial orthophotos and a GNSS (high-cost) receiver were used simultaneously. Although the GPS IMU data and estimated trajectories provided results comparable to those of the GNSS receiver, the use of georeferenced images proved less accurate. The results and comments in the paper should be of use to survey practitioners when they need to select an acquisition methodology appropriate to the desired level of accuracy and in line with budget constraints. This is the authors' manuscript to the article (preprint).

In the recent years, the authors developed a system for the characterization of the radiation pattern of VHF and UHF antennas by means of a test-source mounted on a micro Unmanned Aerial Vehicle (UAV). So far, the adopted scan strategies typically consisted of two orthogonal straight paths at constant height from ground to obtain E- or H-plane cuts. In this paper, more complex scan strategies are presented to perform two-dimensional pattern measurements over planar and curved surfaces.

Smartphone technology is widespread both in the academy and in the commercial world. Almost every people have today a smartphone in their pocket, that are not only used to call other people but also to share their location on social networks or to plan activities. Today with a smartphone we can compute our position using the sensors settled inside the device that may also include accelerometers, gyroscopes and magnetometers, teslameter, proximity sensors, barometer, and GPS/GNSS chipset. In this chapter we want to analyze the state-of-the-art of the positioning with smartphone technology, considering both outdoor and indoor scenarios. Particular attention will be paid to this last situation, where the accuracy can be improved fusing information coming from more than one sensor. In particular, we will investigate an innovative method of image recognition based (IRB) technology, particularly useful in GNSS denied environment, taking into account the two main problems that arise when the IRB positioning methods are considered: the first one is the optimization of the battery, that implies the minimization of the frame rate, and secondly the latencies due to image processing for visual search solutions, required by the size of the database with the 3D environment images.

The cultural and artistic heritage has always been at the center of activities aimed at its preservation and enhancement. Italy is a country particularly rich in terms of heritage to be protected, where the high-risk due to natural hazard, as earthquakes, landslides and floods, which are adds to human activities, contribute to make the heritage more frail, land needs to be safeguarded and enhanced and new mass market technology can be considered as innovative tools for the documentation of cultural heritage. In order to increase our country on the artistic point of view, it must be known in an historical and cultural way. Moreover, it is important also to define the cultural heritage on metric terms, to be able to describe and represent it with the best approach, with the purpose to offer to the people who comes to visit our beautiful country, the reliable model of some important object, that is no longer in exposition. The possibility to use the mass-market devices can allow us to realize it, because they are available for the greater part of the visitors, in a photogrammetric way to reconstruct our models. In the last years, these devices have been very improved and the embedded sensors are becoming more and more efficient in terms of precision and reliability. Also several small video cameras are now used to document our travels and activities and to share them through Internet. In this scenario, the aim of this research is to study and validate the possibility to use mass-market technology for this purpose, testing four different devices (smartphones and video cameras) for the documentation of the cultural heritage.

During the past years, UAVs (Unmanned Aerial Vehicles) became very popular as low-cost image acquisition platforms since they allow for high resolution and repetitive flights in a flexible way. One application is to monitor dynamic scenes. However, the fully automatic co-registration of the acquired multi-temporal data still remains an open issue. Most UAVs are not able to provide accurate direct image georeferencing and the co-registration process is mostly performed with the manual introduction of ground control points (GCPs), which is time consuming, costly and sometimes not possible at all. A new technique to automate the co-registration of multi-temporal high resolution image blocks without the use of GCPs is investigated in this paper. The image orientation is initially performed on a reference epoch and the registration of the following datasets is achieved including some anchor images from the reference data. The interior and exterior orientation parameters of the anchor images are then fixed in order to constrain the Bundle Block Adjustment of the slave epoch to be aligned with the reference one. The study involved the use of two different datasets acquired over a construction site and a post-earthquake damaged area. Different tests have been performed to assess the registration procedure using both a manual and an automatic approach for the selection of anchor images. The tests have shown that the procedure provides results comparable to the traditional GCP-based strategy and both the manual and automatic selection of the anchor images can provide reliable results.

A UAV-mounted radio-frequency transmitter is proposed as a known reference field source to perform a set of functional tests on aperture arrays. The experimental results obtained on complete prototypes (end-to-end) and sub-assemblies provide good confidence on both amplitude and timing verification.

An essential support for environmental monitoring activities is a rigorous definition of a homogeneous cartographic system, required to correctly georeference and analyse the acquired data. Furthermore, since 2007, the European Infrastructure for Spatial Information in the European Community (INSPIRE) Directive affirms the necessity to harmonise the European maps for permitting cross-border analysis. For satisfying these requirements, the authors have developed a procedure for the cartographic harmonisation in the cross-border area studied during the European project Alpes Latines-Cooperation Transfrontaliere (ALCOTRA)–Alpes Latines-Individuation Resources Hydriques Souterraines (ALIRHyS). It concerns the hydrogeological study of various springs and other water resources in an area between Italy and France including their constitution in a cross-border system. The basic cartographic information is obtained from existing national maps (Italian and French data), which use different coordinate systems or projection methods and are produced from different data acquisitions and processes. In this paper, the authors describe the methods used to obtain well-harmonised middle-scale maps (aerial orthophotos, digital terrain model and digital maps). The processing has been performed using geographic information system (GIS) solutions or image analysis software in order to obtain useful and correct cartographic support for the monitoring data, even if the obtained maps could be further analysed or refined in future works.

Air quality is a factor of primary importance for the quality of life. The increase of the pollutants percentage in the air can cause serious problems to the human and environmental health. For this reason it is essential to monitor its values to prevent the consequences of an excessive concentration, to reduce the pollution production or to avoid the contact with major pollutant concentration through the available tools. Some recently developed tools for the monitoring and sharing of the data in an effective system permit to manage the information in a smart way, in order to improve the knowledge of the problem and, consequently, to take preventing measures in favour of the urban air quality and human health. In this paper, the authors describe an innovative solution that implements geomatics sensors (GNSS) and pollutant measurement sensors to develop a low cost sensor for the acquisition of pollutants dynamic data using a mobile platform based on bicycles. The acquired data can be analysed to evaluate the local distribution of pollutant density and shared through web platforms that use standard protocols for an effective smart use.

This paper deals with the electromagnetic modeling of a dipole antenna mounted on a micro Unmanned Aerial Vehicle (UAV). Such a setup has been recently used as a test source to characterize the radiation pattern of fairly large VHF and UHF antennas in their installation conditions i.e. on the ground, with adjacent elements. In this framework, an accurate modeling of the test source is fundamental to distinguish properly its radiation characteristics from those of the antenna under test. The computed results are verified with both reflection coefficient and pattern measurements showing a very good agreement.

(TLS, Terrestrial Laser Scanning), and large scale mapping derived by UAV (Unmanned Aerial Vehicle) survey. This paper refers an example of 3D survey and reality based modelling applied on landscape and architectural assets. The choice of methods for documentation, in terms of survey techniques, depends primarily on issues and features of the area. The achieved experience, allow to consider that the easy handling of TLS has enabled the use in limited spaces among buildings and collapsed roofs, but the topographic measure of GCPs (Ground Control Points), neither by total station nor by GPS/RTK technique, was easily feasible. Even more than proving the ability of the integration of TLS and UAV photogrammetry to achieve a multi-source and multi-scale whole model of a village, the experience has been a test to experiment the registration of terrestrial clouds with the support of control points derived by UAV survey and finally, a comparison among different strategies of clouds registration is reported. Analysing for each approach a number of parameters (number of clouds registration, number of needed points, processing time, overall accuracy) the further comparisons have been achieved. The test revealed that it is possible to decrease the large number of terrestrial control points when their determination by topographical measures is difficult, and it is possible to combine the techniques not only for the integration of the final 3Dmodel, but also to solve and make the initial stage of the drafting process more effective.

This paper is focused on two main topics: crime scene reconstruction, based on a geomatic approach, and crime scene analysis, through GIS based procedures. According to the experience of the authors in performing forensic analysis for real cases, the aforesaid topics will be examined with the specific goal of verifying the relationship of human walk paths at a crime scene with blood patterns on the floor. In order to perform such analyses, the availability of pictures taken by first aiders is mandatory, since they provide information about the crime scene before items are moved or interfered with. Generally, those pictures are affected by large geometric distortions, thus - after a brief description of the geomatic techniques suitable for the acquisition of reference data (total station surveying, photogrammetry and laser scanning) - it will be shown the developed methodology, based on photogrammetric algorithms, aimed at calibrating, georeferencing and mosaicking the available images acquired on the scene. The crime scene analysis is based on a collection of GIS functionalities for simulating human walk movements and creating a statistically significant sample. The developed GIS software component will be described in detail, showing how the analysis of this statistical sample of simulated human walks allows to rigorously define the probability of performing a certain walk path without touching the bloodstains on the floor.

Exploiting the decrease of costs related to UAV technology, the humanitarian community started piloting the use of similar systems in humanitarian crises several years ago in different application fields, i.e., disaster mapping and information gathering, community capacity building, logistics and even transportation of goods. Part of the author’s group, composed of researchers in the field of applied geomatics, has been piloting the use of UAVs since 2006, with a specific focus on disaster management application. In the framework of such activities, a UAV deployment exercise was jointly organized with the Regional Civil Protection authority, mainly aimed at assessing the operational procedures to deploy UAVs for mapping purposes and the usability of the acquired data in an emergency response context. In the paper the technical features of the UAV platforms will be described, comparing the main advantages/disadvantages of fixed-wing versus rotor platforms. The main phases of the adopted operational procedure will be discussed and assessed especially in terms of time required to carry out each step, highlighting potential bottlenecks and in view of the national regulation framework, which is rapidly evolving. Different methodologies for the processing of the acquired data will be described and discussed, evaluating the fitness for emergency response applications.

In the framework of the Square Kilometer Array (SKA) project, the Italian Institute for Astrophysics (INAF) has addressed several efforts in the design and prototyping of aperture arrays for low-frequency radio astronomical research. The Sardinia Array Demonstrator (SAD) is a national project aimed to develop know-how in this area and to test different architectural technologies and calibration algorithms. SAD consists of 128 prototypical dual-polarized Vivaldi antennas designed to operate at radio frequencies below 650 MHz. The antennas will be deployed at the Sardinia Radio Telescope’s site with a versatile approach able to provide two different array configurations: (i) all antennas grouped in one large station or (ii) spread among a core plus few satellite stations. This paper provides an overview of the SAD project from an instrumental point of view, and illustrates its status after 2 years from its start.

The National Institute for Astrophysics (INAF) is deeply involved in the designing phase of the Low Frequency Aperture Array (LFAA) of the Square Kilometer Array (SKA). In particular, the technological contributions of INAF are basically spread in three main areas: (i) antenna and array pattern characterization and calibration, (ii) analog and photonic receiver design, and (iii) development of digital back-ends FPGA-based.

The advent of smartphones and tablets, means that we can constantly get information on our current geographical location. These devices include not only GPS/GNSS chipsets but also mass-market inertial platforms that can be used to plan activities, share locations on social networks, and also to perform positioning in indoor and outdoor scenarios. This paper shows the performance of smartphones and their inertial sensors in terms of gaining information about the user’s current geographical locatio n considering an indoor navigation scenario. Tests were carried out to determine the accuracy and precision obtainable with internal and external sensors. In terms of the attitude and drift estimation with an updating interval equal to 1 s, 2D accuracies of about 15 cm were obtained with the images. Residual benefits were also obtained, however, for large intervals, e.g. 2 and 5 s, where the accuracies decreased to 50 cm and 2.2 m, respectively.

Innovative technologies such as ICTs (Information and Communications Technologies) are recognized as being a key player against climate change and the use of sensors and actuators can efficiently control the whole energy chain in the Smart Thermal Grids at district level. On the other side, advances on 3D modelling, visualization and interaction technologies enable user profiling and represent part of the holistic approach which aims at integrating renewable energy solutions in the existing building stock. To unlock the potentiality of these technologies, the case study selected for this research focuses on interoperability between Building Information Models (BIM), GIS (Geographic Information System) models and Energy Analysis Models (EAM) for designing Renewable Energy Strategies (RES) among the demonstrator. The objectives aim at making a whole series of data concerning the energy efficiency and reduction at district level usable for various stakeholders, by creating a District Information Model (DIM). The described system also integrates BIM and district level 3D models with real-time data from sensors to analyse and correlate buildings utilization and provide real-time energy-related behaviours. An important role is played by the energy simulation through the EAM for matching measured and simulated data and to assess the energy performance of buildings starting from a BIM model or shared data. With this purpose interoperability tests are carried out between the BIM models and quasi-steady energy analysis tools in order to optimize the calculation of the energy demand according to the Italian technical specification UNI TS 11300. Information about the roofs slope and their orientation from the GIS model are used to predict the use of renewable energy – solar thermal and PV – within the selected buildings (both public and private) of the demonstrator in Turin, Italy. The expected results are a consistent reduction in both energy consumption and CO 2 emissions by enabling a more efficient energy distribution policies, according to the real characteristics of district buildings as well as a more efficient utilization and maintenance of the energy distribution network, based on social behaviour and users attitudes and demand. In the future the project will allow open access with personal devices and A/R visualization of energy-related information to client applications for energy and cost-analysis, tariff planning and evaluation, failure identification and maintenance, energy information sharing in order to increase the user's awareness in the field of energy consumption.

A novel antenna pattern measurement technique has been recently developed exploiting the capabilities of a micro Unmanned Aerial Vehicle (UAV) as a far-field test-source. This technique is suitable for characterizing VHF antennas such as those for low-frequency radio astronomy. This paper presents some of the measurements recently performed on the Square Kilometer Array Log-periodic Antenna (SKALA), which has been selected as the receiving element for the Low Frequency Aperture Array (LFAA).

A novel antenna measurement strategy has been developed to characterize both the embedded-element and the array patterns in the real installation conditions at low frequencies (50 MHz - 650 MHz) [1]. It exploits a micro Unmanned Aerial Vehicle (UAV) as a far-field test source. The system demonstrated good performance on the copolarization radiation pattern of VHF and UHF antennas. Several results with reference antennas have been published [2, 3]. Further experiments have been performed on the Aperture Array Verification System (AAVS0) array and the Medicina Array Demonstrator (MAD), which also focus on the calibration of a small array and the beam forming. This paper presents two recent experiments performed on a log-periodic antenna at 250 MHz and 350 MHz. Significant results have been achieved for both co- and cross-polarization patterns. Moreover, the symmetry of the test-source pattern has also been demonstrated.

One of the most challenging aspects of the new-generation Low-Frequency Aperture Array (LFAA) radio telescopes is instrument calibration. The operational LOw-Frequency ARray (LOFAR) instrument and the future LFAA element of the Square Kilometre Array (SKA) require advanced calibration techniques to reach the expected outstanding performance. In this framework, a small array, called Medicina Array Demonstrator (MAD), has been designed and installed in Italy to provide a test bench for antenna characterization and calibration techniques based on a flying artificial test source. A radio-frequency tone is transmitted through a dipole antenna mounted on a micro Unmanned Aerial Vehicle (UAV) (hexacopter) and received by each element of the array. A modern digital FPGA-based back-end is responsible for both data-acquisition and data-reduction. A simple amplitude and phase equalization algorithm is exploited for array calibration owing to the high stability and accuracy of the developed artificial test source. Both the measured embedded element patterns and calibrated array patterns are found to be in good agreement with the simulated data. The successful measurement campaign has demonstrated that a UAV-mounted test source provides a means to accurately validate and calibrate the full-polarized response of an antenna/array in operating conditions, including consequently effects like mutual coupling between the array elements and contribution of the environment to the antenna patterns. A similar system can therefore find a future application in the SKA-LFAA context.

This paper describes a positioning algorithm for mobile phones based on image recognition. The use of image recognition based (IRB) positioning in mobile applications is characterized by the availability of a single camera for estimate the camera position and orientation. A prior knowledge of 3D environment is needed in the form of a database of images with associated spatial information that can be built projecting the 3D model on a set of synthetic solid images (range + RGB images). The IRB procedure proposed by the authors can be divided in two steps: the selection from the database of the most similar image to the query image used to locate the camera and the estimation of the position and orientation of the camera based on available 3D data on the reference image. The MPEG standard Compact Descriptors for Visual Search (CDVS) has been used to reduce hugely the processing time. Some practical results of the location methodology in outdoor environment have been described in terms of processing time and accuracy of position and attitude.

Dense matching techniques, implemented in many commercial and open source software, are useful instruments for carrying out a rapid and detailed analysis of complex objects, including various types of details and surfaces. For this reason these tools were tested in the metric survey of a frescoed ceiling in the hall of honour of a baroque building. The surfaces are covered with trompe-l’oeil paintings which theoretically can give a very good texture to automatic matching algorithms but in this case problems arise when attempting to reconstruct the correct geometry: in fact, in correspondence with the main architectonic painted details, the models present some irregularities, unexpectedly coherent with the painted drawing. The photogrammetric models have been compared with data deriving from a LIDAR survey of the same object, to evaluate the entity of this blunder: some profiles of selected sections have been extracted, verifying the different behaviours of the software tools.