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Nel corso degli ultimi anni, la crescente disponibilità di scene acquisite da satelliti ad alta risoluzione spaziale (come GeoEye-1, WorldView-1 e 2 o Pleiades-1A e 1B) ha aperto nuovi scenari di applicazioni realizzabili a scala medio-piccola, avvicinando così il Telerilevamento alla Fotogrammetria.

Recently, there has been a growing interest in studying non-contact techniques for strain and displacement measurement. Within photogrammetry, Digital Image Correlation (DIC) has received particular attention thanks to the recent advances in the field of lowcost, high resolution digital cameras, computer power and memory storage. DIC is indeed an optical technique able to measure full field displacements and strain by comparing digital images of the surface of a material sample at different stages of deformation and thus can play a major role in structural monitoring applications. For all these reasons, a free and open source 2D DIC software, named py2DIC, was developed at the Geodesy and Geomatics Division of DICEA, University of Rome La Sapienza. Completely written in python, the software is based on the template matching method and computes the displacement and strain fields. The potentialities of Py2DIC were evaluated by processing the images captured during a tensile test performed in the Lab of Structural Engineering, where three different Glass Fiber Reinforced Polymer samples were subjected to a controlled tension by means of a universal testing machine. The results, compared with the values independently measured by several strain gauges fixed on the samples, demonstrate the possibility to successfully characterize the deformation mechanism of the investigated material. Py2DIC is indeed able to highlight displacements at few microns level, in reasonable agreement with the reference, both in terms of displacements (again, at few microns in the average) and Poisson’s module.

The production of reliable documentation of small finds is a crucial process during archaeological excavations. Range cameras can be a valid alternative to traditional illustration methods: they are veritable 3D scanners able to easily collect the 3D geometry (shape and dimensions in metric units) of an object/scene practically in real-time. This work investigates precisely the potentialities of a promising low-cost range camera, the Structure SensorTM by Occipital, for rapid modelling archaeological objects. The accuracy assessment was thus performed by comparing the 3D model of a Cipriot-Phoenician globular jug captured by this device with the 3D model of the same object obtained through photogrammetry. In general, the performed analysis shows that Structure Sensor is capable to acquire the 3D geometry of a small object with an accuracy comparable at millimeter level to that obtainable with the photogrammetric method, even though the finer details are not always correctly modelled. The texture reconstruction is instead less accurate. In the end, it can be concluded that the range camera used for this work, due to its low-cost and flexibility, is a suitable tool for the rapid documentation of archaeological small finds, especially when not expert users are involved.

Thanks to the advances in computer power, memory storage and the availability of low-cost and high resolution digital cameras, Digital Image Correlation (DIC) is currently one of the most used optical and non-contact techniques for measuring material deformations. A free and open source 2D DIC software, named py2DIC, was developed at the Geodesy and Geomatics Division of the Sapienza University of Rome. Implemented in Python, the software is based on the template matching method and computes the 2D displacements and strains of samples subjected to mechanical loading. In this work, the potentialities of py2DIC were evaluated by processing two different sets of experimental data and comparing the results with other three well known DIC software packages Ncorr, Vic-2D and DICe. Moreover, an accuracy assessment was performed comparing the results with the values independently measured by a strain gauge fixed on one of the samples. The results demonstrate the possibility of successfully characterizing the deformation mechanism of the investigated materials, highlighting the pros and cons of each software package.

In the last few decades, there has been a growing interest in studying non-contact methods for full-field displacement and strain measurement. Among such techniques, Digital Image Correlation (DIC) has received particular attention, thanks to its ability to provide these information by comparing digital images of a sample surface before and after deformation. The method is now commonly adopted in the field of civil, mechanical and aerospace engineering and different companies and some research groups implemented 2D and 3D DIC software. In this work a review on DIC software status is given at first. Moreover, a free and open source 2D DIC software is presented, named py2DIC and developed in Python at the Geodesy and Geomatics Division of DICEA of the University of Rome “La Sapienza”; its potentialities were evaluated by processing the images captured during tensile tests performed in the Structural Engineering Lab of the University of Rome “La Sapienza” and comparing them to those obtained using the commercial software Vic-2D developed by Correlated Solutions Inc, USA. The agreement of these results at one hundredth of millimetre level demonstrate the possibility to use this open source software as a valuable 2D DIC tool to measure full-field displacements on the investigated sample surface.

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto- Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters. In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale.