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Change detection is one of the main topics in Earth Observation, due to its wide range of applications, varying from urban development monitoring to natural disaster management. Most of the recently developed change detection methodologies rely on the use of deep learning algorithms. These kinds of algorithms are generally focused on generating two-dimensional (2D) change maps, thus they are only able to detect horizontal changes in land use/land cover, not considering nor returning any information on the corresponding elevation changes. Our work proposes a step forward, creating and sharing a dataset where two optical images acquired in different epochs are provided together with both the related 2D change maps containing land use/land cover variations and the three-dimensional (3D) maps containing elevation changes. Particularly, our aim is to provide a dataset useful to address and possibly solve the change detection task in 3D. Indeed, the proposed dataset, on the one hand, can empower a further development of 2D change detection algorithms, and, on the other hand, can allow to develop algorithms able to provide 3D change detection maps from two optical images captured in different epochs, without the need to rely directly on elevation data as input. The proposed dataset is publicly available at the following link: https://bit.ly/3wDdo41.

Freshwater is one the most important renewable water resources of the planet but, due to climate change, surface freshwater available in the form of lakes, rivers, reservoirs, snow, and glaciers is becoming significantly threatened. As a result, surface water level monitoring is fundamental for understanding climatic changes and their impact on humans and biodiversity.This study evaluates the accuracy of the Global Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection And Ranging) instrument for monitoring inland water levels. Four lakes in northern Italy were selected for comparison with gauge station measurements. To evaluate the accuracy of GEDI altimetric data, two steps of outlier removal are proposed. The first stage employs GEDI metadata to filter out footprints with very low accuracy. Then, a robust version of the standard 3σ test using a 3NMAD (Normalized Median Absolute Deviation) test is iteratively applied.After the outlier removal, which led to the elimination of between 80% to 87% of the data, the remaining footprints show an average standard deviation of 0.36 m, a mean NMAD of 0.38 m, and a Root Mean Square Error (RMSE) of 0.44 m, proving the promising potentialities of GEDI L2A altimetric data for inland water monitoring.

ABSTRACT Water reservoirs are an essential resource for human health, natural ecosystems, and socio-economic activities, making their effective monitoring mandatory for informed decision-making on sustainable water management. Particularly important is monitoring the extent and level, which enable determination of volume variations. In this respect, this work investigates the performance of Segment Anything Model (SAM) – a foundation model for segmentation released by Meta AI Research – in segmenting water bodies from medium-resolution satellite imagery. SAM was applied in its original form to Sentinel-1 and Sentinel-2 images through prompt engineering (seed modality), testing five different 3-band combinations of the input images in two areas of the Ligurian coast (Italy). Overall, the study demonstrates the adaptability and efficiency of SAM in segmenting water bodies. Among the tested configurations, the SAR 3-band combination achieved the best performance, with$F1$F 1 scores ranging from 0.874 to 0.994. Furthermore, SAM performs better in simpler scenarios with uniform radiometric properties and regular water boundaries, achieving$F1$F 1 score close to 1 with seeds in any position within the water body. Conversely, in more complex scenarios, accurate seed prompt placement becomes critical; analysis of$F1$F 1 maps revealed that seeds placed near the edge of the water, where sharp gradients occur, significantly improve SAM segmentation performance.

3D modelling of inscribed archaeological finds (such as tablets or small objects) has to consider issues related to the correct acquisition and reading of ancient inscriptions, whose size and degree of conservation may vary greatly, in order to guarantee the needed requirements for visual inspection and analysis of the signs. In this work, photogrammetry and laser scanning were tested in order to find the optimal sensors and settings, useful to the complete 3D reconstruction of such inscribed archaeological finds, paying specific attention to the final geometric accuracy and operative feasibility in terms of required sensors and necessary time. Several 3D modelling tests were thus carried out on four replicas of inscribed objects, which are characterized by different size, material and epigraphic peculiarities. Specifically, in relation to photogrammetry, different cameras and lenses were used and a robust acquisition setup, able to guarantee a correct and automatic alignment of images during the photogrammetric process, was identified. The focus stacking technique was also investigated. The Canon EOS 1200D camera equipped with prime lenses and iPad camera showed respectively the best and the worst accuracy. From an overall geometric point of view, 50 mm and 100 mm lenses achieved very similar results, but the reconstruction of the smallest details with the 50 mm lens was not appropriate. On the other hand, the acquisition time for the 50 mm lens was considerably lower than the 100 mm one. In relation to laser scanning, the ScanRider 1.2 model was used. The 3D models produced (in less time than using photogrammetry) clearly highlight how this scanner is able to reconstruct even the high frequencies with high resolution. However, the models in this case are not provided with texture. For these reasons, a robust procedure for integrating the texture of photogrammetry models with the mesh of laser scanning models was also carried out.

Water reservoirs are subjected to increasing hydrological stresses, therefore continuous and accurate monitoring of these resources is essential to ensure their sustainable management. This work proposes a methodology to remotely monitor the surface extent of water reservoirs through the analysis of satellite multispectral and Synthetic Aperture Radar (SAR) images. In particular, a segmentation strategy was implemented within Google Earth Engine (GEE) to distinguish water bodies from the surrounding land surface and measure their extension, by applying three different approaches to Sentinel-1, Sentinel-2, and Landsat-8 imagery. The first approach is based on the use of the Automatic Water Extraction Index (AWEI) and the self-adaptive Otsu’s thresholding method, the second approach is based on the image conversion from RGB (Red-Green-Blue) to HSV (Hue, Saturation, Value) and the use of a parametric threshold, the third approach is based on the use of SAR imagery and an empirically selected threshold. A “static” validation strategy was developed from scratch and standard segmentation metrics were computed to evaluate the accuracy of the three approaches. The average values of the F1 scores on the Sentinel imagery were equal to 0.95, 0.90, and 0.84 for the three approaches, respectively. The same metric on the Landsat imagery was 0.95 for the first approach and 0.93 for the second approach. The best approach, i.e. the AWEI-based method, was then applied to three water bodies in which the effects of the 2022 drought were particularly significant: Sawa lake (Iraq), Poyang lake (China), and Po river (Italy). The results visually highlighted the good performance of the approach in segmenting the water bodies from the surrounding areas.

The research is framed in multimedia photogrammetry, a specific domain aimed at acquiring geometric information about static objects immersed or semi-submerged in a liquid through one or more cameras external to the liquid. If the liquid is water, this field belongs to the broader field of applied metrology for analysing and understanding the aquatic world. Specifically, the various passive sensing techniques for acquiring Underwater Cultural Heritage (UCH) in shallow water are central to understanding the research domain's underlying issues. Our research is framed in the domain, implementing the automatic analysis to estimate a priori (and correcting a posteriori) the camera's behaviour under certain conditions for acquiring submerged or semi-submerged objects. The first analytical results are framed in a two-year project, which aims to define a behaviour model in a controlled environment with encoded targets and stereo-photogrammetry, automatically extracting the camera orientation parameters under different water height conditions. A planar reproduction of a CH artefact, which simulates an immersed architectonic floor, has been applied to validate the process in a first case study, testing the system's capacity to extract the correct coordinates of the image. At the end of this first experimental phase, the aim is to define a model for the behaviour of water deformation. It will make it possible to predict and correct the water refraction by calculating the correct coordinates within the liquid. In the future, this model will be tested under different and incrementally more complex acquisition conditions. The global project's primary goal is to arrive at the application of this model in an uncontrolled environment for the survey of UCH in shallow water.

Nowadays, it is essential to find new strategies, able to perform the first step of the scan-to-BIM process, by retrieving the geometrical information contained in point clouds that are so easily collected through laser scanners and range cameras. This paper presents a new algorithm for the automatic extraction of the layout and the height of a small indoor environment from its point cloud. In particular, the algorithm was tested on a point cloud of 600000 vertices, selected from the dataset of the ISPRS benchmark on indoor modelling. The preliminary results are encouraging: the 3D shape (layout and height) of the investigated room is effectively reconstructed.

Civil infrastructures, such as tunnels and bridges, are directly related to the overall economic and demographic growth of countries. The aging of these infrastructures increases the probability of catastrophic failures that results in loss of lives and high repair costs; all over the world, these factors drive the need for advanced infrastructure monitoring systems. For these reasons, in the last years, different types of devices and innovative infrastructure monitoring techniques have been investigated to automate the process and overcome the main limitation of standard visual inspections that are used nowadays. This paper presents some preliminary findings of an ongoing research project, named TACK, that combines advanced deep learning techniques and innovative photogrammetric algorithms to develop a monitoring system. Specifically, the project focuses on the development of an automatic procedure for crack detection and measurement using images of tunnels and bridges acquired with a mobile mapping system. In this paper, some preliminary results are shown to investigate the potential of a deep learning algorithm in detecting cracks occurred in concrete material. The model is a CNN (Convolutional Neural Network) based on the U-Net architecture; in this study, we tested the transferability of the model that has been trained on a small available labeled dataset and tested on a large set of images acquired using a customized mobile mapping system. The results have shown that it is possible to effectively detect cracks in unseen imagery and that the primary source of errors is the false positive detection of crack-like objects (i.e., contact wires, cables and tile borders).

Global Navigation Satellite System (GNSS) sensors represent nowadays a mature technology, low-cost and efficient, to collect large spatio-temporal datasets (Geo Big Data) of vehicle movements in urban environments. Anyway, to extract the mobility information from such Floating Car Data (FCD), specific analysis methodologies are required. In this work, the first attempts to analyse the FCD of the Turin Public Transportation system are presented. Specifically, a preliminary methodology was implemented, in view of an automatic and possible real-time impedance map generation. The FCD acquired by all the vehicles of the Gruppo Torinese Trasporti (GTT) company in the month of April 2017 were thus processed to compute their velocities and a visualization approach based on Osmnx library was adopted. Furthermore, a preliminary temporal analysis was carried out, showing higher velocities in weekend days and not peak hours, as could be expected. Finally, a method to assign the velocities to the line network topology was developed and some tests carried out.

DATE (Digital Automatic Terrain Extractor) is a Free and Open Source Software for Geospatial (FOSS4G), which combines photogrammetric and computer vision algorithms in order to automatically generate DSMs from multi-view SAR and optical high resolution satellite imagery, following an iterative and pyramidal workflow in order to refine a coarse DSM used as reference. Consequently, DATE is able to face both the issues of DSM generation and epipolar resampling of satellite imagery. The aim of this work is to evaluate DATE performance, by carrying out a sensitivity analysis based on the dense matching parameters. In particular, DATE implements the Semi-Global Block Matching (SGBM) algorithm, a modified version of Semi-Global Matching method: thus, the sensitivity analysis aims at assessing how SGBM parameters – namely, the difference between maximum and minimum disparity (ndisparities), the minimum disparity value (minimumDisp) and the matched block size (SADWindowSize) – affect the efficiency of the disparity map computation and the final DSM accuracy. The analysis focuses on the case study of Trento and of the Adige Valley, which was chosen due to its geomorphological heterogeneity and complexity, allowing to perform an accuracy evaluation on four tiles, characterized by specific roughness frequencies and morphologies (thus having different effects on disparity variations). Several practical indications on the optimal and critical parameter combinations were retrieved; in addition to this, this work highlighted the most influential parameters both in terms of accuracy (minimumDisp) and computation time (ndisparities), paving the way to further principal component analyses. Finally, the obtained results showed no clear relationship between the area morphology and the solution structure.