EXTRACTION of DEMS and ORTHOIMAGES from ARCHIVE AERIAL IMAGERY to SUPPORT PROJECT PLANNING in CIVIL ENGINEERING

Archive aerial photos represent a valuable heritage to provide information about land content and topography in the past years. Today, the availability of low-cost and open-source solutions for photogrammetric processing of close-range and drone images offers the chance to provide outputs such as DEM's and orthoimages in easy way. This paper is aimed at demonstrating somehow and to which level of accuracy digitized archive aerial photos may be used within a such kind of low-cost software (Agisoft Photoscan Professional®) to generate photogrammetric outputs. Different steps of the photogrammetric processing workflow are presented and discussed. The main conclusion is that this procedure may come to provide some final products, which however do not feature the high accuracy and resolution that may be obtained using high-end photogrammetric software packages specifically designed for aerial survey projects. In the last part a case study is presented about the use of four-epoch archive of aerial images to analyze the area where a tunnel has to be excavated

A BIM/GIS DIGITALIZATION PROCESS TO EXPLORE THE POTENTIAL OF DISUSED RAILWAYS IN ITALY

Digitization processes, i.e., the practice of converting physical assets into digital assets, are gradually transforming the Architecture, Engineering, Construction and Owner Operators (AECOO) industry, and it concerns new and existing buildings and infrastructures. This study is about the management of disused railways and aims to present a BIM (Building Information Modelling) / GIS (Geographic Information Systems) workflow to identify and model disused railways buildings. The workflow was tested on the ‘Potenza Inferiore Scalo’ – ‘Laurenzara’ disused railway located in the South of Italy. The objective is the creation of a comprehensive digital database to help decision-makers give new utility to these existing infrastructures and buildings. And this is because the railway heritage can represent an essential factor in sustainable development processes and landscape regeneration

A 3D INDOOR-OUTDOOR BENCHMARK DATASET FOR LoD3 BUILDING POINT CLOUD SEMANTIC SEGMENTATION

Deep learning (DL) algorithms require high quality training samples as well as accurate and thorough annotations to work effectively. Up until now a limited number of datasets are available to train DL techniques for semantic segmentation of 3D building point clouds, except a few ones focusing on specific categories of constructions (e.g., cultural heritage buildings). This paper presents a new 3D Indoor/Outdoor building dataset (BIO dataset), which is aimed to provide a highly accurate, detailed, and comprehensive dataset to be used for applications related to sematic classification of buildings based on point clouds and meshes. This benchmark dataset contains 100 building models generated from existing polygonal models and belonging to different categories. These include commercial buildings, residential houses, industrial and institutional buildings. Structural elements of buildings are annotated into 11 semantic categories, following standards from IFC and CityGML. To verify the applicability of the BIO dataset for the semantic segmentation task, it has been successfully tested by using one machine learning technique and four different DL algorithms

VALIDATION OF FULL-RESOLUTION DINSAR-DERIVED VERTICAL DISPLACEMENT IN CULTURAL HERITAGE MONITORING: INTEGRATION WITH GEODETIC LEVELLING MEASUREMENTS

Towards revealing the potential of satellite Synthetic Aperture Radar (SAR) Interferometry (InSAR) for efficient detection and monitoring of Cultural Heritage (CH) encouraging resilient built CH, this study is devoted to the validation of InSAR-derived vertical displacements with a full-resolution perspective taking advantage of high-precision geodetic levelling measurements. Considering the Cathedral of Como, northern Italy, as the case study, two different Persistent Scatterer Interferometry (PSI) techniques have been applied to Cosmo-SkyMed high-resolution SAR images acquired in both ascending and descending orbit tacks within the time interval of 2010–2012. Besides using the simplified approach for obtaining the vertical displacement velocity from Line of Sight (LOS) velocity, a weighted, localized, multi-track Vertical Displacement Extraction (VDE) approach is proposed and evaluated, which uses the technical outcome of Differential InSAR (DInSAR) and spatial information. The results, using a proper PSI technique, showed that the accuracy level of extracted vertical displacement velocities in a full-resolution application is ca. 0.6 [mm/year] with a dense concentration of InSAR-Levelling absolute errors lower than 0.3 [mm/year] which are reliable and reasonable levels based on the employed validation framework in this study. Also, the weighted localized VDE can significantly decrease the InSAR-Levelling errors, adding to the reliability of the InSAR application for CH monitoring and condition assessment in practice

A PRE-TRAINING METHOD FOR 3D BUILDING POINT CLOUD SEMANTIC SEGMENTATION

Abstract. As a result of the success of Deep Learning (DL) techniques, DL-based approaches for extracting information from 3D building point clouds have evolved in recent years. Despite noteworthy progress in existing methods for interpreting point clouds, the excessive cost of annotating 3D data has resulted in DL-based 3D point cloud understanding tasks still lagging those for 2D images. The notion that pre-training a network on a large source dataset may help enhance performance after it is fine-tuned on the target task and dataset has proved vital in numerous tasks in the Natural Language Processing (NLP) domain. This paper proposes a straightforward but effective pre-training method for 3D building point clouds that learns from a large source dataset. Specifically, it first learns the ability of semantic segmentation by pre-training on a cross-domain source Stanford 3D Indoor Scene Dataset. It then initialises the downstream networks with the pre-trained weights. Finally, the models are fine-tuned with the target building scenes obtained from the ArCH benchmarking dataset. Our paper evaluates the proposed method by employing four fully supervised networks as backbones. The results of two pipelines are compared between training from scratch and pre-training. The results illustrate that pre-training on the source dataset can consistently improve the performance of the target dataset with an average gain of 3.9%

Calibration of close-range thermal imagery for integration into 3D VR models

The appearance of terrestrial laser scanners (TLS) has provided a new data source of geometric information. Several TLS allow to be equipped by a calibrated camera, whose images may be directly mapped on the DSM as photo-texture. Here a further improvement is proposed, i.e. the integration of thermal imagery into the 3D model in order to acquire knowledge about internal stratigraphy of walls, floors, ceilings and other ancient structures. Obviously, a fundamental pre-requisite to obtain this task is the calibration of thermal sensor and the orientation of each image into the object reference system of the TLS data. Unfortunately, due to the poor radiometric and geometric quality of themal images, their integration into the TLS 3D model is a complex task; moreover, looking for control points which could be measured on both 3D model and thermal image is not trivial. This leads to the failure of methods performing calibration and orientation in a unique task, such as self-calibration approaches. Calibration has to be performed in laboratory. We have performed the calibration of a thermal camera NEC Thermotracer TH 7102 WX by means of a calibration dig and the computation of inner calibration in a bundle block l.s. adjustment. Data processing has been performed by using a low-cost photogrammetric commercial software

AUTOMATIC REGISTRATION OF MULTI-SOURCE MEDIUM RESOLUTION SATELLITE DATA

Multi-temporal and multi-source images gathered from satellite platforms are nowadays a fundamental source of information in several domains. One of the main challenges in the fusion of different data sets consists in the registration issue, i.e., the integration into the same framework of images collected with different spatial resolution and acquisition geometry. This paper presents a novel methodology to accomplish this task on the basis of a method that stands out from existing approaches. The whole data (time series) set is simultaneously co-registered with a two-dimensional multiple Least Squares adjustment with different geometric transformations implemented. Some tests were carried out with different geometric transformation models (including similarity, affine, and polynomial) and variable matching thresholds. They showed a sub-pixel precision after the computation of multiple adjustment. The use of multi-image corresponding points allowed the improvement of the registration accuracy and reliability of a time series made up of data imaged with different sensors

Procedures for condition mapping using 360° images

The identification of deterioration mechanisms and their monitoring over time is an essential phase for conservation. This work aimed at developing a novel approach for deterioration mapping and monitoring based on 360° images, which allows for simple and rapid data collection. The opportunity to capture the whole scene around a 360° camera reduces the number of images needed in a condition mapping project, resulting in a powerful solution to document small and narrow spaces. The paper will describe the implemented workflow for deterioration mapping based on 360° images, which highlights pathologies on surfaces and quantitatively measures their extension. Such a result will be available as standard outputs as well as an innovative virtual environment for immersive visualization. The case of multi-temporal data acquisition will be considered and discussed as well. Multiple 360° images acquired at different epochs from slightly different points are co-registered to obtain pixel-to-pixel correspondence, providing a solution to quantify and track deterioration effects