Estrazione di layer vettoriali per utilizzo cartografico da immagini satellitari ad alta risoluzione.

Le immagini ad alta risoluzione riprese dai sensori spaziali, grazie alla loro crescente disponibilità e al continuo miglioramento degli algoritmi di ortorettifica resi disponibili agli utenti finali, sembrano poter diventare in un prossimo futuro utili strumenti per la produzione e l’aggiornamento di cartografia a media-grande scala. Attualmente, le possibilità di restituzione dalle immagini satellitari sono fortemente influenzate dalla difficoltà di ottenere coppie stereoscopiche (almeno per i satelliti a più alta risoluzione) e questo, di fatto, ne limita ancora l’utilizzo alla sola produzione di cartografia 2D. Evidentemente, il prodotto cartografico che può essere ricavato per questa via è diverso rispetto alla cartografia numerica 3D ricavata con metodi aero-fotogrammetrici: la terza dimensione dell’oggetto non può essere calcolata e la dimensione del pixel dei sensori attualmente disponibili per usi civili consente la produzione di cartografia esclusivamente alle scale comprese tra 1:10.000 e 1:5.000. In questo lavoro si presenta un primo test di estrazione di layer vettoriali (edifici e strade) da immagini IKONOS pancromatiche sulla zona del lago di Lecco, da utilizzare successivamente per la produzione di cartografia alla scala 1:10.000. I layer vettoriali estratti dalle immagini IKONOS sono stati confrontati con la CTR 1:10.000 e con alcune ortofoto dell’area test, verificando l’ottenimento delle precisioni planimetriche previste per la produzione della cartografia alla scala 1:10.000

Sensors for deformation monitoring of large civil infrastructures

In the maintenance of large infrastructures such as dams, bridges, railways, underground structures (tunnels, mines) and others, monitoring of deformations plays a key role in maintaining the safety serviceability conditions and for mitigating any consequences due to ageing factors and possible structural failures. [...]

Laser scanning and data integration for three-dimensional digital recording of complex historical structures: The case of mevlana museum

Terrestrial laser scanning method is widely used in three-dimensional (3-D) modeling projects. Nevertheless it usually requires measurement data from other sources for full measurement of the shapes. In this study a 3-D model of the historical Mevlana Museum (Mevlana Mausoleum) in Konya, Turkey was created using state-of-the art measurement techniques. The building was measured by terrestrial laser scanner (TLS). In addition, some shapes of the indoor area were measured by a time-of-flight camera. Thus, a 3-D model of the building was created by combining datasets of all measurements. The point cloud model was created with 2.3 cm and 2.4 cm accuracy for outdoor and indoor measurements, and then it was registered to a georeferenced system. In addition a 3-D virtual model was created by mapping the texture on a mesh derived from the point cloud

UNDERSTANDING 3D POINT CLOUD DEEP NEURAL NETWORKS BY VISUALIZATION TECHNIQUES

Abstract. In the wake of the success of Deep Learning Networks (DLN) for image recognition, object detection, shape classification and semantic segmentation, this approach has proven to be both a major breakthrough and an excellent tool in point cloud classification. However, understanding how different types of DLN achieve still lacks. In several studies the output of segmentation/classification process is compared against benchmarks, but the network is treated as a "black-box" and intermediate steps are not deeply analysed. Specifically, here the following questions are discussed: (1) what exactly did DLN learn from a point cloud? (2) On the basis of what information do DLN make decisions? To conduct such a quantitative investigation of these DLN applied to point clouds, this paper investigates the visual interpretability for the decision-making process. Firstly, we introduce a reconstruction network able to reconstruct and visualise the learned features, in order to face with question (1). Then, we propose 3DCAM to indicate the discriminative point cloud regions used by these networks to identify that category, thus dealing with question (2). Through answering the above two questions, the paper would like to offer some initial solutions to better understand the application of DLN to point clouds

A Sustainable Approach for upgrading geographic databases based on high resolution satellite imagery

The availability of high-resolution satellite images could be exploited for upgrading geographic databases at medium scales (1:5,000-1:25,000) as alternative to aerial photogrammetry. The paper presents a procedure to carry out this task which is based on an automatic image-to-image registration procedure of new satellite data to existing ortho-photomaps that have to be upgraded. In order to get a regularization of control points extracted in automatic way, a technique implementing a neural network algorithm is applied. Once an image has been georeferenced, this can be ortho-corrected thanks to a DTM (nowadays available in almost all developed countries). However, the product which is obtained so far is still a raster maps. To cope with the increasing need of vector data in geographic geographic databases, some tests performed on the extraction of features (buildings and roads) from real high-resolution satellite images have been performed and results are shown here. Finally, to complete the data acquisition process, the use of GPS-GIS data-logger receivers in differential mode is proposed

Towards automatic reconstruction of indoor scenes from incomplete point clouds: door and window detection and regularization

In the last years, point clouds have become the main source of information for building modelling. Although a considerable amount of methodologies addressing the automated generation of 3D models from point clouds have been developed, indoor modelling is still a challenging task due to complex building layouts and the high presence of severe clutters and occlusions. Most of methodologies are highly dependent on data quality, often producing irregular and non-consistent models. Although manmade environments generally exhibit some regularities, they are not commonly considered. This paper presents an optimization-based approach for detecting regularities (i.e., same shape, same alignment and same spacing) in building indoor features. The methodology starts from the detection of openings based on a voxel-based visibility analysis to distinguish ‘occluded’ from ‘empty’ regions in wall surfaces. The extraction of regular patterns in windows is addressed from studying the point cloud from an outdoor perspective. The layout is regularized by minimizing deformations while respecting the detected constraints. The methodology applies for elements placed in the same planeXunta de Galicia | Ref. ED481B 2016/079-

Recognition of landslides in lunar impact craters

Landslides have been observed on several planets and minor bodies of the solar System, including the Moon. Notwithstanding different types of slope failures have been studied on the Moon, a detailed lunar landslide inventory is still pending. Undoubtedly, such will be in a benefit for future geological and morphological studies, as well in hazard, risk and suscept- ibility assessments. A preliminary survey of lunar landslides in impact craters has been done using visual inspection on images and digital elevation model (DEM) (Brunetti et al. 2015) but this method suffers from subjective interpretation. A new methodology based on polynomial interpolation of crater cross-sections extracted from global lunar DEMs is presented in this paper. Because of their properties, Chebyshev polynomials were already exploited for para- metric classification of different crater morphologies (Mahanti et al., 2014). Here, their use has been extended to the discrimination of slumps in simple impact craters. Two criteria for recognition have provided the best results: one based on fixing an empirical absolute thresholding and a second based on statistical adaptive thresholding. The application of both criteria to a data set made up of 204 lunar craters’ cross-sections has demonstrated that the former criterion provides the best recognition