Coarse-to-Fine Classification of Road Infrastructure Elements from Mobile Point Clouds Using Symmetric Ensemble Point Network and Euclidean Cluster Extraction.

Classifying point clouds obtained from mobile laser scanning of road environments is a fundamental yet challenging problem for road asset management and unmanned vehicle navigation. Deep learning networks need no prior knowledge to classify multiple objects, but often generate a certain amount of false predictions. However, traditional clustering methods often involve leveraging a priori knowledge, but may lack generalisability compared to deep learning networks.

Extreme rainfall trends of 21 typical urban areas in China during 1998-2015 based on remotely sensed data sets.

With the increase of population, many cities are growing in size at a phenomenal rate. Urbanization changes the urban underlying surface, influences the micro-climate, and sometimes affects the local precipitation process. In this study, we investigated the trends of extreme rainfall in China's 21 typical urban areas.

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. [..

Geodetic and Remote-Sensing Sensors for Dam Deformation Monitoring.

In recent years, the measurement of dam displacements has benefited from a great improvement of existing technology, which has allowed a higher degree of automation. This has led to data collection with an improved temporal and spatial resolution. Robotic total stations and GNSS (Global Navigation Satellite System) techniques, often in an integrated manner, may provide efficient solutions for measuring 3D displacements on precise locations on the outer surfaces of dams.

Development and implementation of image-based algorithms for measurement of deformations in material testing.

This paper presents the development and implementation of three image-based methods used to detect and measure the displacements of a vast number of points in the case of laboratory testing on construction materials. Starting from the needs of structural engineers, three ad hoc tools for crack measurement in fibre-reinforced specimens and 2D or 3D deformation analysis through digital images were implemented and tested. These tools make use of advanced image processing algorithms and can integrate or even substitute some traditional sensors employed today in most laboratories.