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. Based on a series of daily rainfall and "Urban/built-up" dataset from TMPA 3B42 and MCD12Q1 products in China, trends in extreme precipitation, with the threshold defined as 95th (pre95p) and 99th (pre99p) percentiles of annual rain days during 1998-2015, have been assessed in China, and especially in 21 typical urban areas from 1998 to 2015. The tendency curves in extreme rainfall of different years are presented. In this period, more than 66% regions of China covered by TMPA 3B42 have increasing trends in extreme rainfall with pre95p threshold. The 21 typical urban areas showed different trends-in over half of these areas, upward tendencies in extreme rainfall were observed, particularly in Dalian, Beijing, and Chongqing. Seventeen urban areas showed increasing tendencies in pre95p extreme rainfall days, including Shanghai, Nanjing, Hangzhou, and Suzhou in the Yangtze River Delta region. The results also illustrate that southeastern coastal urban areas of China may have experienced decreasing occurrences in extreme rainfall.

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. This paper presents a classification method that coarsely classifies multiple objects of road infrastructure with a symmetric ensemble point (SEP) network and then refines the results with a Euclidean cluster extraction (ECE) algorithm. The SEP network applies a symmetric function to capture relevant structural features at different scales and select optimal sub-samples using an ensemble method. The ECE subsequently adjusts points that have been predicted incorrectly by the first step. The experimental results indicate that this method effectively extracts six types of road infrastructure elements: road surfaces, buildings, walls, traffic signs, trees and streetlights. The overall accuracy of the SEP-ECE method improves by 3.97% with respect to PointNet. The achieved average classification accuracy is approximately 99.74%, which is suitable for practical use in transportation network management.

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. On the other hand, remote-sensing techniques, such as terrestrial laser scanning, ground-based SAR (synthetic aperture radar) and satellite differential interferometric SAR offer the chance to extend the observed region to a large portion of a structure and its surrounding areas, integrating the information that is usually provided in a limited number of in-situ control points. The design and implementation of integrated monitoring systems have been revealed as a strategic solution to analyze different situations in a spatial and temporal context. Research devoted to the optimization of data processing tools has evolved with the aim of improving the accuracy and reliability of the measured deformations. The analysis of the observed data for the interpretation and prediction of dam deformations under external loads has been largely investigated on the basis of purely statistical or deterministic methods. The latter may integrate observation from geodetic, remote-sensing and geotechnical/structural sensors with mechanical models of the dam structure. In this paper, a review of the available technologies for dam deformation monitoring is provided, including those sensors that are already applied in routinary operations and some experimental solutions. The aim was to support people who are working in this field to have a complete view of existing solutions, as well as to understand future directions and trends.

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. In addition, the automation provided by the implemented software, the limited cost of the instruments and the possibility to operate with an indefinite number of points offer new and more extensive analysis in the field of material testing. Several comparisons with other traditional sensors widely adopted inside most laboratories were carried out in order to demonstrate the accuracy of the implemented software. Implementation details, simulations and real applications are reported and discussed in this paper.