3,808 research outputs found
Performance Measures to Assess Resiliency and Efficiency of Transit Systems
Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service.
This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster
Review on Active and Passive Remote Sensing Techniques for Road Extraction
Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe
A local Gaussian filter and adaptive morphology as tools for completing partially discontinuous curves
This paper presents a method for extraction and analysis of curve--type
structures which consist of disconnected components. Such structures are found
in electron--microscopy (EM) images of metal nanograins, which are widely used
in the field of nanosensor technology.
The topography of metal nanograins in compound nanomaterials is crucial to
nanosensor characteristics. The method of completing such templates consists of
three steps. In the first step, a local Gaussian filter is used with different
weights for each neighborhood. In the second step, an adaptive morphology
operation is applied to detect the endpoints of curve segments and connect
them. In the last step, pruning is employed to extract a curve which optimally
fits the template
Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery
Bridge detection in remote sensing images (RSIs) plays a crucial role in
various applications, but it poses unique challenges compared to the detection
of other objects. In RSIs, bridges exhibit considerable variations in terms of
their spatial scales and aspect ratios. Therefore, to ensure the visibility and
integrity of bridges, it is essential to perform holistic bridge detection in
large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with
large-size VHR RSIs limits the deep learning algorithms' performance on bridge
detection. Due to the limitation of GPU memory in tackling large-size images,
deep learning-based object detection methods commonly adopt the cropping
strategy, which inevitably results in label fragmentation and discontinuous
prediction. To ameliorate the scarcity of datasets, this paper proposes a
large-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled from
diverse geographic locations across the globe. These images encompass a wide
range of sizes, varying from 2,048*2,048 to 16,38*16,384 pixels, and
collectively feature 59,737 bridges. Furthermore, we present an efficient
network for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Net
presents a separate detector-based feature fusion (SDFF) architecture and is
optimized via a shape-sensitive sample re-weighting (SSRW) strategy. Based on
the proposed GLH-Bridge dataset, we establish a bridge detection benchmark
including the OBB and HBB tasks, and validate the effectiveness of the proposed
HBD-Net. Additionally, cross-dataset generalization experiments on two publicly
available datasets illustrate the strong generalization capability of the
GLH-Bridge dataset.Comment: 16 pages, 11 figures, 6 tables; due to the limitation "The abstract
field cannot be longer than 1,920 characters", the abstract appearing here is
slightly shorter than that in the PDF fil
Breaking new ground in mapping human settlements from space -The Global Urban Footprint-
Today 7.2 billion people inhabit the Earth and by 2050 this number will have
risen to around nine billion, of which about 70 percent will be living in
cities. Hence, it is essential to understand drivers, dynamics, and impacts of
the human settlements development. A key component in this context is the
availability of an up-to-date and spatially consistent map of the location and
distribution of human settlements. It is here that the Global Urban Footprint
(GUF) raster map can make a valuable contribution. The new global GUF binary
settlement mask shows a so far unprecedented spatial resolution of 0.4 arcsec
() that provides - for the first time - a complete picture of the
entirety of urban and rural settlements. The GUF has been derived by means of a
fully automated processing framework - the Urban Footprint Processor (UFP) -
that was used to analyze a global coverage of more than 180,000 TanDEM-X and
TerraSAR-X radar images with 3m ground resolution collected in 2011-2012.
Various quality assessment studies to determine the absolute GUF accuracy based
on ground truth data on the one hand and the relative accuracies compared to
established settlements maps on the other hand, clearly indicate the added
value of the new global GUF layer, in particular with respect to the
representation of rural settlement patterns. Generally, the GUF layer achieves
an overall absolute accuracy of about 85\%, with observed minima around 65\%
and maxima around 98 \%. The GUF will be provided open and free for any
scientific use in the full resolution and for any non-profit (but also
non-scientific) use in a generalized version of 2.8 arcsec ().
Therewith, the new GUF layer can be expected to break new ground with respect
to the analysis of global urbanization and peri-urbanization patterns,
population estimation or vulnerability assessment
Rapid post-disaster infrastructure damage characterisation enabled by remote sensing and deep learning technologies -- a tiered approach
Critical infrastructure, such as transport networks and bridges, are systematically targeted during wars and suffer damage during extensive natural disasters because it is vital for enabling connectivity and transportation of people and goods, and hence, underpins national and international economic growth. Mass destruction of transport assets, in conjunction with minimal or no accessibility in the wake of natural and anthropogenic disasters, prevents us from delivering rapid recovery and adaptation. As a result, systemic operability is drastically reduced, leading to low levels of resilience. Thus, there is a need for rapid assessment of its condition to allow for informed decision-making for restoration prioritisation. A solution to this challenge is to use technology that enables stand-off observations. Nevertheless, no methods exist for automated characterisation of damage at multiple scales, i.e. regional (e.g., network), asset (e.g., bridges), and structural (e.g., road pavement) scales. We propose a methodology based on an integrated, multi-scale tiered approach to fill this capability gap. In doing so, we demonstrate how automated damage characterisation can be enabled by fit-for-purpose digital technologies. Next, the methodology is applied and validated to a case study in Ukraine that includes 17 bridges, damaged by human targeted interventions. From regional to component scale, we deploy technology to integrate assessments using Sentinel-1 SAR images, crowdsourced information, and high-resolution images for deep learning to facilitate automatic damage detection and characterisation. For the first time, the interferometric coherence difference and semantic segmentation of images were deployed in a tiered multi-scale approach to improve the reliability of damage characterisations at different scales
Analysis of Multiple Scattering Characteristics of Cable-Stayed Bridges with Multi-band SAR
Accurate localization of multi-scattering features of cable-stayed bridges in multi-band Synthetic Aperture Radar (SAR) imagery is crucial for intelligent recognition of bridge targets within images, as well as for precise water level extraction. This study focuses on the Badong Yangtze River Bridge, utilizing Unmanned Aerial Vehicle (UAV) LiDAR data of the bridge, and analyzes the multi-scattering characteristics of different bridge structural targets based on Geometric Optics (GO) methods and the Range-Doppler principle. Furthermore, the study integrates LiDAR data of the bridge's cable-stays to examine their multi-scattering phenomena, finding that the undulations of the Yangtze River's surface waves significantly contribute to the pronounced double scattering features of the bridge's cable-stays. Additionally, statistical analysis of multi-source SAR data indicates that this phenomenon is not directly correlated with radar wavelength, implying no direct connection to surface roughness. Utilizing LiDAR point cloud data from the bridge's street lamps, this paper proposes a novel method for estimating water level elevation by identifying the center position of spots formed by double scattering from lamp posts. The results show that using TerraSAR ascending and descending orbit images, this method achieves a water level elevation accuracy of approximately 0.2 meters
Target Detection: Remote Sensing Techniques for Defence Applications
The tremendous development in remote sensing technology in the recent past has opened up new challenges in defence applications. On important area of such applications is in target detection. This paper describes both classical and newly developed approaches to detect the targets by using remotely-sensed digital images. The classical approach includes statistical classification methods and image processing techniques. The new approach deals with a relatively new sensor technology, namely, synthetic aperture radar (SAR) systems and fast developing tools, like neural networks and multisource data integration for analysis and interpretation. With SAR images, it is possible to detect targets or features of a target that is otherwise not possible. Neural networks and multisource data integration tools also have a great potential in analysing and interpreting remote sensing data for target detection
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