16 research outputs found
Road Damage Detection Acquisition System based on Deep Neural Networks for Physical Asset Management
Research on damage detection of road surfaces has been an active area of
re-search, but most studies have focused so far on the detection of the
presence of damages. However, in real-world scenarios, road managers need to
clearly understand the type of damage and its extent in order to take effective
action in advance or to allocate the necessary resources. Moreover, currently
there are few uniform and openly available road damage datasets, leading to a
lack of a common benchmark for road damage detection. Such dataset could be
used in a great variety of applications; herein, it is intended to serve as the
acquisition component of a physical asset management tool which can aid
governments agencies for planning purposes, or by infrastructure mainte-nance
companies. In this paper, we make two contributions to address these issues.
First, we present a large-scale road damage dataset, which includes a more
balanced and representative set of damages. This dataset is composed of 18,034
road damage images captured with a smartphone, with 45,435 in-stances road
surface damages. Second, we trained different types of object detection
methods, both traditional (an LBP-cascaded classifier) and deep learning-based,
specifically, MobileNet and RetinaNet, which are amenable for embedded and
mobile and implementations with an acceptable perfor-mance for many
applications. We compare the accuracy and inference time of all these models
with others in the state of the art
“Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan, China
Improving remote sensing flood assessment using volunteered geographical data
A new methodology for the generation of flood hazard maps is presented fusing remote sensing and volunteered geographical data. Water pixels are identified utilizing a machine learning classification of two Landsat remote sensing scenes, acquired before and during the flooding event as well as a digital elevation model paired with river gage data. A statistical model computes the probability of flooded areas as a function of the number of adjacent pixels classified as water. Volunteered data obtained through Google news, videos and photos are added to modify the contour regions. It is shown that even a small amount of volunteered ground data can dramatically improve results
Road assessment after flood events using non-authoritative data
This research proposes a methodology that leverages non-authoritative data to
augment flood extent mapping and the evaluation of transportation
infrastructure. The novelty of this approach is the application of freely
available, non-authoritative data and its integration with established data
and methods. Crowdsourced photos and volunteered geographic data are fused
together using a geostatistical interpolation to create an estimation of
flood damage in New York City following Hurricane Sandy. This damage
assessment is utilized to augment an authoritative storm surge map as well as
to create a road damage map for the affected region
A Framework for Outdoor Mobile Augmented Reality and Its Application to Mountain Peak Detection
Outdoor augmented reality applications project information of interest onto views of the world in real-time. Their core challenge is recognizing the meaningful objects present in the current view and retrieving and overlaying pertinent information onto such objects. In this paper we report on the development of a framework for mobile outdoor augmented reality application, applied to the overlay of peak information onto views of mountain landscapes. The resulting app operates by estimating the virtual panorama visible from the viewpoint of the user, using an online Digital Terrain Model (DEM), and by matching such panorama to the actual image framed by the camera. When a good match is found, meta-data from the DEM (e.g., peak name, altitude, distance) are projected in real time onto the view. The application, besides providing a nice experience to the user, can be employed to crowdsource the collection of annotated mountain images for environmental applications
High Temperature Fire Experiment for TET and LANDSAT 8 in Test Site DEMMIN (Germany)
In 2012, the German Aerospace Center (DLR) launched the small satellite TET-1 (Experimental Technology Carrier) as a test platform for new satellite technologies and as a carrier for the Multi-Spectral Camera System (MSC) with five spectral bands (Green, Red, Near Infrared, Middle Infrared, and Thermal Infrared). The MSC has been designed to provide quantitative parameters (e.g. fire radiative power, burned area) observing high-temperature
events. The detection of such events provides information for operational support to fire brigades, to change detection of hotspots, to assess CO2 emissions of burning vegetation, and, finally, contributes to the monitoring programs that support climate models. In order to investigate the sensitivity and accuracy of the MSC system, a calibration and validation fire campaign was developed and executed, to derive characteristic signal changes of corresponding pixels in the MWIR and LWIR bands. The planning and execution of the validation campaign
and the results are presented