6,966 research outputs found
Snow Covered Pedestrian Crosswalk Enhancement Via Projected Light Demarcation
Presented to the Faculty
of the University of Alaska Anchorage
in Partial Fulfilment of the Requirements
for the Degree of
MASTER OF SCIENCESnow coverage of streets in Anchorage, Alaska, can visually block pedestrians and
drivers from viewing painted crosswalk demarcations. This study investigates the
potential of utilizing light projected onto the snow’s surface to mimic the intended
demarcation of the painted demarcation during snow coverage.
This is investigated via hypothetically fitting an existing crosswalk location with
available-for-purchase manufactured light projectors. The configuration is then
evaluated for angle of light projection, discomfort glare, and contrast.
The proposed installation is found to be theoretically acceptable. However, further
analysis could be performed regarding effective visual detection of contrast during
driving conditions and regarding acceptable levels of disability glare.Signature Page / Title Page / Abstract / Table of Contents / List of Figures / List of Tables / Introduction / Terms and Definitions / Existing Dimensions / Proposed Projector Installation / Light Analysis / Conclusion / Reference
RadarSLAM: Radar based Large-Scale SLAM in All Weathers
Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been
presented in last decade using different sensor modalities. However, robust
SLAM in extreme weather conditions is still an open research problem. In this
paper, RadarSLAM, a full radar based graph SLAM system, is proposed for
reliable localization and mapping in large-scale environments. It is composed
of pose tracking, local mapping, loop closure detection and pose graph
optimization, enhanced by novel feature matching and probabilistic point cloud
generation on radar images. Extensive experiments are conducted on a public
radar dataset and several self-collected radar sequences, demonstrating the
state-of-the-art reliability and localization accuracy in various adverse
weather conditions, such as dark night, dense fog and heavy snowfall
Dominance of grain size impacts on seasonal snow albedo at deforested sites in New Hampshire
Snow cover serves as a major control on the surface energy budget in temperate regions due to its high reflectivity compared to underlying surfaces. Winter in the northeastern United States has changed over the last several decades, resulting in shallower snowpacks, fewer days of snow cover, and increasing precipitation falling as rain in the winter. As these climatic changes occur, it is imperative that we understand current controls on the evolution of seasonal snow albedo in the region. Over three winter seasons between 2013 and 2015, snow characterization measurements were made at three open sites across New Hampshire. These near-daily measurements include spectral albedo, snow optical grain size determined through contact spectroscopy, snow depth, snow density, black carbon content, local meteorological parameters, and analysis of storm trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory model. Using analysis of variance, we determine that land-based winter storms result in marginally higher albedo than coastal storms or storms from the Atlantic Ocean. Through multiple regression analysis, we determine that snow grain size is significantly more important in albedo reduction than black carbon content or snow density. And finally, we present a parameterization of albedo based on days since snowfall and temperature that accounts for 52% of variance in albedo over all three sites and years. Our improved understanding of current controls on snow albedo in the region will allow for better assessment of potential response of seasonal snow albedo and snow cover to changing climate
Dominance of grain size impacts on seasonal snow albedo at deforested sites in New Hampshire
Snow cover serves as a major control on the surface energy budget in temperate regions due to its high reflectivity compared to underlying surfaces. Winter in the northeastern United States has changed over the last several decades, resulting in shallower snowpacks, fewer days of snow cover, and increasing precipitation falling as rain in the winter. As these climatic changes occur, it is imperative that we understand current controls on the evolution of seasonal snow albedo in the region. Over three winter seasons between 2013 and 2015, snow characterization measurements were made at three open sites across New Hampshire. These near-daily measurements include spectral albedo, snow optical grain size determined through contact spectroscopy, snow depth, snow density, black carbon content, local meteorological parameters, and analysis of storm trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory model. Using analysis of variance, we determine that land-based winter storms result in marginally higher albedo than coastal storms or storms from the Atlantic Ocean. Through multiple regression analysis, we determine that snow grain size is significantly more important in albedo reduction than black carbon content or snow density. And finally, we present a parameterization of albedo based on days since snowfall and temperature that accounts for 52% of variance in albedo over all three sites and years. Our improved understanding of current controls on snow albedo in the region will allow for better assessment of potential response of seasonal snow albedo and snow cover to changing climate
Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons
We hypothesize that the strong sensitivity of radar backscatter to surface dielectric properties, and hence to the phase (solid or liquid) of any water near the surface should make space-borne radar observations a powerful tool for large-scale spatial monitoring of the freeze/thaw state of the land surface, and thus ecosystem growing season length. We analyzed the NASA scatterometer (NSCAT) backscatter from September 1996 to June 1997, along with temperature and snow depth observations and ecosystem modeling, for three BOREAS sites in central Canada. Because of its short wavelength (2.14 cm), NSCAT was sensitive to canopy and surface water. NSCAT had 25 km spatial resolution and approximately twice-daily temporal coverage at the BOREAS latitude. At the northern site the NSCAT signal showed strong seasonality, with backscatter around −8 dB in winter and −12 dB in early summer and fall. The NSCAT signal for the southern sites had less seasonality. At all three sites there was a strong decrease in backscatter during spring thaw (4–6 dB). At the southern deciduous site, NSCAT backscatter rose from −11 to −9.2 dB during spring leaf-out. All sites showed 1–2 dB backscatter shifts corresponding to changes in landscape water state coincident with brief midwinter thaws, snowfall, and extreme cold (Tmax\u3c−25°C). Freeze/thaw detection algorithms developed for other radar instruments gave reasonable results for the northern site but were not successful at the two southern sites. We developed a change detection algorithm based on first differences of 5-day smoothed NSCAT backscatter measurements. This algorithm had some success in identifying the arrival of freezing conditions in the autumn and the beginning of thaw in the spring. Changes in surface freeze/thaw state generally coincided with the arrival and departure of the seasonal snow cover and with simulated shifts in the directions of net carbon exchange at each of the study sites
A summer time series of particulate carbon in the air and snow at Summit, Greenland
Carbonaceous particulate matter is ubiquitous in the lower atmosphere, produced by natural and anthropogenic sources and transported to distant regions, including the pristine and climate-sensitive Greenland Ice Sheet. During the summer of 2006, ambient particulate carbonaceous compounds were characterized on the Greenland Ice Sheet, including the measurement of particulate organic (OC) and elemental (EC) carbon, particulate water-soluble organic carbon (WSOC), particulate absorption coefficient (σap), and particle size-resolved number concentration (PM0.1–1.0). Additionally, parallel ∼50-day time series of water-soluble organic carbon (WSOC), water-insoluble organic carbon (WIOC), and elemental carbon (EC) were quantified at time increments of 4–24 h in the surface snow. Measurement of atmospheric particulate carbon found WSOC (average of 52 ng m−3) to constitute a major fraction of particulate OC (average of 56 ng m−3), suggesting that atmospheric organic compounds reaching the Greenland Ice Sheet in summer are highly oxidized. Atmospheric EC (average of 7 ng m−3) was well-correlated with σap (r = 0.95) and the calculated mass-absorption cross-section (average of 24 m2 g−1) appears to be similar to that measured using identical techniques in an urban environment in the United States. Comparing surface snow to atmospheric particulate matter concentrations, it appears the snow has a much higher OC (WSOC+WIOC) to EC ratio (205:1) than air (10:1), suggesting that snow is additionally influenced by water-soluble gas-phase compounds. Finally, the higher-frequency (every 4–6 h) sampling of snow-phase WSOC revealed significant loss (40–54%) of related organic compounds in surface snow within 8 h of wet deposition
Rain Removal in Traffic Surveillance: Does it Matter?
Varying weather conditions, including rainfall and snowfall, are generally
regarded as a challenge for computer vision algorithms. One proposed solution
to the challenges induced by rain and snowfall is to artificially remove the
rain from images or video using rain removal algorithms. It is the promise of
these algorithms that the rain-removed image frames will improve the
performance of subsequent segmentation and tracking algorithms. However, rain
removal algorithms are typically evaluated on their ability to remove synthetic
rain on a small subset of images. Currently, their behavior is unknown on
real-world videos when integrated with a typical computer vision pipeline. In
this paper, we review the existing rain removal algorithms and propose a new
dataset that consists of 22 traffic surveillance sequences under a broad
variety of weather conditions that all include either rain or snowfall. We
propose a new evaluation protocol that evaluates the rain removal algorithms on
their ability to improve the performance of subsequent segmentation, instance
segmentation, and feature tracking algorithms under rain and snow. If
successful, the de-rained frames of a rain removal algorithm should improve
segmentation performance and increase the number of accurately tracked
features. The results show that a recent single-frame-based rain removal
algorithm increases the segmentation performance by 19.7% on our proposed
dataset, but it eventually decreases the feature tracking performance and
showed mixed results with recent instance segmentation methods. However, the
best video-based rain removal algorithm improves the feature tracking accuracy
by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System
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