Exploring the Impact of Waterfront Street Environments on Human Perception
Buildings,
Год журнала:
2025,
Номер
15(10), С. 1678 - 1678
Опубликована: Май 16, 2025
Urban
waterfront
streets
are
important
mediators
that
reflect
a
city’s
image
and
characteristics.
They
play
positive
role
in
enhancing
residents’
cohesion,
mental
physical
health,
social
interactions.
Human
perceptions
represent
individuals’
psychological
experiences
feelings
toward
the
surrounding
environment.
Previous
studies
have
explored
impact
of
urban
street-built
environmental
factors
on
perceptions;
however,
research
focusing
street
environments
their
impacts
human
remains
limited.
Therefore,
exploring
specific
characteristics
different
dimensions
perception
is
essential
for
guiding
development
livable
cities.
Based
Street
View
images
(SVIs),
this
study
applied
artificial
neural
networks
machine
learning
semantic
segmentation
techniques
to
obtain
feature
data
Murasaki
River
line
spaces
Kitakyushu,
Japan.
In
addition,
correlation
regression
analyses
were
conducted
explore
features
spaces,
corresponding
optimization
strategies
proposed.
The
results
show
greenness
significantly
enhances
safety,
wealth,
beauty,
while
effectively
reducing
boredom
depression.
Furthermore,
building
visual
ratio
contributes
increased
vitality.
On
other
hand,
such
as
openness,
spatial
indicators,
color
diversity
negative
effects
perceptions,
including
safety
particular,
openness
increases
This
advances
exploration
from
perspective
perception,
providing
theoretical
foundation
improving
quality
offering
references
human-centered
planning
construction.
Язык: Английский
Evaluation of the Visual Perception of Urban Single/Double-Layer Riverfront Greenway Landscapes Based on Deep Learning
Sustainability,
Год журнала:
2024,
Номер
16(23), С. 10391 - 10391
Опубликована: Ноя. 27, 2024
Urban
inland
rivers
are
closely
related
to
urban
development,
but
high-density
urbanisation
has
reduced
the
natural
function
of
streams
and
riverbanks
hardened
into
two
parts,
embankment
walls
berms,
which
give
rise
a
variety
riparian
landscapes.
However,
difference
in
height
walkways
affects
degree
their
greening
landscape
effects.
In
this
paper,
we
studied
single-
double-decker
greenways,
constructed
quantitative
indicators
spatial
elements
based
on
deep
learning
algorithms
using
an
image
semantic
segmentation
(ISS)
model
that
simulates
human
visual
perception,
used
random
forests
multivariate
linear
regression
models
study
impact
riverfront
greenway
clarified
differences
caused
by
different
types
space
aesthetic
preferences
(LP)
confirmed
specific
extent
components
influence
preferences.
The
results
showed
there
were
significant
perception
scores
between
single
double
layers.
(1)
WED
(negative
correlation)
NI
(positive
is
large
single-layer
greenway.
colour,
material
structure
guardrail
can
be
beautified
diversified
quality
greenery
taken
account
maintain
visibility
order
improve
score
(2)
BVI
double-layered
positive.
Water-friendly
or
water-viewing
spaces
added
appropriately
greenways.
This
applicable
regional
feature
identification
greenways
large-scale
hard
barge
bank
images,
realises
whole-region
perspective
effective
expansion
analysis
techniques
for
sustainable
planning
design
Язык: Английский