Measuring
and
predicting
Carbon
Emission
(CE)
is
important
to
enabling
the
main
culprit
of
various
urgent
environmental
issues
including
global
warming.
However,
prior
studies
did
not
fully
incorporate
impact
micro-level
urban
streetscapes,
which
might
lead
biased
prediction
CE.
To
fill
gap,
we
developed
an
effective
framework
predict
residential
CE
in
areas
from
widely
existing
publicly
available
street-view
images
(SVI)
using
machine
learning.
First,
used
a
semantic
segmentation
algorithm
classify
more
than
30
streetscape
elements
SVI
describe
built
environment
whose
features
affect
transportation
Second,
based
on
streetscapes
quantified,
trained
10-fold
cross-validation
method
with
learning
models
at
1KM
grid
level
data
PlanetData.
We
found
first,
such
as
sidewalks,
roads,
fences,
buildings,
walls
are
significantly
correlated
presence
buildings
subtle
(e.g.,
walls,
fences)
indicates
higher-density
related
Third,
vegetation
trees
grass)
reversely
Our
findings
shed
light
feasibility
single
open
source
(i.e.,
SVI)
effectively
model
neighborhood-level
for
regions
across
diverse
forms.
useful
planners
inform
new
town
development
regeneration
towards
low
goals.
Environmental Science and Ecotechnology,
Journal Year:
2024,
Volume and Issue:
20, P. 100433 - 100433
Published: May 17, 2024
In
the
dynamic
landscape
of
sustainable
smart
cities,
emerging
computational
technologies
and
models
are
reshaping
data-driven
planning
strategies,
practices,
approaches,
paving
way
for
attaining
environmental
sustainability
goals.
This
transformative
wave
signals
a
fundamental
shift
—
marked
by
synergistic
operation
artificial
intelligence
(AI),
things
(AIoT),
urban
digital
twin
(UDT)
technologies.
While
previous
research
has
largely
explored
AI,
AIoT,
UDT
in
isolation,
significant
knowledge
gap
exists
regarding
their
interplay,
collaborative
integration,
collective
impact
on
context
cities.
To
address
this
gap,
study
conducts
comprehensive
systematic
review
to
uncover
intricate
interactions
among
these
interconnected
technologies,
models,
domains
while
elucidating
nuanced
dynamics
untapped
synergies
complex
ecosystem
Central
four
guiding
questions:
What
theoretical
practical
foundations
underpin
convergence
UDT,
planning,
how
can
components
be
synthesized
into
novel
framework?
How
does
integrating
AI
AIoT
reshape
improve
performance
cities?
augment
capabilities
enhance
processes
challenges
barriers
arise
implementing
what
strategies
devised
surmount
or
mitigate
them?
Methodologically,
involves
rigorous
analysis
synthesis
studies
published
between
January
2019
December
2023,
comprising
an
extensive
body
literature
totaling
185
studies.
The
findings
surpass
mere
interdisciplinary
enrichment,
offering
valuable
insights
potential
advance
development
practices.
By
enhancing
processes,
integrated
offer
innovative
solutions
challenges.
However,
endeavor
is
fraught
with
formidable
complexities
that
require
careful
navigation
mitigation
achieve
desired
outcomes.
serves
as
reference
guide,
spurring
groundbreaking
endeavors,
stimulating
implementations,
informing
strategic
initiatives,
shaping
policy
formulations
sustainable,
development.
These
have
profound
implications
researchers,
practitioners,
policymakers,
providing
roadmap
fostering
resiliently
designed,
technologically
advanced,
environmentally
conscious
environments.
Annals of the American Association of Geographers,
Journal Year:
2024,
Volume and Issue:
114(5), P. 876 - 897
Published: April 8, 2024
The
visual
dimension
of
cities
has
been
a
fundamental
subject
in
urban
studies
since
the
pioneering
work
late-nineteenth-
to
mid-twentieth-century
scholars
such
as
Camillo
Sitte,
Kevin
Lynch,
Rudolf
Arnheim,
and
Jane
Jacobs.
Several
decades
later,
big
data
artificial
intelligence
(AI)
are
revolutionizing
how
people
move,
sense,
interact
with
cities.
This
article
reviews
literature
on
appearance
function
illustrate
information
used
understand
them.
A
conceptual
framework,
intelligence,
is
introduced
systematically
elaborate
new
image
sources
AI
techniques
reshaping
way
researchers
perceive
measure
cities,
enabling
study
physical
environment
its
interactions
socioeconomic
at
various
scales.
argues
that
these
approaches
would
allow
revisit
classic
theories
themes
potentially
help
create
environments
align
human
behaviors
aspirations
today's
AI-driven
data-centric
era.
International Journal of Applied Earth Observation and Geoinformation,
Journal Year:
2023,
Volume and Issue:
122, P. 103385 - 103385
Published: June 17, 2023
Street
View
Imagery
(SVI)
is
crucial
in
estimating
indicators
such
as
Sky
Factor
(SVF)
and
Green
Index
(GVI),
but
(1)
approaches
terminology
differ
across
fields
planning,
transportation
climate,
potentially
causing
inconsistencies;
(2)
it
unknown
whether
the
regularly
used
panoramic
imagery
actually
essential
for
tasks,
or
we
can
use
only
a
portion
of
imagery,
simplifying
process;
(3)
do
not
know
if
non-panoramic
(single-frame)
photos
typical
crowdsourced
platforms
serve
same
purposes
ones
from
services
Google
Baidu
Maps
their
limited
perspectives.
This
study
first
to
examine
comprehensively
built
form
metrics,
influence
different
practices
on
computing
them
multiple
fields,
usability
normal
(from
consumer
cameras).
We
overview
run
experiments
70
million
images
5
cities
analyse
impact
multitude
variants
SVI
characterising
physical
environment
mapping
street
canyons:
few
(e.g.
fisheye)
96
scenarios
perspective
with
variable
directions,
view,
aspect
ratios
mirroring
diverse
smartphones
dashcams.
demonstrate
that
disparate
give
mostly
comparable
results
metric
R=0.82
R=0.98
metrics);
often
when
using
front-facing
ultrawide
camera),
single-frame
derive
commercial
counterparts.
finding
may
simplify
processes
data
also
unlock
value
billions
images,
which
are
overlooked,
benefit
scores
locations
worldwide
yet
covered
by
services.
Further,
aggregated
city-scale
analyses,
correspond
closely.
Sustainable Cities and Society,
Journal Year:
2023,
Volume and Issue:
100, P. 105047 - 105047
Published: Nov. 8, 2023
Computer
vision
(CV)
technology,
a
key
subset
of
artificial
intelligence,
provides
powerful
tools
for
extracting
valuable
insights
from
visual
data,
which
is
crucial
component
the
urban
planning
process.
Despite
promising
potential
CV
in
planning,
its
applications
this
context
have
not
been
thoroughly
examined.
This
lack
scholarship
represents
critical
knowledge
gap
our
understanding
role
planning.
paper
aims
to
provide
consolidated
process
and
challenges
planners
face
during
adoption
CV.
The
conducts
systematic
literature
review
tackle
questions
how
applied
process,
what
are
adopting
techniques
process?
findings
revealed:
(a)
could
support
broad
range
tasks
including
data
collection
analysis,
issue
identification
prioritisation,
public
participation,
plan
design
adoption,
implementation
evaluation;
(b)
improve
decision-making
through
various
information,
but
limitations
need
be
considered,
and;
(c)
Utilisation
efforts
sustainable
development.
study
informs
policy-
plan-making
circles
by
providing
into
existing
prospective
contributions
transforms
augments
practices,
elaborates
adoption.
