Land,
Год журнала:
2024,
Номер
13(11), С. 1735 - 1735
Опубликована: Окт. 23, 2024
Understanding
and
recognizing
urban
morphology
evolution
is
a
crucial
issue
in
planning,
with
extensive
research
dedicated
to
detecting
the
extent
of
expansion.
However,
as
development
patterns
shift
from
incremental
expansion
stock
optimization,
related
studies
on
meso-
microscale
face
limitations
such
insufficient
spatiotemporal
data
granularity,
poor
generalizability,
inability
extract
internal
patterns.
This
study
employs
deep
learning
meso-/microscopic
form
indicators
develop
generic
framework
for
extracting
describing
meso-/microscale
morphology.
The
includes
three
steps:
constructing
specific
datasets,
semantic
segmentation
form,
mapping
using
Tile-based
Urban
Change
(TUC)
classification
system.
We
applied
this
conduct
combined
quantitative
qualitative
analysis
Binhai
New
Area
2009
2022,
detailed
visualizations
at
each
time
point.
identified
that
different
locations
area
exhibited
seven
distinct
patterns:
edge
areal
expansion,
preservation
developmental
potential,
industrial
land
pattern,
rapid
comprehensive
demolition
construction
linear
mixed
evolution,
stable
evolution.
results
indicate
phase,
high-density
areas
exhibit
multidimensional
characteristics
by
region,
period,
function.
Our
work
demonstrates
potential
grid
providing
scalable,
cost-effective,
quantitative,
portable
approach
historical
understanding.
Advances in public policy and administration (APPA) book series,
Год журнала:
2024,
Номер
unknown, С. 347 - 372
Опубликована: Дек. 5, 2024
This
chapter
explores
the
transformative
role
of
Artificial
Intelligence
(AI)
in
enhancing
design,
implementation,
and
management
Blue-Green
Infrastructure
(BGI),
a
sustainable
urban
planning
approach
that
integrates
natural
engineered
systems
to
address
environmental
challenges.
The
convergence
AI
with
BGI
offers
unprecedented
opportunities
improve
resilience,
optimize
resource
management,
mitigate
impacts
climate
change.
Through
advanced
data
analytics,
predictive
modeling,
real-time
monitoring,
AI-driven
solutions
can
enhance
efficiency
effectiveness
projects.
delves
into
various
applications
BGI,
including
smart
water
flood
prediction
prevention,
heat
island
mitigation,
biodiversity
conservation.
Case
studies
examples
from
global
cities
illustrate
how
is
being
leveraged
create
more
adaptive,
sustainable,
resilient
environments.
also
discusses
challenges
ethical
considerations
associated
integration
emphasizing
need
for
interdisciplinary
collaboration
responsible
deployment
ensure
equitable
long-term
benefits.
Sustainable Cities and Society,
Год журнала:
2024,
Номер
112, С. 105597 - 105597
Опубликована: Июнь 20, 2024
Climate
changes
have
led
to
increasing
global
energy
consumption,
detrimental
the
sustainable
development
of
society.
Urban
blue-green
infrastructure
(UBGI)
can
improve
urban
microclimate.
However,
influence
intensity
UBGI
on
microclimate
has
not
been
quantified
deeply
use
efficiency
water
and
greenery
resources.
To
solve
research
deficiencies,
this
study
numerically
simulated
for
44
scenarios
with
different
resource
configurations
(various
body
areas
coverages)
in
summer.
Based
simulations,
developed
novel
mathematical
models
thermo-environment
(BGTE)
quantify
UBGI.
The
results
indicated
that
daytime
synergies
first
increased
then
decreased
time.
significance
time
(t),
area
(Sw),
tree
coverage
rate
(TCR),
shrub
(SCR),
grassland
(GLCR)
synergy
was
by
artificial
neural
network:
t
(39.4%),
Sw
(22.6%),
TCR
(22.0%),
SCR
(13.2%),
GLCR
(2.8%).
make
overall
effect
relatively
efficient,
should
be
less
than
10000
m2,
greater
65%,
close
15%.
This
provides
practical
ideas
efficient
