A three-dimensional future land use simulation (FLUS-3D) model for simulating the 3D urban dynamics under the shared socio-economic pathways
Landscape and Urban Planning,
Год журнала:
2024,
Номер
250, С. 105135 - 105135
Опубликована: Июнь 17, 2024
Язык: Английский
Assessing the space-use efficiency of French cities by coupling city volumes with mobile data traffic
Sustainable Cities and Society,
Год журнала:
2025,
Номер
unknown, С. 106292 - 106292
Опубликована: Март 1, 2025
Язык: Английский
Urbanization induced urban canopy parameters enhance the heatwave intensity: A case study of Beijing
Sustainable Cities and Society,
Год журнала:
2024,
Номер
unknown, С. 106089 - 106089
Опубликована: Дек. 1, 2024
Язык: Английский
Leveraging Machine Learning to Generate a Unified and Complete Building Height Dataset for Germany
Energy and AI,
Год журнала:
2024,
Номер
17, С. 100408 - 100408
Опубликована: Июль 31, 2024
Building
geometry
data
is
crucial
for
detailed,
spatially-explicit
analyses
of
the
building
stock
in
energy
systems
analysis
and
beyond.
Despite
existence
diverse
datasets
methods,
a
standardized
validated
approach
creating
nation-wide
unified
complete
dataset
German
heights
not
yet
available.
This
study
develops
validates
such
methodology,
combining
different
sources
footprints
filling
gaps
height
using
an
XGBoost
machine
learning
algorithm.
The
model
achieves
mean
absolute
error
1.78
m
at
national
level
between
1.52
3.47
federal
state
level.
goal
proving
applicability
methodology
large
scale
useful
dataset.
resulting
thoroughly
evaluated
on
building-by-building
spatially
resolved
statistics
quality
are
reported.
detailed
validation
found
that
number
footprint
area
90.31%
94.84%
correct,
respectively,
accuracy
0.59
However,
errors
homogeneous
across
Germany
further
research
needed
into
impact
including
additional
datasets,
especially
regions
types
with
lower
accuracies.
proves
chosen
generating
workflow,
some
modifications
regional
availability,
can
be
transferred
to
other
countries.
generated
constitutes
valuable
basis
community
fields
as
research,
urban
planning
decarbonization
policy
development.
Язык: Английский
Quantifying spatial patterns of urban building morphology in the China’s Guangdong-Hong Kong-Marco greater bay area
International Journal of Digital Earth,
Год журнала:
2024,
Номер
17(1)
Опубликована: Авг. 19, 2024
Understanding
the
spatial
patterns
of
urban
building
morphology
is
crucial
for
revealing
interplay
between
built
and
social
environments.
Previous
research
has
predominantly
concentrated
on
computation
building-level
metrics
which
makes
it
challenging
to
quantify
compare
variations
across
different
cities.
Using
newly
available
world
settlement
footprint
3D
(WSF3D)
data,
this
study
examines
various
cities
within
Guangdong-Hong
Kong-Macao
Greater
Bay
Area,
a
rapidly
urbanizing
region
in
China.
Specifically,
we
applied
concentric
ring
approach
delineate
gradients
fraction,
area,
height,
volume
from
center
suburban
fringes.
Subsequently,
utilized
dynamic
time
warping
multi-dimensional
scaling
technique
facilitating
comparative
analysis
these
Developed
demonstrated
more
homogenous
distributions
morphologies;
however,
notable
differences
were
observed
among
distinct
metrics.
Furthermore,
correlation
degree
development
was
revealed,
suggesting
that
developed
exhibit
significantly
smaller
declines
core
rural
periphery.
Язык: Английский