Urban Science,
Journal Year:
2025,
Volume and Issue:
9(2), P. 34 - 34
Published: Feb. 5, 2025
The
assessment
of
urban
heat
resilience
has
become
crucial
due
to
increasing
extreme
weather
events.
This
study
introduces
the
Running
Activity
Z-score
(RAZ)
index
based
on
running
activity
trajectory
data
evaluate
resilience.
Through
a
case
an
August
2022
heatwave
in
Beijing,
we
examined
index’s
sensitivity
and
explored
its
spatial
relationships
with
key
built
environment
factors,
including
plot
ratio,
green
coverage,
population
density,
blue
space
proximity.
Our
results
reveal
two
findings:
(1)
RAZ
serves
as
effective
real-time,
high-precision
indicator
impacts,
evidenced
by
extremely
low
values
consistently
coinciding
periods,
(2)
offers
valuable
insights
for
identifying
potential
areas
supporting
planning
decisions,
demonstrated
significant
correlations
factors
that
align
previous
studies
while
uncovering
more
detailed
relationships.
Although
effectively
complements
traditional
measurement
methods,
application
requires
careful
consideration
external
such
social
dynamics
climate
variability.
Humanities and Social Sciences Communications,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: April 27, 2024
Abstract
The
local
climate
zones
(LCZs)
classification
system
has
emerged
as
a
more
refined
method
for
assessing
the
urban
heat
island
(UHI)
effect.
However,
few
researchers
have
conducted
systematic
critical
reviews
and
summaries
of
research
on
LCZs,
particularly
regarding
significant
advancements
this
field
in
recent
years.
This
paper
aims
to
bridge
gap
scientific
by
systematically
reviewing
evolution,
current
status,
future
trends
LCZs
framework
research.
Additionally,
it
critically
assesses
impact
climate-responsive
planning
design.
findings
study
highlight
several
key
points.
First,
challenge
large-scale,
efficient,
accurate
mapping
persists
issue
Despite
challenge,
universality,
simplicity,
objectivity
make
promising
tool
wide
range
applications
future,
especially
realm
In
conclusion,
makes
substantial
contribution
advancement
advocates
broader
adoption
foster
sustainable
development.
Furthermore,
offers
valuable
insights
practitioners
engaged
field.
Abstract.
China
has
undergone
rapid
urbanization
and
internal
migration
in
past
years
its
up-to-date
gridded
population
datasets
are
essential
for
diverse
applications.
Existing
China,
however,
suffer
from
either
outdatedness
or
failure
to
incorporate
the
latest
seventh
national
census
data
conducted
2020.
In
this
study,
we
develop
a
novel
downscaling
approach
that
leverages
stacking
ensemble
learning
geospatial
big
produce
grids
at
100-m
resolution
both
county
town
levels.
The
proposed
employs
random
forest,
XGBoost,
LightGBM
as
base
models
delineates
inhabited
areas
enhance
estimation.
Experimental
results
demonstrate
exhibits
best
fit
performance
compared
individual
models.
Meanwhile,
out-of-sample
town-level
test
set
indicates
estimated
dataset
(R2=0.8936)
is
more
accurate
than
existing
WorldPop
(R2=0.7427)
LandScan
(R2=0.7165)
products
Furthermore,
with
enhancement,
spatial
distribution
of
reasonable
intuitively
two
products.
Hence,
provides
valuable
option
producing
datasets.
holds
great
significance
future
applications
it
publicly
available
https://figshare.com/s/d9dd5f9bb1a7f4fd3734
(Chen
et
al.,
2024).
Remote Sensing of Environment,
Journal Year:
2024,
Volume and Issue:
305, P. 114057 - 114057
Published: Feb. 27, 2024
Three-dimensional
(3D)
building
models
provide
horizontal
and
vertical
information
of
urban
development
patterns,
which
are
significant
to
urbanization
analysis,
solar
energy
planning,
carbon
reduction
sustainability.
Despite
that
many
popular
products
on
a
global
or
national
scale
proposed,
these
usually
focus
extraction
height
estimation
at
fairly
coarse
resolutions
while
categories
not
taken
into
consideration.
In
this
study,
we
extend
the
previous
work
in
two
aspects
involving
introduction
semantically
fine-grained
(i.e.,
12
rooftop
classes)
spatially
representations
individual
buildings
with
compact
polygons.
Specifically,
develop
novel
framework
for
generation
3D
models,
including
developing
network
joint
classification,
another
parallel
estimation,
post-processing
algorithm
fusion
results
from
independent
networks.
To
train
networks
improve
generalization,
construct
custom
large-scale
datasets
addition
existing
Urban
Building
Classification
(UBC)
dataset
2023
IEEE
Data
Fusion
Contest
(DFC
2023)
dataset.
Finally,
nation-scale
fine-GrAined
BuiLding
modEl
(GABLE)
product
is
derived
based
Beijing-3
satellite
images
(0.5–0.8
m)
our
proposed
framework.
GABLE
provides
polygon,
category
value
each
instance.
Further
analyses
conducted
uncover
distribution
terms
diversity,
density.
These
demonstrate
significance
values
GALBE,
potentials
far
beyond
these.
Earth system science data,
Journal Year:
2024,
Volume and Issue:
16(11), P. 5357 - 5374
Published: Nov. 25, 2024
Abstract.
Understanding
urban
vertical
structures,
particularly
building
heights,
is
essential
for
examining
the
intricate
interaction
between
humans
and
their
environment.
Such
datasets
are
indispensable
a
variety
of
applications,
including
climate
modeling,
energy
consumption
analysis,
socioeconomic
activities.
Despite
importance
this
information,
previous
studies
have
primarily
focused
on
estimating
heights
regionally
at
grid
scale,
often
resulting
in
with
limited
coverage
or
spatial
resolution.
This
limitation
hampers
comprehensive
global
analysis
ability
to
generate
actionable
insights
finer
scales.
In
study,
we
developed
height
map
footprint
scale
by
leveraging
Earth
Observation
(EO)
advanced
machine
learning
techniques.
Our
approach
integrated
multisource
remote-sensing
features
morphology
develop
estimation
models
using
extreme
gradient
boosting
(XGBoost)
regression
method
across
diverse
regions.
methodology
allowed
us
estimate
individual
buildings
worldwide,
culminating
creation
three-dimensional
(3D)
Global
Building
Footprints
(3D-GloBFP)
dataset
year
2020.
evaluation
results
show
that
perform
exceptionally
well
R2
values
ranging
from
0.66
0.96
root-mean-square
errors
(RMSEs)
1.9
14.6
m
33
subregions.
Comparisons
other
demonstrate
3D-GloBFP
closely
matches
distribution
pattern
reference
heights.
derived
3D
shows
distinct
regions,
countries,
cities,
gradually
decreasing
city
center
surrounding
rural
areas.
Furthermore,
our
findings
indicate
disparities
built-up
infrastructure
(i.e.,
volume)
different
countries
cities.
China
country
most
intensive
total
(5.28×1011
m3,
accounting
23.9
%
total),
followed
USA
(3.90×1011
17.6
total).
Shanghai
has
largest
volume
(2.1×1010
m3)
all
representative
The
building-footprint-scale
reveals
significant
heterogeneity
environments,
providing
valuable
dynamics
climatology.
available
https://doi.org/10.5281/zenodo.11319912
(Building
Americas,
Africa,
Oceania
3D-GloBFP;
Che
et
al.,
2024c),
https://doi.org/10.5281/zenodo.11397014
Asia
2024a),
https://doi.org/10.5281/zenodo.11391076
Europe
2024b).
