International Journal of Digital Earth,
Journal Year:
2024,
Volume and Issue:
17(1)
Published: Aug. 13, 2024
Recent
studies
on
the
accessibility
of
sports
facilities
have
rarely
considered
specific
attributes
facilities,
limiting
their
ability
to
define
service
potential,
and
often
neglected
critical
aspect
equitable
access.
This
study
proposed
a
novel
approach
based
remote
sensing
images
optimize
spatial
outdoor
facilities.
Using
Shanghai,
China,
as
area,
identified
four
types
using
deep
learning
object
detection
method,
which
allowed
capacities
(areas)
be
measured
more
precisely.
A
greedy
heuristic
algorithm
was
then
developed
"trade-off"
strategy
that
seeks
facility
access
by
reconciling
objectives
enhancing
ensuring
equality
weighing
benefits
utilizing
existing
resources
(school
facilities)
against
necessity
developing
new
ones.
The
method
achieved
precision
recall
rates
88%
96%,
respectively,
optimization
efforts
resulted
in
73%
increase
while
also
significantly
reducing
Gini
coefficient
from
0.58
0.34.
outperformed
random
selection
all-school-opening
strategies.
results
indicated
methodology
can
effectively
create
refined
datasets
for
enhance
accessibility.
Atmosphere,
Journal Year:
2025,
Volume and Issue:
16(1), P. 40 - 40
Published: Jan. 2, 2025
In
order
to
assess
the
spatial
and
temporal
characteristics
of
urban
thermal
environment
in
Zhengzhou
City
supplement
climate
adaptation
design
work,
based
on
Landsat
8–9
OLI/TIRS
C2
L2
data
for
12
periods
from
2019–2023,
combined
with
lLocal
zone
(LCZ)
classification
subsurface
classification,
this
study,
we
used
statistical
mono-window
(SMW)
algorithm
invert
land
surface
temperature
(LST)
classify
heat
island
(UHI)
effect,
analyze
differences
distribution
environments
areas
aggregation
characteristics,
explore
influence
LCZ
landscape
pattern
temperature.
The
results
show
that
proportions
built
natural
types
Zhengzhou’s
main
metropolitan
area
are
79.23%
21.77%,
respectively.
most
common
landscapes
wide
mid-rise
(LCZ
5)
structures
large-ground-floor
8)
structures,
which
make
up
21.92%
20.04%
study
area’s
total
area,
varies
seasons,
pooling
during
summer
peaking
winter,
strong
or
extremely
islands
centered
suburbs
a
hot
cold
spots
aggregated
observable
features.
As
building
heights
increase,
UHI
1–6)
increases
then
reduces
spring,
summer,
autumn
decreases
winter
as
increase.
Water
bodies
G)
dense
woods
A)
have
lowest
effects
among
settings.
Building
size
is
no
longer
primary
element
affecting
LST
buildings
become
taller;
instead,
connectivity
clustering
take
center
stage.
Seasonal
variations,
variations
types,
responsible
area.
should
see
an
increase
vegetation
cover,
gaps
must
be
appropriately
increased.
