Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 22, 2025
Aiming
at
the
optimization
of
public
sports
service
quality,
this
study
analyzes
data
deeply
by
constructing
a
supervised
learning
model.
Firstly,
theoretical
framework
is
established.
Secondly,
technical
constructed
based
on
Finally,
comprehensive
performance
model
evaluated
using
dataset
and
practical
application.
The
results
show
that
when
used
to
process
data,
its
excellent.
Specifically,
model's
accuracy
recall
in
processing
various
types
markedly
exceed
expectations,
with
reaching
more
than
88%
remaining
similarly
high
level.
This
remarkable
result
not
only
validates
practicability
quality
services
but
also
highlights
huge
application
potential
value.
In
addition,
possibility
challenge
are
discussed,
which
provides
useful
reference
for
further
improving
service.
findings
enrich
research
methods
field
offer
scientific
basis
relevant
decision-making,
helps
promote
continuous
development
services.
Remote Sensing of Environment,
Год журнала:
2024,
Номер
311, С. 114290 - 114290
Опубликована: Июль 14, 2024
Mapping
the
distribution,
pattern,
and
composition
of
urban
land
use
categories
plays
a
valuable
role
in
understanding
environmental
dynamics
facilitating
sustainable
development.
Decades
effort
mapping
have
accumulated
series
approaches
products.
New
trends
characterized
by
open
big
data
advanced
artificial
intelligence,
especially
deep
learning,
offer
unprecedented
opportunities
for
patterns
from
regional
to
global
scales.
Combined
with
large
amounts
geospatial
data,
learning
has
potential
promote
higher
levels
scale,
accuracy,
efficiency,
automation.
Here,
we
comprehensively
review
advances
based
research
practices
aspects
sources,
classification
units,
approaches.
More
specifically,
delving
into
different
settings
on
learning-based
mapping,
design
eight
experiments
Shenzhen,
China
investigate
their
impacts
performance
terms
sample,
model.
For
each
investigated
setting,
provide
quantitative
evaluations
discussed
inform
more
convincing
comparisons.
Based
historical
retrospection
experimental
evaluation,
identify
prevailing
limitations
challenges
suggest
prospective
directions
that
could
further
facilitate
exploitation
techniques
using
remote
sensing
other
spatial
across
various
Environment and Planning B Urban Analytics and City Science,
Год журнала:
2023,
Номер
51(5), С. 1104 - 1123
Опубликована: Сен. 29, 2023
Geospatial
artificial
intelligence
(GeoAI)
is
proliferating
in
urban
analytics,
where
graph
neural
networks
(GNNs)
have
become
one
of
the
most
popular
methods
recent
years.
However,
along
with
success
GNNs,
black
box
nature
AI
models
has
led
to
various
concerns
(e.g.
algorithmic
bias
and
model
misuse)
regarding
their
adoption
particularly
when
studying
socio-economics
high
transparency
a
crucial
component
social
justice.
Therefore,
desire
for
increased
explainability
interpretability
attracted
increasing
research
interest.
This
article
proposes
an
explainable
spatially
explicit
GeoAI-based
analytical
method
that
combines
convolutional
network
(GCN)
graph-based
(XAI)
method,
called
GNNExplainer.
Here,
we
showcase
ability
our
proposed
two
studies
within
analytics:
traffic
volume
prediction
population
estimation
tasks
node
classification
classification,
respectively.
For
these
tasks,
used
Street
View
Imagery
(SVI),
trending
data
source
analytics.
We
extracted
semantic
information
from
images
assigned
them
as
features
roads.
The
GCN
first
provided
reasonable
predictions
related
by
encoding
roads
nodes
connectivities
graphs.
GNNExplainer
then
offered
insights
into
how
certain
are
made.
Through
such
process,
practical
conclusions
can
be
derived
phenomena
studied
here.
In
this
paper
also
set
out
path
developing
XAI
future
studies.
International Journal of Geographical Information Science,
Год журнала:
2024,
Номер
38(11), С. 2183 - 2215
Опубликована: Июль 14, 2024
The
emergence
of
crowdsourced
geographic
information
(CGI)
has
markedly
accelerated
the
evolution
land-use
and
land-cover
(LULC)
mapping.
This
approach
taps
into
collective
power
public
to
share
spatial
information,
providing
a
relevant
data
source
for
producing
LULC
maps.
Through
analysis
262
papers
published
from
2012
2023,
this
work
provides
comprehensive
overview
field,
including
prominent
researchers,
key
areas
study,
major
CGI
sources,
mapping
methods,
scope
research.
Additionally,
it
evaluates
pros
cons
various
sources
methods.
findings
reveal
that
while
applying
with
labels
is
common
way
by
using
analysis,
limited
incomplete
coverage
other
quality
issues.
In
contrast,
extracting
semantic
features
interpretation
often
requires
integrating
multiple
datasets
remote
sensing
imagery,
alongside
advanced
methods
such
as
ensemble
deep
learning.
paper
also
delves
challenges
posed
in
explores
promising
potential
introducing
large
language
models
overcome
these
hurdles.
