Buildings,
Год журнала:
2024,
Номер
14(10), С. 3255 - 3255
Опубликована: Окт. 14, 2024
After
an
earthquake,
rapid
assessment
of
building
damage
is
crucial
for
emergency
response,
reconstruction
planning,
and
public
safety.
This
study
evaluates
the
performance
various
Generative
Artificial
Intelligence
(GAI)
models
in
analyzing
post-earthquake
images
to
classify
structural
according
EMS-98
scale,
ranging
from
minor
total
destruction.
Correct
classification
rates
masonry
buildings
varied
28.6%
64.3%,
with
mean
grade
errors
between
0.50
0.79,
while
reinforced
concrete
buildings,
ranged
37.5%
75.0%,
0.88.
Fine-tuning
these
could
substantially
improve
accuracy.
The
practical
implications
are
significant:
integrating
accurate
GAI
into
disaster
response
protocols
can
drastically
reduce
time
resources
required
compared
traditional
methods.
acceleration
enables
services
make
faster,
data-driven
decisions,
optimize
resource
allocation,
potentially
save
lives.
Furthermore,
widespread
adoption
enhance
resilience
planning
by
providing
valuable
data
future
infrastructure
improvements.
results
this
work
demonstrate
promise
rapid,
automated,
precise
evaluation,
underscoring
their
potential
as
invaluable
tools
engineers,
policymakers,
responders
scenarios.
Information,
Год журнала:
2024,
Номер
15(12), С. 755 - 755
Опубликована: Ноя. 27, 2024
Deep
learning
(DL)
has
become
a
core
component
of
modern
artificial
intelligence
(AI),
driving
significant
advancements
across
diverse
fields
by
facilitating
the
analysis
complex
systems,
from
protein
folding
in
biology
to
molecular
discovery
chemistry
and
particle
interactions
physics.
However,
field
deep
is
constantly
evolving,
with
recent
innovations
both
architectures
applications.
Therefore,
this
paper
provides
comprehensive
review
DL
advances,
covering
evolution
applications
foundational
models
like
convolutional
neural
networks
(CNNs)
Recurrent
Neural
Networks
(RNNs),
as
well
such
transformers,
generative
adversarial
(GANs),
capsule
networks,
graph
(GNNs).
Additionally,
discusses
novel
training
techniques,
including
self-supervised
learning,
federated
reinforcement
which
further
enhance
capabilities
models.
By
synthesizing
developments
identifying
current
challenges,
insights
into
state
art
future
directions
research,
offering
valuable
guidance
for
researchers
industry
experts.
Technologies,
Год журнала:
2024,
Номер
12(9), С. 163 - 163
Опубликована: Сен. 13, 2024
The
synergy
between
artificial
intelligence
(AI)
and
hyperspectral
imaging
(HSI)
holds
tremendous
potential
across
a
wide
array
of
fields.
By
leveraging
AI,
the
processing
interpretation
vast
complex
data
generated
by
HSI
are
significantly
enhanced,
allowing
for
more
accurate,
efficient,
insightful
analysis.
This
powerful
combination
has
to
revolutionize
key
areas
such
as
agriculture,
environmental
monitoring,
medical
diagnostics
providing
precise,
real-time
insights
that
were
previously
unattainable.
In
instance,
AI-driven
can
enable
precise
crop
monitoring
disease
detection,
optimizing
yields
reducing
waste.
this
technology
track
changes
in
ecosystems
with
unprecedented
detail,
aiding
conservation
efforts
disaster
response.
diagnostics,
AI-HSI
could
earlier
accurate
improving
patient
outcomes.
As
AI
algorithms
advance,
their
integration
is
expected
drive
innovations
enhance
decision-making
various
sectors.
continued
development
these
technologies
likely
open
new
frontiers
scientific
research
practical
applications,
accessible
tools
wider
range
users.
Advances in environmental engineering and green technologies book series,
Год журнала:
2025,
Номер
unknown, С. 73 - 114
Опубликована: Янв. 24, 2025
This
chapter
examines
using
artificial
intelligence
(AI)
and
deep
learning
(DL)
in
disaster
management.
It
describes
a
paradigm
shift
towards
proactive
measures
preventing
managing
natural
disasters.
Traditional,
reactive
methods
often
reach
their
limits.
At
the
same
time,
AI-based
approaches
can
improve
early
warning
systems
allocate
resources
more
efficiently
through
analysis
of
large,
heterogeneous
data
sets
ability
to
recognize
complex
patterns.
The
article
highlights
application
DL
models,
such
as
Convolutional
Neural
Networks
(CNNs),
analyze
satellite
imagery
utility
response.
Both
technical
ethical
challenges
are
discussed,
particularly
protection,
bias,
transparency
models.
Finally,
framework
is
presented
that
provides
guidelines
for
effective
responsible
use
AI
management
promotes
long-term
sustainability
fairness
this
area.
ISPRS International Journal of Geo-Information,
Год журнала:
2025,
Номер
14(2), С. 56 - 56
Опубликована: Фев. 1, 2025
Recent
years
have
witnessed
a
revolution
of
artificial
intelligence
(AI)
technologies,
highlighted
by
the
rise
generative
AI
and
geospatial
(GeoAI)
[...]
International Journal of Advanced Research in Science Communication and Technology,
Год журнала:
2025,
Номер
unknown, С. 369 - 375
Опубликована: Апрель 3, 2025
Natural
hazards
may
result
in
catastrophic
damage
and
significant
socioeconomic
loss.In
recent
decades,
there
has
been
an
increasing
trend
the
actual
loss
that
observed.
Disaster
managers
are
therefore
under
pressure
to
proactively
safeguard
their
communities
through
development
of
effective
management
techniques.
In
order
support
informed
disaster
management,
several
research
studies
process
disaster-related
data
using
artificial
intelligence
(AI)
The
four
stages
management—preparation,
response,
recovery,
mitigation—are
covered
this
study's
summary
current
AI
applications.
Along
with
some
useful
AI-based
decision
tools,
it
provides
examples
how
various
techniques
can
be
applied
highlights
advantages
for
assisting
at
stages.
We
discover
most
applications
concentrate
on
phase
response.
motivate
scientific
community
develop
methods
resolving
these
issues
subsequent
studies,
study
also
identifies
challenges
