Buildings,
Journal Year:
2024,
Volume and Issue:
14(12), P. 3777 - 3777
Published: Nov. 26, 2024
With
the
increasing
complexity
of
construction
site
environments,
robust
object
detection
and
segmentation
technologies
are
essential
for
enhancing
intelligent
monitoring
ensuring
safety.
This
study
investigates
application
YOLOv11-Seg,
an
advanced
target
technology,
recognition
on
sites.
The
research
focuses
improving
13
categories,
including
excavators,
bulldozers,
cranes,
workers,
other
equipment.
methodology
involves
preparing
a
high-quality
dataset
through
cleaning,
annotation,
augmentation,
followed
by
training
YOLOv11-Seg
model
over
351
epochs.
loss
function
analysis
indicates
stable
convergence,
demonstrating
model’s
effective
learning
capabilities.
evaluation
results
show
[email protected]
average
0.808,
F1
Score(B)
0.8212,
Score(M)
0.8382,
with
81.56%
test
samples
achieving
confidence
scores
above
90%.
performs
effectively
in
static
scenarios,
such
as
equipment
Xiong’an
New
District,
dynamic
real-time
workers
vehicles,
maintaining
performance
even
at
1080P
resolution.
Furthermore,
it
demonstrates
robustness
under
challenging
conditions,
nighttime,
non-construction
scenes,
incomplete
images.
concludes
that
exhibits
strong
generalization
capability
practical
utility,
providing
reliable
foundation
safety
Future
work
may
integrate
edge
computing
UAV
to
support
digital
transformation
management.
IEEE Communications Surveys & Tutorials,
Journal Year:
2024,
Volume and Issue:
26(2), P. 1127 - 1170
Published: Jan. 1, 2024
Artificial
Intelligence-Generated
Content
(AIGC)
is
an
automated
method
for
generating,
manipulating,
and
modifying
valuable
diverse
data
using
AI
algorithms
creatively.
This
survey
paper
focuses
on
the
deployment
of
AIGC
applications,
e.g.,
ChatGPT
Dall-E,
at
mobile
edge
networks,
namely
that
provide
personalized
customized
services
in
real
time
while
maintaining
user
privacy.
We
begin
by
introducing
background
fundamentals
generative
models
lifecycle
which
includes
collection,
training,
fine-tuning,
inference,
product
management.
then
discuss
collaborative
cloud-edge-mobile
infrastructure
technologies
required
to
support
enable
users
access
networks.
Furthermore,
we
explore
AIGC-driven
creative
applications
use
cases
Additionally,
implementation,
security,
privacy
challenges
deploying
Finally,
highlight
some
future
research
directions
open
issues
full
realization
Buildings,
Journal Year:
2024,
Volume and Issue:
14(1), P. 220 - 220
Published: Jan. 14, 2024
In
the
last
decade,
despite
rapid
advancements
in
artificial
intelligence
(AI)
transforming
many
industry
practices,
construction
largely
lags
adoption.
Recently,
emergence
and
adoption
of
advanced
large
language
models
(LLMs)
like
OpenAI’s
GPT,
Google’s
PaLM,
Meta’s
Llama
have
shown
great
potential
sparked
considerable
global
interest.
However,
current
surge
lacks
a
study
investigating
opportunities
challenges
implementing
Generative
AI
(GenAI)
sector,
creating
critical
knowledge
gap
for
researchers
practitioners.
This
underlines
necessity
to
explore
prospects
complexities
GenAI
integration.
Bridging
this
is
fundamental
optimizing
GenAI’s
early
stage
within
sector.
Given
unprecedented
capabilities
generate
human-like
content
based
on
learning
from
existing
content,
we
reflect
two
guiding
questions:
What
will
future
bring
industry?
are
delves
into
reflected
perception
literature,
analyzes
using
programming-based
word
cloud
frequency
analysis,
integrates
authors’
opinions
answer
these
questions.
paper
recommends
conceptual
implementation
framework,
provides
practical
recommendations,
summarizes
research
questions,
builds
foundational
literature
foster
subsequent
expansion
its
allied
architecture
engineering
domains.
Cartography and Geographic Information Science,
Journal Year:
2024,
Volume and Issue:
51(4), P. 599 - 630
Published: Jan. 16, 2024
The
past
decade
has
witnessed
the
rapid
development
of
geospatial
artificial
intelligence
(GeoAI)
primarily
due
to
ground-breaking
achievements
in
deep
learning
and
machine
learning.
A
growing
number
scholars
from
cartography
have
demonstrated
successfully
that
GeoAI
can
accelerate
previously
complex
cartographic
design
tasks
even
enable
creativity
new
ways.
Despite
promise
GeoAI,
researchers
practitioners
concerns
about
ethical
issues
for
cartography.
In
this
paper,
we
conducted
a
systematic
content
analysis
narrative
synthesis
research
studies
integrating
summarize
current
trends
regarding
usage
design.
Based
on
review
synthesis,
first
identify
dimensions
methods
such
as
data
sources,
formats,
map
evaluations,
six
contemporary
models,
each
which
serves
variety
tasks.
These
models
include
decision
trees,
knowledge
graph
semantic
web
technologies,
convolutional
neural
networks,
generative
adversarial
reinforcement
Further,
seven
applications
where
been
effectively
employed:
generalization,
symbolization,
typography,
reading,
interpretation,
analysis,
production.
We
also
raise
five
potential
challenges
need
be
addressed
integration
cartography:
commodification,
responsibility,
privacy,
bias,
(together)
transparency,
explainability,
provenance.
conclude
by
identifying
four
directions
future
with
GeoAI:
GeoAI-enabled
active
symbolism,
human-in-the-loop
cartography,
GeoAI-based
mapping-as-a-service,
Urban Informatics,
Journal Year:
2024,
Volume and Issue:
3(1)
Published: Oct. 14, 2024
Abstract
The
digital
transformation
of
modern
cities
by
integrating
advanced
information,
communication,
and
computing
technologies
has
marked
the
epoch
data-driven
smart
city
applications
for
efficient
sustainable
urban
management.
Despite
their
effectiveness,
these
often
rely
on
massive
amounts
high-dimensional
multi-domain
data
monitoring
characterizing
different
sub-systems,
presenting
challenges
in
application
areas
that
are
limited
quality
availability,
as
well
costly
efforts
generating
scenarios
design
alternatives.
As
an
emerging
research
area
deep
learning,
Generative
Artificial
Intelligence
(GenAI)
models
have
demonstrated
unique
values
content
generation.
This
paper
aims
to
explore
innovative
integration
GenAI
techniques
twins
address
planning
management
built
environments
with
focuses
various
such
transportation,
energy,
water,
building
infrastructure.
survey
starts
introduction
cutting-edge
generative
AI
models,
Adversarial
Networks
(GAN),
Variational
Autoencoders
(VAEs),
Pre-trained
Transformer
(GPT),
followed
a
scoping
review
existing
science
leverage
intelligent
autonomous
capability
facilitate
research,
operations,
critical
subsystems,
holistic
environment.
Based
review,
we
discuss
potential
opportunities
technical
strategies
integrate
into
next-generation
more
intelligent,
scalable,
automated
development
People and Nature,
Journal Year:
2024,
Volume and Issue:
6(2), P. 882 - 893
Published: Feb. 23, 2024
Abstract
The
ongoing
biodiversity
and
climate
change
crises
require
society
to
adopt
nature‐based
solutions
that
integrate
enhance
ecosystems.
To
achieve
successful
implementation
of
solutions,
it
is
vital
communicate
scientific
information
about
their
benefits
suitability.
This
article
explores
the
potential
generative
artificial
intelligence
(GenAI)
as
a
tool
for
automating
scaling
up
science
communication,
outreach,
extension
solutions.
illustrate
GenAI,
we
present
three
case
study
examples;
(1)
reporting
on
ecosystem
services,
future
land
use
options,
farms
(2)
interactively
providing
guidance
in
response
homeowner
questions
biodiversity‐friendly
garden
design
(3)
visualising
scenarios
landscape
incorporate
diverse
nature
based
technological
These
examples
demonstrate
applications
which
may
be
relevant
other
systems
types
While
GenAI
offers
significant
opportunities,
this
new
technology
brings
risks
bias,
false
information,
data
privacy,
mistrust,
high
energy
usage.
Alongside
development,
integrated
social
research
into
ethics,
public
acceptability,
user
experience,
maximise
while
limiting
these
risks.
an
opportunity
accelerate
dissemination
strategies
reach
broader
audience,
by
synthesising
producing
tailored
content
specific
users
locations.
By
harnessing
power
alongside
human
expertise,
can
support
tackle
complex
challenges
sustainability.
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