Remote Sensing,
Journal Year:
2023,
Volume and Issue:
15(16), P. 4112 - 4112
Published: Aug. 21, 2023
Integrating
Artificial
Intelligence
(AI)
techniques
with
remote
sensing
holds
great
potential
for
revolutionizing
data
analysis
and
applications
in
many
domains
of
Earth
sciences.
This
review
paper
synthesizes
the
existing
literature
on
AI
sensing,
consolidating
analyzing
methodologies,
outcomes,
limitations.
The
primary
objectives
are
to
identify
research
gaps,
assess
effectiveness
approaches
practice,
highlight
emerging
trends
challenges.
We
explore
diverse
including
image
classification,
land
cover
mapping,
object
detection,
change
hyperspectral
radar
analysis,
fusion.
present
an
overview
technologies,
methods
employed,
relevant
use
cases.
further
challenges
associated
practical
such
as
quality
availability,
model
uncertainty
interpretability,
integration
domain
expertise
well
solutions,
advancements,
future
directions.
provide
a
comprehensive
researchers,
practitioners,
decision
makers,
informing
at
exciting
intersection
sensing.
Drones,
Journal Year:
2023,
Volume and Issue:
7(10), P. 637 - 637
Published: Oct. 16, 2023
This
paper
reviews
the
diverse
applications
of
drone
technologies
in
built
environment
and
their
role
climate
change
research.
Drones,
or
unmanned
aerial
vehicles
(UAVs),
have
emerged
as
valuable
tools
for
environmental
scientists,
offering
new
possibilities
data
collection,
monitoring,
analysis
urban
environment.
The
begins
by
providing
an
overview
different
types
drones
used
environment,
including
quadcopters,
fixed-wing
drones,
hybrid
models.
It
explores
capabilities
features,
such
high-resolution
cameras,
LiDAR
sensors,
thermal
imaging,
which
enable
detailed
acquisition
studying
impacts
areas.
then
examines
specific
contribution
to
These
include
mapping
heat
islands,
assessing
energy
efficiency
buildings,
monitoring
air
quality,
identifying
sources
greenhouse
gas
emissions.
UAVs
researchers
collect
spatially
temporally
rich
data,
allowing
a
trends
patterns.
Furthermore,
discusses
integrating
with
artificial
intelligence
(AI)
derive
insights
develop
predictive
models
mitigation
adaptation
environments.
Finally,
addresses
technologies’
challenges
future
directions
encompass
regulatory
frameworks,
privacy
concerns,
management,
need
interdisciplinary
collaboration.
By
harnessing
potential
scientists
can
enhance
understanding
areas
contribute
developing
sustainable
strategies
resilient
cities.
Food Chemistry X,
Journal Year:
2024,
Volume and Issue:
22, P. 101309 - 101309
Published: March 19, 2024
The
increasing
global
population
drives
a
rising
demand
for
food,
particularly
fish
as
preferred
protein
source,
straining
capture
fisheries.
Overfishing
has
depleted
wild
stocks,
emphasizing
the
need
advanced
aquaculture
technologies.
Unlike
agriculture,
not
seen
substantial
technological
advancements.
Artificial
Intelligence
(AI)
tools
like
Internet
of
Things
(IoT),
machine
learning,
cameras,
and
algorithms
offer
solutions
to
reduce
human
intervention,
enhance
productivity,
monitor
health,
feed
optimization,
water
resource
management.
However,
challenges
such
data
collection,
standardization,
model
accuracy,
interpretability,
integration
with
existing
systems
persist.
This
review
explores
adoption
AI
techniques
advance
industry
bridge
gap
between
food
supply
demand.
Advances in environmental engineering and green technologies book series,
Journal Year:
2023,
Volume and Issue:
unknown, P. 1 - 18
Published: Dec. 30, 2023
Earth
observations
have
become
a
developing
trend
over
the
last
decade
because
of
their
ability
to
enable
real-time
tracking
and
forecasting
various
environmental
phenomena,
including
landslides,
drought,
floods,
wildfires.
However,
conventional
approaches
in
observation
relied
on
guide
processing
or
human
interpretation
statistics.
Via
mixing
AI,
statement's
achievement
has
progressed
significantly.
AI
offers
automatic
timely
analysis
significant
volumes
faraway
sensing
satellite
TV
for
computer
facts,
considering
natural
events
approaches.
The
software
Remark
enabled
several
advantages,
improved
accuracy
mapping
classification
gadgets,
detection
hobby
which
include
homes,
roads,
forests,
changes
land
use
cover.
Marine Pollution Bulletin,
Journal Year:
2024,
Volume and Issue:
202, P. 116273 - 116273
Published: April 3, 2024
Coral
reefs
are
home
to
a
variety
of
species,
and
their
preservation
is
popular
study
area;
however,
monitoring
them
significant
challenge,
for
which
the
use
robots
offers
promising
answer.
The
purpose
this
analyze
current
techniques
tools
employed
in
coral
reef
monitoring,
with
focus
on
role
robotics
its
potential
transforming
sector.
Using
systematic
review
methodology
examining
peer-reviewed
literature
across
engineering
earth
sciences
from
Scopus
database
focusing
"robotics"
"coral
reef"
keywords,
article
divided
into
three
sections:
case
studies.
initial
findings
indicated
strategies,
each
own
advantages
disadvantages.
Case
studies
have
also
highlighted
global
application
emphasizing
challenges
opportunities
unique
context.
Robotic
interventions
driven
by
artificial
intelligence
machine
learning
led
new
era
monitoring.
Such
developments
not
only
improve
but
support
conservation
restoration
these
vulnerable
ecosystems.
Further
research
required,
particularly
robotic
systems
nurseries
maximizing
health
both
indoor
open-sea
settings.
Frontiers in Artificial Intelligence,
Journal Year:
2023,
Volume and Issue:
6
Published: Jan. 26, 2023
Effective
conservation
of
maritime
environments
and
wildlife
management
endangered
species
require
the
implementation
efficient,
accurate
scalable
solutions
for
environmental
monitoring.
Ecoacoustics
offers
advantages
non-invasive,
long-duration
sampling
sounds
has
potential
to
become
reference
tool
biodiversity
surveying.
However,
analysis
interpretation
acoustic
data
is
a
time-consuming
process
that
often
requires
great
amount
human
supervision.
This
issue
might
be
tackled
by
exploiting
modern
techniques
automatic
audio
signal
analysis,
which
have
recently
achieved
impressive
performance
thanks
advances
in
deep
learning
research.
In
this
paper
we
show
convolutional
neural
networks
can
indeed
significantly
outperform
traditional
methods
challenging
detection
task:
identification
dolphin
whistles
from
underwater
recordings.
The
proposed
system
detect
signals
even
presence
ambient
noise,
at
same
time
consistently
reducing
likelihood
producing
false
positives
negatives.
Our
results
further
support
adoption
artificial
intelligence
technology
improve
monitoring
marine
ecosystems.
