Enhancing Change Detection in Multi-Temporal Optical Images Using a Novel Multi-Scale Deep Learning Approach Based on LSTM
Advances in Space Research,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 1, 2025
Language: Английский
Land-use and habitat quality prediction in the Fen River Basin based on PLUS and InVEST models
Yanjun Hou,
No information about this author
Juemei Wu
No information about this author
Frontiers in Environmental Science,
Journal Year:
2024,
Volume and Issue:
12
Published: July 19, 2024
Assessment
and
prediction
analyses
of
the
ecological
environmental
quality
river
basins
are
pivotal
to
realize
protection
high-quality
coordinated
development.
Methods:
The
PLUS
InVEST
models
were
used
analyze
spatiotemporal
evolution
characteristics
land-use
in
Fen
River
Basin
simulate
spatial
pattern
under
natural
development
(ND),
(EC),
economic
(ED)
scenarios
2030,
as
well
evaluate
habitat
(HQ)
its
variation
from
2000
2030.
From
2020,
consisted
primarily
cultivated
land,
followed
by
forests,
then
unused
land.
Habitat
showed
a
downward
trend
2020.
Between
2010
rate
decline
decreased,
HQ
EC
scenario
exhibited
improvement
compared
However,
there
was
reduction
obvious
heterogeneity
distribution,
showing
“low
middle
high
edge”.
land
converted
into
construction
grasslands,
conversion
forests
dominated
changes
Basin.
Language: Английский
Introduction to Google Earth Engine: A comprehensive workflow
Nitin Arora,
No information about this author
Sakshi Sakshi,
No information about this author
Sartajvir Singh
No information about this author
et al.
Elsevier eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 3 - 18
Published: Jan. 1, 2025
Language: Английский
AI-driven tools and technologies for agriculture land use & land cover classification using earth observation data analytics
Amandeep Kaur,
No information about this author
Gurwinder Singh,
No information about this author
Sartajvir Singh
No information about this author
et al.
Elsevier eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 527 - 541
Published: Jan. 1, 2025
Language: Английский
A Novel Pixel-based Deep Neural Network in Posterior Probability Space for the Detection of Agriculture Changes Using Remote Sensing Data
Gurwinder Singh,
No information about this author
Narayan Vyas,
No information about this author
Neelam Dahiya
No information about this author
et al.
Remote Sensing Applications Society and Environment,
Journal Year:
2025,
Volume and Issue:
unknown, P. 101591 - 101591
Published: May 1, 2025
Language: Английский
Recurrent Neural Network-Based Classification of Potato Leaves using RGB Images
Apoorva Sharma,
No information about this author
Avni Sharma
No information about this author
Published: May 2, 2024
In
this
study,
a
highly
accurate
classification
system
for
potato
leaves
using
Recurrent
Neural
Networks
(RNNs)
on
the
Plant
Village
dataset
has
been
presented.
The
comprises
diverse
collection
of
leaf
images,
with
annotations
healthy
and
those
affected
by
various
diseases
stressors.
Leveraging
temporal
dependencies
inherent
in
RNNs,
it
is
aimed
to
effectively
capture
intricate
patterns
features
sequential
data,
particularly
crucial
time-series
analysis
plant
growth
stages
disease
progression.
proposed
RNN
architecture,
incorporating
long
short-term
memory
(LSTM)
units
address
vanishing
gradient
problem,
demonstrated
exceptional
performance
accurately
classifying
health,
enabling
early
detection
timely
interventions
improved
crop
management.
Our
study
demonstrates
robustness
high
accuracy
RNNs
leaves,
metrics
exceeding
92%.
integration
RNN-based
systems
into
precision
agriculture
holds
tremendous
promise,
providing
farmers
valuable
insights
interventions,
optimizing
ensuring
sustainable
food
production.
From
experimental
outcomes,
observed
that
"RNN
Model"
records
highest
0.927,
which
higher
value
compared
"CNN
(Convolution
neural
network)
(0.902)
"Feedforward
network"
(0.867).
success
DL
model
agricultural
applications
illustrates
transformative
potential
AI
technologies
addressing
global
security
challenges
revolutionizing
future
agriculture.
Language: Английский