Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh
Water,
Journal Year:
2024,
Volume and Issue:
16(8), P. 1141 - 1141
Published: April 17, 2024
Mapping
spatial
data
is
essential
for
the
monitoring
of
flooded
areas,
prognosis
hazards
and
prevention
flood
risks.
The
Ganges
River
Delta,
Bangladesh,
world’s
largest
river
delta
prone
to
floods
that
impact
social–natural
systems
through
losses
lives
damage
infrastructure
landscapes.
Millions
people
living
in
this
region
are
vulnerable
repetitive
due
exposure,
high
susceptibility
low
resilience.
Cumulative
effects
monsoon
climate,
rainfall,
tropical
cyclones
hydrogeologic
setting
Delta
increase
probability
floods.
While
engineering
methods
mitigation
include
practical
solutions
(technical
construction
dams,
bridges
hydraulic
drains),
regulation
traffic
land
planning
support
systems,
geoinformation
rely
on
modelling
remote
sensing
(RS)
evaluate
dynamics
hazards.
Geoinformation
indispensable
mapping
catchments
areas
visualization
affected
regions
real-time
monitoring,
addition
implementing
developing
emergency
plans
vulnerability
assessment
warning
supported
by
RS
data.
In
regard,
study
used
monitor
southern
segment
Delta.
Multispectral
Landsat
8-9
OLI/TIRS
satellite
images
were
evaluated
(March)
post-flood
(November)
periods
analysis
extent
landscape
changes.
Deep
Learning
(DL)
algorithms
GRASS
GIS
modules
qualitative
quantitative
as
advanced
image
processing.
results
constitute
a
series
maps
based
classified
Language: Английский
Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review
Ivan Malashin,
No information about this author
D. A. Martysyuk,
No information about this author
В С Тынченко
No information about this author
et al.
Polymers,
Journal Year:
2024,
Volume and Issue:
16(23), P. 3368 - 3368
Published: Nov. 29, 2024
The
integration
of
machine
learning
(ML)
into
material
manufacturing
has
driven
advancements
in
optimizing
biopolymer
production
processes.
ML
techniques,
applied
across
various
stages
production,
enable
the
analysis
complex
data
generated
throughout
identifying
patterns
and
insights
not
easily
observed
through
traditional
methods.
As
sustainable
alternatives
to
petrochemical-based
plastics,
biopolymers
present
unique
challenges
due
their
reliance
on
variable
bio-based
feedstocks
processing
conditions.
This
review
systematically
summarizes
current
applications
techniques
aiming
provide
a
comprehensive
reference
for
future
research
while
highlighting
potential
enhance
efficiency,
reduce
costs,
improve
product
quality.
also
shows
role
algorithms,
including
supervised,
unsupervised,
deep
Language: Английский
Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS
Geomatics,
Journal Year:
2025,
Volume and Issue:
5(1), P. 5 - 5
Published: Jan. 20, 2025
This
article
presents
the
application
of
novel
cartographic
methods
vegetation
mapping
with
a
case
study
Rif
Mountains,
northern
Morocco.
The
area
is
notable
for
varied
geomorphology
and
diverse
landscapes.
methodology
includes
ML
modules
GRASS
GIS
‘r.learn.train’,
‘r.learn.predict’,
‘r.random’
algorithms
supervised
classification
implemented
from
Scikit-Learn
libraries
Python.
approach
provides
platform
processing
spatiotemporal
data
satellite
image
analysis.
objective
to
determine
robustness
“DecisionTreeClassifier”
“ExtraTreesClassifier”
algorithms.
time
series
images
covering
Morocco
consists
six
Landsat
scenes
2023
bimonthly
interval.
Land
cover
maps
are
produced
based
on
processed,
classified,
analyzed
images.
results
demonstrated
seasonal
changes
in
land
types.
validation
was
performed
using
dataset
Food
Agriculture
Organization
(FAO).
contributes
environmental
monitoring
North
Africa
processing.
Using
RS
combined
powerful
functionality
FAO-derived
datasets,
topographic
variability,
moderate-scale
habitat
heterogeneity,
distribution
types
have
been
assessed
first
time.
Language: Английский
Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python
Examples and Counterexamples,
Journal Year:
2025,
Volume and Issue:
7, P. 100180 - 100180
Published: Feb. 3, 2025
Language: Английский
Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS
SSRN Electronic Journal,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
Language: Английский
Integrating the NDVI-Random Forest Classification for Vegetation Analysis - Yercaud Hills, India
M. Sam Navin,
No information about this author
B.G Aravind Sidharth,
No information about this author
Gilles Richard
No information about this author
et al.
Published: May 17, 2024
Language: Английский
Flavor Identification Based on Olfactory-Taste Synesthesia Model and Hybrid Convolutional Neural Network-Random Forest
Measurement Science and Technology,
Journal Year:
2024,
Volume and Issue:
35(11), P. 115115 - 115115
Published: Aug. 15, 2024
Abstract
The
bionic-based
electronic
nose
(e-nose)
and
tongue
(e-tongue)
show
satisfactory
performance
in
flavor
analysis.
Traditional
analysis
of
the
e-nose
e-tongue
systems
focuses
on
data
fusion,
effects
bionic
characteristics
are
rarely
studied.
Motivated
by
this,
a
method,
including
an
olfactory-taste
synesthesia
model
(OTSM)
convolutional
neural
network-random
forest
(CNN-RF),
is
proposed
for
effective
identification
substances.
OTSM
developed
human
nerve
conduction
mechanisms
to
enhance
combined
with
CNN-RF
identification.
results
that,
first,
when
stimulated
data,
physiological
1/
f
synchronization
shown
using
OTSM.
enhancement
fusion
system
validated
synchronization.
Second,
fully
connected
layer
CNN
replaced
RF
improve
Finally,
evaluated
comparison
other
recognition
models
ablation
studies
confirm
its
effectiveness.
By
comparison,
best
performance,
accuracies
96.67%,
95.00%,
F
1
-scores
96.65%,
96.66%,
94.95%,
kappa
coefficients
96.03%,
96.10%,
93.44%,
five
beers,
apples,
four
mixed
solutions,
respectively,
obtained
CNN-RF.
In
conclusion,
excellent
achieved
models.
Language: Английский
STGRL: SNN based Two-Stage Geomagnetic Road Localization Method
Qinghua Luo,
No information about this author
Mutong Yu,
No information about this author
Xiaozhen Yan
No information about this author
et al.
Measurement Science and Technology,
Journal Year:
2024,
Volume and Issue:
36(1), P. 016322 - 016322
Published: Oct. 30, 2024
Abstract
Geomagnetic
navigation
is
a
widely
used
positioning
method
capable
of
correcting
the
cumulative
errors
odometers
and
inertial
systems,
thereby
ensuring
long-distance
for
vehicles
in
GPS-denied
environments.
However,
common
geomagnetic
road
algorithms
are
susceptible
to
measurement
noise,
which
hinder
improvements
efficiency
accuracy.
To
address
this
issue,
paper
proposes
Siamese
Neural
Network
(SNN)
based
two-stage
localization
method.
First,
attitude
angle
information
combined
with
scalar
vector
value
establish
reference
database
increase
feature
dimensions
matching.
Then,
we
use
Random
Forest
algorithm
perform
coarse
matching
data
sequence
determine
current
road,
balancing
increased
computational
load
resulting
from
addition
dimensions.
Finally,
further
reduce
impact
random
employs
SNN
on
Transformer
Encoder
fine
sequence.
Experiments
show
that
compared
existing
methods,
average
absolute
error
our
has
been
reduced
32.36
m
4.07
m,
kept
within
an
acceptable
range.
Language: Английский
Mapping Coastal Regions of Guinea-Bissau for analysis of Mangrove Dynamics using Remote Sensing Data
Transylvanian Review of Systematical and Ecological Research,
Journal Year:
2024,
Volume and Issue:
26(2), P. 17 - 30
Published: Aug. 1, 2024
Abstract
The
study
presents
mapping
of
land
cover
changes
in
Guinea-Bissau
using
remote
sensing
data.
Study
area
includes
tidal
floodplains
the
rivers
Geba,
Caceu,
and
Rio
Grande
de
Buba.
Satellite
images
Landsat
8-9
OLI/TIRS
were
classified
analysed
to
evaluate
landscape
dynamics
from
2017
2023.
methodology
is
based
on
GRASS
GIS
modules
“i.
cluster”
maxlik”
for
image
analysis.
results
indicated
variations
patterns:
decrease
natural
forests,
decline
mangroves,
expansion
urban
agricultural
areas.
coastal
region
one
least
known
tropical
ecosystems
West
Africa,
it
among
most
vulnerable
African
countries
climate
effects.
paper
contributes
environmental
monitoring
coasts.
Language: Английский