Interchangeability of Cross-Platform Orthophotographic and LiDAR Data in DeepLabV3+-Based Land Cover Classification Method
Land,
Год журнала:
2025,
Номер
14(2), С. 217 - 217
Опубликована: Янв. 21, 2025
Riverine
environmental
information
includes
important
data
to
collect,
and
the
collection
still
requires
personnel’s
field
surveys.
These
on-site
tasks
face
significant
limitations
(i.e.,
hard
or
danger
entry).
In
recent
years,
as
one
of
efficient
approaches
for
collection,
air-vehicle-based
Light
Detection
Ranging
technologies
have
already
been
applied
in
global
research,
i.e.,
land
cover
classification
(LCC)
monitoring.
For
this
study,
authors
specifically
focused
on
seven
types
LCC
bamboo,
tree,
grass,
bare
ground,
water,
road,
clutter)
that
can
be
parameterized
flood
simulation.
A
validated
airborne
LiDAR
bathymetry
system
(ALB)
a
UAV-borne
green
System
(GLS)
were
study
cross-platform
analysis
LCC.
Furthermore,
visualized
using
high-contrast
color
scales
improve
accuracy
methods
through
image
fusion
techniques.
If
high-resolution
aerial
imagery
is
available,
then
it
must
downscaled
match
resolution
low-resolution
point
clouds.
Cross-platform
interchangeability
was
assessed
by
comparing
interchangeability,
which
measures
absolute
difference
overall
(OA)
macro-F1
interchangeability.
It
noteworthy
relying
solely
photographs
inadequate
achieving
precise
labeling,
particularly
under
limited
sunlight
conditions
lead
misclassification.
such
cases,
plays
crucial
role
facilitating
target
recognition.
All
digital
imagery,
LiDAR-derived
fusion)
present
results
over
0.65
OA
around
0.6
macro-F1.
The
found
vegetation
(bamboo,
grass)
road
species
comparatively
better
performance
compared
with
clutter
ground
species.
Given
stated
conditions,
differences
derived
from
different
years
(ALB
year
2017
GLS
2020)
are
main
reason.
Because
identification
all
items
except
relative
RGB-based
features
cannot
substituted
easily
because
3-year
gap
other
Derived
reconstruction,
also
has
further
change
between
ALB
leads
decreased
case
individual
species,
without
considering
seasons
platforms,
classify
bamboo
trees
higher
F1
scores
especially
proved
high
types.
photography
(UAV),
high-precision
measurement
(ALB,
GLS),
satellite
used.
equipment
expensive,
opportunities
limited.
Based
this,
would
desirable
if
could
continuously
classified
Artificial
Intelligence,
investigated
unique
aspect
exploring
models
across
platforms.
Язык: Английский
Various scenarios of measurements using a smartphone with a LiDAR sensor in the context of integration with the TLS point cloud
Reports on Geodesy and Geoinformatics,
Год журнала:
2025,
Номер
119(1), С. 14 - 22
Опубликована: Фев. 5, 2025
Abstract
Smartphones
with
Light
Detection
and
Ranging
(LiDAR)
sensors
are
increasingly
used
for
engineering
measurements.
Although
the
processing
of
acquired
point
clouds
seems
similar
to
measured
with,
example,
a
terrestrial
laser
scanner,
data
from
smartphone
requires
special
approach,
first
all,
when
it
comes
methods
obtaining
registering
obtain
one
complete
metric
cloud.
The
research
consisted
comparing
various
scenarios
measuring
using
LiDAR
sensor
(a
held
in
hand,
on
selfie
stick,
mounted
gimbal),
two
acquisition
strategies
(one
direction
zigzag)
registration
(point
cloud
cloud).
aim
study
was
find
best
solution
obtained
referenced
scanning
(TLS)
It
turns
out
that
how
we
field
is
important
affects
accuracy
integration.
results
showed
use
additional
devices
such
as
gimbal
supports
process
has
an
impact
registration.
In
analysed
case,
RMSE
error
smallest
amounted
0.012
m
0.019
m,
while
largest
0.060
0.065
object
1
2,
respectively.
result
proposed
methodology
can
be
considered
satisfactory.
Язык: Английский
Everyday-Carry Equipment Mapping: A Portable and Low-Cost Method for 3D Digital Documentation of Architectural Heritage by Integrated iPhone and Microdrone
Buildings,
Год журнала:
2024,
Номер
15(1), С. 89 - 89
Опубликована: Дек. 30, 2024
Mapping
constitutes
a
critical
component
of
architectural
heritage
research,
providing
the
groundwork
for
both
conservation
and
utilization
efforts.
Three-dimensional
(3D)
digital
documentation
represents
prominent
form
mapping
in
contemporary
era,
its
value
is
widely
recognized.
However,
cost
portability
constraints
often
limit
widespread
use
routine
research
initiatives.
This
study
proposes
cost-effective
portable
approach
to
3D
documentation,
employing
everyday-carry
(EDC)
equipment,
iPhone
15
Pro
DJI
Mini
4
Pro,
data
acquisition
heritage.
The
workflow
was
subsequently
optimized,
datasets
from
iPhone-LiDAR
microdrone
were
seamlessly
integrated,
resulting
an
integrated
model
indoor
outdoor
spaces
site.
demonstrated
overall
relative
error
4.93%,
achieving
centimeter-level
accuracy,
precise
spatial
alignment
between
sections,
clear
smooth
texture
mapping,
high
visibility,
suitability
display
applications.
optimized
leverages
strengths
EDC
equipment
types
while
addressing
limitations
identified
prior
studies.
Язык: Английский