The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences,
Год журнала:
2024,
Номер
XLVIII-1-2024, С. 395 - 400
Опубликована: Май 10, 2024
Abstract.
GF-7
satellite
was
successfully
launched
on
3
Nov
2019.
The
two-line
stereo
camera
can
effectively
obtain
20
km-width
panchromatic
images
with
resolution
better
than
0.8m
and
3.2m-resolution
multispectral
images.
Through
the
composite
mapping
mode
of
laser
altimeter,
realizes
1:10,000-scale
mapping.
This
article
selected
Qiafuqihai
Reservoir
its
upstream
Yili
River
Basin
as
study
area.
Hydrological
landforms
vegetation
coverage
were
monitored
three-dimensional
dynamic
simulation
software
developed
to
verify
potential
application
data
in
supporting
drainage
basin
water
resource
allocation
management
future
scenarios.
meters
DSM
derived
from
surveying
finely
portrayed
characteristics
hydroponic
small
watersheds.
divided
into
eleven
density
8.5
times
more
which
produced
STRM
90m.
elevation
water-level
fluctuation
zone
ranged
970
998
area
changed
28.3
57.6
square
kilometres.
terrain
northwest
northeast
flat,
main
types
being
natural
grasslands
(64.9%)
arid
lands
(5.03%).
visualization
demonstrated
hydrology
information,
information
changes
landform
environment.
GF-7,
satellite,
could
be
perfectly
able
support
resources
deployment
future.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
Год журнала:
2024,
Номер
17, С. 6514 - 6528
Опубликована: Янв. 1, 2024
Extracting
building
heights
from
single-view
remote
sensing
images
greatly
enhances
the
application
of
data.
While
methods
for
extracting
height
shadow
have
been
widely
studied,
it
remains
a
challenging
task.
The
main
reasons
are
as
follows:
(1)
traditional
method
information
exhibits
low
accuracy.
(2)
use
only
to
extract
results
in
limited
scenarios.
To
solve
above
problems,
this
paper
introduces
side
and
complement
each
other,
proposes
extraction
high-resolution
using
information.
Firstly,
we
propose
RMU-Net
method,
which
utilizes
multi-scale
features
This
aims
address
issues
related
pixel
detail
loss
imprecise
edge
segmentation,
result
significant
scale
differences
within
segmentation
targets.
Additionally,
employ
area
threshold
optimize
results,
specifically
tackle
small
stray
patches
holes,
enhancing
overall
integrity
accuracy
extraction.
Secondly,
that
integrates
based
on
an
enhanced
proportional
coefficient
model.
measuring
lengths
is
improved
by
incorporating
fishing
net
informed
our
analysis
geometric
relationships
among
buildings.
Finally,
establish
dataset
containing
images,
select
multiple
areas
experimental
analysis.
demonstrate
91.03%
90.29%.
average
absolute
error
(MAE)
1.22,
while
root
mean
square
(RMSE)
1.21.
Furthermore,
proposed
method's
validity
scalability
affirmed
through
analyses
applicability
anti-interference
performance
extensive
areas.
Earth system science data,
Год журнала:
2024,
Номер
16(11), С. 5357 - 5374
Опубликована: Ноя. 25, 2024
Abstract.
Understanding
urban
vertical
structures,
particularly
building
heights,
is
essential
for
examining
the
intricate
interaction
between
humans
and
their
environment.
Such
datasets
are
indispensable
a
variety
of
applications,
including
climate
modeling,
energy
consumption
analysis,
socioeconomic
activities.
Despite
importance
this
information,
previous
studies
have
primarily
focused
on
estimating
heights
regionally
at
grid
scale,
often
resulting
in
with
limited
coverage
or
spatial
resolution.
This
limitation
hampers
comprehensive
global
analysis
ability
to
generate
actionable
insights
finer
scales.
In
study,
we
developed
height
map
footprint
scale
by
leveraging
Earth
Observation
(EO)
advanced
machine
learning
techniques.
Our
approach
integrated
multisource
remote-sensing
features
morphology
develop
estimation
models
using
extreme
gradient
boosting
(XGBoost)
regression
method
across
diverse
regions.
methodology
allowed
us
estimate
individual
buildings
worldwide,
culminating
creation
three-dimensional
(3D)
Global
Building
Footprints
(3D-GloBFP)
dataset
year
2020.
evaluation
results
show
that
perform
exceptionally
well
R2
values
ranging
from
0.66
0.96
root-mean-square
errors
(RMSEs)
1.9
14.6
m
33
subregions.
Comparisons
other
demonstrate
3D-GloBFP
closely
matches
distribution
pattern
reference
heights.
derived
3D
shows
distinct
regions,
countries,
cities,
gradually
decreasing
city
center
surrounding
rural
areas.
Furthermore,
our
findings
indicate
disparities
built-up
infrastructure
(i.e.,
volume)
different
countries
cities.
China
country
most
intensive
total
(5.28×1011
m3,
accounting
23.9
%
total),
followed
USA
(3.90×1011
17.6
total).
Shanghai
has
largest
volume
(2.1×1010
m3)
all
representative
The
building-footprint-scale
reveals
significant
heterogeneity
environments,
providing
valuable
dynamics
climatology.
available
https://doi.org/10.5281/zenodo.11319912
(Building
Americas,
Africa,
Oceania
3D-GloBFP;
Che
et
al.,
2024c),
https://doi.org/10.5281/zenodo.11397014
Asia
2024a),
https://doi.org/10.5281/zenodo.11391076
Europe
2024b).
Abstract
Accurate
building
extraction
is
crucial
for
urban
understanding,
but
it
often
requires
a
substantial
number
of
samples.
While
some
datasets
are
available
model
training,
there
remains
lack
high-quality
covering
and
rural
areas
in
China.
To
fill
this
gap,
study
creates
high-resolution
GaoFen-7
(GF-7)
Building
dataset
utilizing
the
Chinese
GF-7
imagery
from
six
cities.
The
comprises
5,175
pairs
512
×
image
tiles,
573.17
km
2
.
It
contains
170,015
buildings,
with
84.8%
buildings
15.2%
areas.
usability
has
been
proved
seven
convolutional
neural
networks,
all
achieving
an
overall
accuracy
(OA)
exceeding
93%.
Experiments
have
shown
that
can
be
used
scenarios.
proposed
boasts
high
quality
diversity.
supplements
existing
will
contribute
to
promoting
new
algorithms
extraction,
as
well
facilitating
intelligent
interpretation
International Journal of Applied Earth Observation and Geoinformation,
Год журнала:
2024,
Номер
130, С. 103870 - 103870
Опубликована: Май 15, 2024
Although
the
photon
point
cloud
data
acquired
from
ICESat-2/ATLAS
can
be
efficiently
employed
in
urban
building
height
extraction,
its
universal
applicability
undulating
terrain
scenarios
is
constrained,
and
there
are
noticeable
issues
of
false
positives
negatives.
This
research
establishes
a
terrain-adaptive
methodological
framework
based
on
to
extract
high-precision,
high-density
across
varied
topographical
conditions.
First,
elevation
buffer
utilized
coarse
denoise
cloud,
involving
removal
majority
noise
photons
scene,
thereby
enhancing
efficiency
subsequent
algorithms.
Second,
signal
extracted
remaining
original
using
Adaptive
Method
Based
Single-Photon
Spatial
Distribution
(SPSD-AM).
approach
demonstrates
high
universality
various
scenes,
while
simultaneously
ensuring
stable
accuracy
extraction.
Subsequently,
ground
fit
curve
Differences
Urban
Signal
Photons
(USPSD-AM),
which
addresses
challenge
potential
mixing
complex
scenarios.
A
precise
then
photons.
In
order
mitigate
such
as
negatives,
post-processing
steps,
including
completion
denoising
photons,
implemented.
Finally,
adopted
accurate
parameters.
The
precision
verification
results
show
that
heights
considerably
consistent
with
reference
heights.
mean
RMSE
MAE
0.273
m
0.202
for
flat
terrains
1.168
0.759
terrains,
respectively.
proposed
method
superior
diverse
scenarios,
providing
robust
theoretical
foundation
large-scale
retrieval
efforts.
