IEEE Transactions on Geoscience and Remote Sensing,
Journal Year:
2024,
Volume and Issue:
62, P. 1 - 12
Published: Jan. 1, 2024
Accurate
estimation
of
building
heights
is
crucial
for
effective
urban
planning
and
resource
management
as
it
provides
essential
geometric
information
about
the
landscape.
Many
end-to-end
deep
learning-based
networks
have
been
proposed
image-to-height
mapping
using
high-resolution
non-optical
optical
remote
sensing
imagery.
In
this
study,
we
develop
a
novel
deep-learning
architecture
that
incorporates
stereo
parallax-based
mathematical
formulation
height
estimation.
We
estimate
parameters
include
differential
parallax
(ΔP)
image,
average
photo-base
(b),
satellite
(h
s
).
The
final
map
computed
by
utilizing
these
in
equation,
thus
combining
closed-form
solutions
within
learning
paradigm.
Moreover,
to
improve
ΔP,
also
introduce
multi-scale
shortcut
connections
module
(MSDSC).
MSDSC
integrates
high-frequency
components
into
lower-resolution
baseline
decoder
features
while
converting
them
features.
To
establish
efficacy
our
network
(Stereollax
Net),
train
evaluate
method
on
densely
populated
cities
China
(42-Cities
dataset)
IEEE
Data
Fusion
Contest
2018
dataset
(DFC2018).
Our
Stereollax
Net
trained
only
with
RGB
imagery
compared
state-of-the-art
methods
utilize
both
panchromatic
multi-spectral
(RGB
Near-infrared)
qualitative
quantitative
results
demonstrate
surpasses
existing
(SOTA)
algorithms,
achieving
superior
performance
fewer
data
training
considerable
margin.
code
will
be
made
publicly
available
via
GitHub
repository.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
Journal Year:
2024,
Volume and Issue:
17, P. 6514 - 6528
Published: Jan. 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.
International Journal of Applied Earth Observation and Geoinformation,
Journal Year:
2023,
Volume and Issue:
124, P. 103495 - 103495
Published: Sept. 20, 2023
The
comprehensive
characterization
of
global
urbanization
requires
consideration
both
human
activities
and
urban
physical
structures.
Both
structures
exhibit
regular
self-similar
patterns,
yet
the
spatial
patterns
between
two
at
a
scale
remain
elusive.
This
study
utilized
NPP-VIIRS
annual
composite
data
newly
available
world
settlement
footprint
3D
to
investigate
relationships
nighttime
light
intensity
building
morphological
indicators
across
several
scales.
Our
results
demonstrated
that
there
is
weak
association
morphology
pixel
level,
as
shown
by
maximum
correlation
coefficient
approximately
0.4,
but
strong
provincial/state
level
with
over
0.8.
Additionally,
we
performed
an
urban-rural
gradient
analysis
evaluate
indicators.
indicated
dominant
gradients
for
morphologies
follow
declining
trend
from
centers
rural
areas.
Notably,
inconsistencies
were
found
predominantly
in
Africa.
findings
also
suggested
can
be
served
indicator
urbanization,
thus
provide
implications
facilitating
solutions
aimed
reducing
income
disparity
promoting
sustainable
development.
ISPRS International Journal of Geo-Information,
Journal Year:
2024,
Volume and Issue:
13(3), P. 62 - 62
Published: Feb. 20, 2024
Accurate
building
geometry
information
is
crucial
for
urban
planning
in
constrained
spaces,
fueling
the
growing
demand
large-scale,
high-precision
3D
city
modeling.
Traditional
methods
like
oblique
photogrammetry
and
LiDAR
prove
time
consuming
expensive
low-cost
reconstruction
of
expansive
scenes.
Addressing
this
challenge,
our
study
proposes
a
novel
approach
to
leveraging
single-view
remote
sensing
images.
By
integrating
shadow
with
deep
learning
networks,
method
measures
height
employs
semantic
segmentation
technique
single-image
high-rise
reconstruction.
In
addition,
we
have
designed
complex
measurement
algorithms
contour
correction
improve
accuracy
models
conjunction
previous
research.
We
evaluate
method’s
precision,
efficiency,
applicability
across
various
data
sources,
scenarios,
scales.
The
results
demonstrate
rapid
accurate
acquisition
maintained
geometric
(mean
error
below
5
m).
This
offers
an
economical
effective
solution
large-scale
modeling,
bridging
gap
cost-efficient
techniques.
International Journal of Applied Earth Observation and Geoinformation,
Journal Year:
2024,
Volume and Issue:
130, P. 103870 - 103870
Published: May 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.
Remote Sensing,
Journal Year:
2023,
Volume and Issue:
15(15), P. 3786 - 3786
Published: July 30, 2023
Building
height
serves
as
an
essential
feature
of
urban
morphology
that
provides
valuable
insights
into
human
socio-cultural
behaviors
and
their
impact
on
the
environment
in
milieu.
However,
openly
accessible
building
information
at
individual
level
is
still
lacking
remains
sorely
limited.
Previous
studies
have
shown
ICESat-2′s
ATL03/08
products
are
good
accuracy
for
heights
retrieval,
however,
these
limited
to
areas
with
available
data
coverage.
To
this
end,
we
propose
a
method
extracting
by
using
ICESat-2
ATL03
photons
high-resolution
remote
sensing
images.
We
first
extracted
roof
footprint
offsets
shadows
from
high
resolution
imagery
multitasking
CNN
frameworks.
Using
samples
calculated
photons,
developed
estimation
combines
offset
shadow
length
information.
assessed
efficacy
proposed
Wujiaochang
area
Shanghai
city,
China.
The
results
indicated
able
extract
MAE
4.7
m,
outperforms
traditional
shadow-based
offset-based
method.
believe
candidate
accurately
retrieving
city-wide
scale.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(2), P. 263 - 263
Published: Jan. 9, 2024
Accurately
estimating
building
heights
is
crucial
for
various
applications,
including
urban
planning,
climate
studies,
population
estimation,
and
environmental
assessment.
However,
this
remains
a
challenging
task,
particularly
large
areas.
Satellite-based
Light
Detection
Ranging
(LiDAR)
has
shown
promise,
but
it
often
faces
difficulties
in
distinguishing
photons
from
other
ground
objects.
To
address
challenge,
we
propose
novel
method
that
incorporates
footprints,
relative
positions
of
photons,
self-adaptive
buffer
photon
selection.
We
employ
the
Ice,
Cloud,
Land
Elevation
Satellite
2
(ICESat-2)
photon-counting
LiDAR,
specifically
ICESat-2/ATL03
data,
along
with
footprints
obtained
New
York
City
(NYC)
Open
Data
platform.
The
proposed
approach
was
applied
to
estimate
17,399
buildings
NYC,
results
showed
strong
consistency
reference
heights.
root
mean
square
error
(RMSE)
8.1
m,
71%
buildings,
absolute
(MAE)
less
than
3
m.
Furthermore,
conducted
an
extensive
evaluation
thoroughly
investigated
influence
terrain,
region,
height,
density,
parameter
also
verified
effectiveness
our
experimental
area
Beijing
compared
existing
methods.
By
leveraging
ICESat-2
LiDAR
advanced
selection
techniques,
demonstrates
potential
accurately
over
broad
International Journal of Applied Earth Observation and Geoinformation,
Journal Year:
2024,
Volume and Issue:
129, P. 103809 - 103809
Published: April 5, 2024
The
building
height
holds
significant
importance
for
comprehensively
understanding
urban
morphology,
enhancing
planning,
and
fostering
sustainable
development.
Although
many
methods
using
optical
SAR
images
have
been
presented
estimation,
these
fall
short
in
capturing
the
influences
of
economic
social
attributes
on
height.
In
this
study,
we
introduced
a
Nature-Economy-Society
(NES)
feature
model
to
represent
information,
established
multi-scale
One-Dimensional
(1-D)
Convolutional
Neural
Network
predicting
heights,
referred
as
NES-CNN.
First,
derived
natural
buildings
from
time-series
Sentinel-1
Sentinel-2
multispectral
images,
well
World
Settlement
Footprint
(WSF)
data
Digital
Elevation
Model
(DEM),
nighttime
light
Gross
Domestic
Product
(GDP)
data,
function
Points
Interest
(POI)
data.
Second,
an
autoencoder
is
employed
reduce
dimensionality
high-dimensional
attribute
features,
minimizing
redundancy.
Finally,
1-D
CNN
explore
correlations
between
multi-source
heterogeneous
NES
features
facilitating
prediction
experiments,
applied
proposed
method
estimate
heights
Beijing
Shanghai
at
spatial
resolution
10
m.
results
indicated
that
Beijing,
RMSE,
MAE,
R
values
are
6.93
m,
4.41
0.84,
respectively,
while
Shanghai,
7.57
5.38
0.80,
respectively.
addition
information
decreases
RMSE
by
6
%
both
compared
with
only
attributes.
comparison
existing
studies
same
mapping
resolution,
39
51
Shanghai.
innovative
inspiring
nature
study
lies
its
application
large-scale
estimation.