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.
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
grid
scale,
often
resulting
in
with
limited
coverage
or
spatial
resolution.
This
limitation
hampers
comprehensive
global
analyses
ability
to
generate
actionable
insights
finer
scales.
In
study,
we
developed
height
map
(3D-GloBFP)
at
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
first
three-dimensional
(3-D)
footprints
(3D-GloBFP).
evaluation
results
show
that
perform
exceptionally
well
worldwide
R2
ranging
from
0.66
0.96
root
mean
square
errors
(RMSEs)
1.9
m
14.6
33
subregions.
Comparisons
other
demonstrate
our
3D-GloBFP
closely
matches
distribution
pattern
reference
heights.
derived
3-D
shows
distinct
regions,
countries,
cities,
gradually
decreasing
city
center
surrounding
rural
areas.
Furthermore,
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
United
States
(3.90×1011
17.6
total).
Shanghai
has
largest
volume
(2.1×1010
m3)
all
representative
The
building-footprint
reveals
significant
heterogeneity
environments,
providing
valuable
dynamics
climatology.
dataset
available
https://doi.org/10.5281/zenodo.11319913
(Building
Americas,
Africa,
Oceania
3D-GloBFP)
(Che
et
al.,
2024a),
https://doi.org/10.5281/zenodo.11397015
Asia
2024b),
https://doi.org/10.5281/zenodo.11391077
Europe
2024c).
International Journal of Applied Earth Observation and Geoinformation,
Год журнала:
2023,
Номер
124, С. 103495 - 103495
Опубликована: Сен. 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.
Remote Sensing,
Год журнала:
2023,
Номер
15(15), С. 3786 - 3786
Опубликована: Июль 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.
