IEEE GRSM DRAFT 2018 1
Multisource and Multitemporal Data Fusion in
Remote Sensing
Pedram Ghamisi, Senior Member, IEEE, Behnood Rasti, Member, IEEE, Naoto Yokoya, Member, IEEE,
Qunming Wang, Bernhard H
¨
ofle, Lorenzo Bruzzone, Fellow, IEEE, Francesca Bovolo, Senior Member, IEEE,
Mingmin Chi, Senior Member, IEEE, Katharina Anders, Richard Gloaguen,
Peter M. Atkinson, and J
´
on Atli Benediktsson, Fellow, IEEE
Abstract—The final version of the paper can be found in IEEE
Geoscience and Remote Sensing Magazine.
The sharp and recent increase in the availability of data
captured by different sensors combined with their considerably
heterogeneous natures poses a serious challenge for the effective
and efficient processing of remotely sensed data. Such an increase
in remote sensing and ancillary datasets, however, opens up the
possibility of utilizing multimodal datasets in a joint manner to
further improve the performance of the processing approaches
with respect to the application at hand. Multisource data fusion
has, therefore, received enormous attention from researchers
worldwide for a wide variety of applications. Moreover, thanks
to the revisit capability of several spaceborne sensors, the
integration of the temporal information with the spatial and/or
spectral/backscattering information of the remotely sensed data
is possible and helps to move from a representation of 2D/3D
data to 4D data structures, where the time variable adds new
information as well as challenges for the information extraction
algorithms. There are a huge number of research works dedicated
to multisource and multitemporal data fusion, but the methods
for the fusion of different modalities have expanded in different
paths according to each research community. This paper brings
together the advances of multisource and multitemporal data
fusion approaches with respect to different research communities
and provides a thorough and discipline-specific starting point
for researchers at different levels (i.e., students, researchers, and
The work of P. Ghamisi is supported by the ”High Potential Program” of
Helmholtz-Zentrum Dresden-Rossendorf.
P. Ghamisi and R. Gloaguen are with the Helmholtz-Zentrum
Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource
Technology (HIF), Exploration, D-09599 Freiberg, Germany (emails:
p.ghamisi@gmail.com,r.gloaguen@hzdr.de).
B. Rasti is with the Faculty of Electrical and Computer Engineering,
University of Iceland, 107 Reykjavik, Iceland (email: behnood@hi.is).
N. Yokoya is with the RIKEN Center for Advanced Intelligence Project,
RIKEN, 103-0027 Tokyo, Japan (e-mail: naoto.yokoya@riken.jp).
Q. Wang is with the College of Surveying and Geo-Informatics,
Tongji University, 1239 Siping Road, Shanghai 200092, China (email:
wqm11111@126.com).
B. H
¨
ofle and K. Anders are with GIScience at the Institute of Geog-
raphy, Heidelberg University, Germany (emails: hoefle@uni-heidelberg.de,
katharina.anders@uni-heidelberg.de).
L. Bruzzone is with the department of Information Engineering
and Computer Science, University of Trento, Trento, Italy (email:
lorenzo.bruzzone@unitn.it).
F. Bovolo is with the Center for Information and Communication Technol-
ogy, Fondazione Bruno Kessler, Trento, Italy (email: bovolo@fbk.eu).
M. Chi is with the school of Computer Science, Fudan University, China
(email: mmchi@fudan.edu.cn).
P. M. Atkinson is with Lancaster Environment Centre, Lancaster University,
Lancaster, U.K (email: pma@lancaster.ac.uk).
J. A. Benediktsson is with the Faculty of Electrical and Computer Engineer-
ing, University of Iceland, 107 Reykjavik, Iceland (e-mail: benedikt@hi.is).
Manuscript received 2018.
senior researchers) willing to conduct novel investigations on this
challenging topic by supplying sufficient detail and references.
More specifically, this paper provides a bird’s-eye view of many
important contributions specifically dedicated to the topics of
pansharpening and resolution enhancement, point cloud data
fusion, hyperspectral and LiDAR data fusion, multitemporal data
fusion, as well as big data and social media. In addition, the
main challenges and possible future research for each section
are outlined and discussed.
Index Terms—Fusion; Multisensor Fusion; Multitemporal Fu-
sion; Downscaling; Pansharpening; Resolution Enhancement;
Spatio-Temporal Fusion; Spatio-Spectral Fusion; Component
Substitution; Multiresolution Analysis; Subspace Representation;
Geostatistical Analysis; Low-Rank Models; Filtering; Composite
Kernels; Deep Learning.
I. INTRODUCTION
The number of data produced by sensing devices has
increased exponentially in the last few decades, creating the
“Big Data” phenomenon, and leading to the creation of the
new field of “data science”, including the popularization of
“machine learning” and “deep learning” algorithms to deal
with such data [1]–[3]. In the field of remote sensing, the
number of platforms for producing remotely sensed data has
similarly increased, with an ever-growing number of satellites
in orbit and planned for launch, and new platforms for
proximate sensing such as unmanned aerial vehicles (UAVs)
producing very fine spatial resolution data. While optical
sensing capabilities have increased in quality and volume,
the number of alternative modes of measurement has also
grown including, most notably, airborne light detection and
ranging (LiDAR) and terrestrial laser scanning (TLS), which
produce point clouds representing elevation, as opposed to
images [4]. The number of synthetic aperture radar (SAR)
sensors, which measure RADAR backscatter, and satellite and
airborne hyperspectral sensors, which extend optical sensing
capabilities by measuring in a larger number of wavebands,
has also increased greatly [5], [6]. Airborne and spaceborne
geophysical measurements such as the satellite mission Grav-
ity Recovery And Climate Experiment (GRACE) or airborne
electro-magnetic surveys are currently been also considered.
In addition, there has been great interest in new sources of
ancillary data, for example, from social media, crowd sourcing,
scraping the internet and so on ([7]–[9]). These data have a
very different modality to remote sensing data, but may be
arXiv:1812.08287v1 [cs.LG] 19 Dec 2018
IEEE GRSM DRAFT 2018 2
related to the subject of interest and, therefore, may add useful
information relevant to specific problems.
The remote sensors onboard the above platforms may vary
greatly in multiple dimensions; for example, the types of
properties sensed and the spatial and spectral resolutions of
the data. This is true, even for sensors that are housed on the
same platform (e.g., the many examples of multispectral and
panchromatic sensors) or that are part of the same satellite
configuration (e.g., the European Space Agency’s (ESA’s)
series of Medium Resolution Imaging Spectrometer (MERIS)
sensors). The rapid increase in the number and availability of
data combined with their deeply heterogeneous natures creates
serious challenges for their effective and efficient processing
([10]). For a particular remote sensing application, there are
likely to be multiple remote sensing and ancillary datasets
pertaining to the problem and this creates a dilemma; how
best to combine the datasets for maximum utility? It is for this
reason that multisource data fusion, in the context of remote
sensing, has received so much attention in recent years [10]–
[13].
Fortunately, the above increase in the number and het-
erogeneity of data sources (presenting both challenge and
opportunity) has been paralleled by increases in computing
power, by efforts to make data more open, available and
interoperable, and by advances in methods for data fusion,
which are reviewed here [15]. There exist a very wide range
of approaches to data fusion (e.g., [11]–[13]). This paper
seeks to review them by class of data modality (e.g., optical,
SAR, laser scanning) because methods for these modalities
have developed somewhat differently, according to each re-
search community. Given this diversity, it is challenging to
synthesize multisource data fusion approaches into a single
framework, and that is not the goal here. Nevertheless, a
general framework for measurement and sampling processes
(i.e., forward processes) is now described briefly to provide
greater illumination of the various data fusion approaches
(i.e., commonly inverse processes or with elements of inverse
processing) that are reviewed in the following sections. Due to
the fact that the topic of multisensor data fusion is extremely
broad and that specific aspects have been reviewed already
we have to restrict what is covered in the manuscript and,
therefore, do not address a few topics such as the fusion of
SAR and optical data.
We start by defining the space and properties of interest.
In remote sensing, there have historically been considered to
be four dimensions in which information is provided. These
are: spatial, temporal, spectral, and radiometric; that is, 2D
spatially, 1D temporally, and 1D spectrally with “radiometric”
referring to numerical precision. The electromagnetic spectrum
(EMS) exists as a continuum and, thus, lends itself to high-
dimensional feature space exploration through definition of
multiple wavebands (spectral dimension). LiDAR and TLS,
in contrast to most optical and SAR sensors, measure a
surface in 3D spatially. Recent developments in photo- and
radargrammetry such as Structure from Motion (SfM) and
InSAR, have increased the availability of 3D data. This
expansion of the dimensionality of interest to 3D in space
and 1D in time makes image and data fusion additionally
challenging [4]. The properties measured in each case vary,
with SAR measuring backscatter, optical sensors (including
hyperspectral) measuring the visible and infrared parts of
the EMS, and laser scanners measuring surface elevation in
3D. Only surface elevation is likely to be a primary interest,
whereas reflectance and backscatter are likely to be only
indirectly related to the property of interest.
Secondly, we define measurement processes. A common
“physical model” in remote sensing is one of four component
models: scene model, atmosphere model, sensor model, and
image model [16]–[21]. The scene model defines the subject
of interest (e.g., land cover, topographic surface), while the
atmosphere model is a transform of the EMS from surface
to sensor, the sensor model represents a measurement process
(e.g., involving a signal-to-noise ratio, the point spread func-
tion) and the image model is a sampling process (e.g., to create
the data as an image of pixels on a regular grid).
Third, the sampling process implied by the image model
above can be expanded and generalized to three key pa-
rameters (the sampling extent, the sampling scheme, and the
sampling support), each of which has four further parameters
(size, geometry, orientation, and position). The support is a
key sampling parameter which defines the space on which
each observation is made; it is most directly related to the
point spread function in remote sensing, and is represented
as an image pixel [22]. The combination and arrangement of
pixels as an image defines the spatial resolution of the image.
Fusion approaches are often concerned with the combination
of two or more datasets with different spatial resolutions such
as to create a unified dataset at the finest resolution [23]–[25].
Fig. 1(a) demonstrates schematically the multiscale nature
(different spatial resolutions) of diverse datasets captured by
spaceborne, airborne, and UAV sensors. In principle, there is
a relation between spatial resolution and scene coverage, i.e.,
data with a coarser spatial resolution (spaceborne data) have a
larger scene coverage while data with a finer spatial resolution
have a limited coverage (UAV data).
All data fusion methods attempt to overcome the above
measurement and sampling processes, which fundamentally
limit the amount of information transferring from the scene to
any one particular dataset. Indeed, in most cases of data fusion
in remote sensing the different datasets to be fused derive in
different ways from the same scene model, at least as defined
in a specific space-time dimension and with specific measur-
able properties (e.g., land cover objects, topographic surface).
Understanding these measurement and sampling processes is,
therefore, key to characterizing methods of data fusion since
each operates on different parts of the sequence from scene
model to data. For example, it is equally possible to perform
the data fusion process in the scene space (e.g., via some data
generating model such as a geometric model) as in the data
space (the more common approach) [21].
Finally, we define the “statistical model” framework as
including: (i) measurement to provide data, as described above,
(ii) characterization of the data through model fitting, (iii)
prediction of unobserved data given (ii), and (iv) forecasting
[26]. (i), (ii), and (iii) are defined in space or space-time, while
(iv) extends through time beyond the range of the current data.
IEEE GRSM DRAFT 2018 3
(a)
(c)
(b)
(d)
200620052004200320022001
RGBUrban
Fig. 1: (a) The multiscale nature of diverse datasets captured by multisensor data (spaceborne, airborne, and UAV sensors) in
Nambia [14]; (b) The trade-off between spectral and spatial resolutions; (c) Elevation information obtained by LiDAR sensors
from the University of Houston; (d) Time-series data analysis for assessing the dynamic of changes using RGB and urban
images captured from 2001 to 2006 in Dubai.
Prediction (iii) can be of the measured property x (e.g., re-
flectance or topographic elevation, through interpolation) or it
can be of a property of interest y to which the measured x data
are related (e.g., land cover or vegetation biomass, through
classification or regression-type approaches). Similarly, data
fusion can be undertaken on x or it can be applied to predict
y from x. Generally, therefore, data fusion is applied either
between (ii) and (iii) (e.g., fusion of x based on the model in
(ii)), as part of prediction (e.g., fusion to predict y) or after
prediction of certain variables (e.g., ensemble unification). In
this paper, the focus is on data fusion to predict x.
Data fusion is made possible because each dataset to be
fused represents a different view of the same real world defined
in space and time (generalized by the scene model), with
each view having its own measurable properties, measurement
processes, and sampling processes. Therefore, crucially, one
should expect some level of coherence between the real world
(the source) and the multiple datasets (the observations), as
well as between the datasets themselves, and this is the basis
of most data fusion methods. This concept of coherence is
central to data fusion [27].
Attempts to fuse datasets are potentially aided by knowledge
of the structure of the real world. The real world is spatially
correlated, at least at some scale [28] and this phenomenon
has been used in many algorithms (e.g., geostatistical models
[27]). Moreover, the real world is often comprised of func-
tional objects (e.g., residential houses, roads) that have expec-
tations around their sizes and shapes, and such expectations
can aid in defining objective functions (i.e., in optimization
solutions) [29]. These sources of prior information (on real
world structure) constrain the space of possible fusion solu-
tions beyond the data themselves.
Many key application domains stand to benefit from data fu-
sion processing. For example, there exists a very large number
of applications where an increase in spatial resolution would
add utility, which is the center of focus in Section II of this pa-
per. These include land cover classification, urban-rural defini-
tion, target identification, geological mapping, and so on (e.g.,
[30]). A large focus of attention currently is on the specific
problem that arises from the trade-off in remote sensing be-
tween spatial resolution and temporal frequency; in particular
the fusion of coarse-spatial-fine-temporal-resolution with fine-
spatial-coarse-temporal-resolution space-time datasets such as
to provide frequent data with fine spatial resolution [31]–[34],
which will be detailed in Section II and V of this paper. Land
cover classification is one of the most vibrant fields of research
in the remote sensing community [35], [36], which attempts
to differentiate between several land cover classes available in
the scene, can substantially benefit from data fusion. Another
example is the trade-off between spatial resolution and spectral
resolution (Fig. 1(b)) to produce fine-spectral-spatial resolution
images, which plays an important role for land cover classifica-
tion and geological mapping. As can be seen in Fig. 1(b), both
fine spectral and spatial resolutions are required to provide
detailed spectral information and avoid the “mixed-pixel”
phenomenon at the same time. Further information about
this topic can be found in Section II. Elevation information
provided by LiDAR and TLS (see Fig. 1(c)) can be used
in addition to optical data to further increase classification
and mapping accuracy, in particular for classes of objects,
which are made up of the same materials (e.g., grassland,
shrubs, and trees). Therefore, Sections III and IV of this paper
are dedicated to the topic of elevation data fusion and their
integration with passive data. Furthermore, new sources of
ancillary data obtained from social media, crowd sourcing,
and scraping the internet can be used as additional sources
of information together with airborne and spaceborne data
for smart city and smart environment applications as well as
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hazard monitoring and identification. This young, yet active,
field of research is the focus of Section VI.
Many applications can benefit from fused fine-resolution,
time-series datasets, particularly those that involve seasonal
or rapid changes, which will be elaborated in Section V.
Fig. 1(d) shows the dynamic of changes for an area in Dubai
from 2001 to 2006 using time-series of RGB and urban
images. For example, monitoring of vegetation phenology (the
seasonal growing pattern of plants) is crucial to monitoring
deforestation [37] and crop yield forecasting, which mitigates
against food insecurity globally, natural hazards (e.g. earth-
quakes, landslides) or illegal activities such as pollutions (e.g.
oil spills, chemical leakages). However, such information is
provided globally only at very coarse resolution, meaning that
local smallholder farmers cannot benefit from such knowledge.
Data fusion can be used to provide frequent data needed for
phenology monitoring, but at a fine spatial resolution that
is relevant to local farmers [38]. Similar arguments can be
applied to deforestation where frequent, fine resolution data
may aid in speeding up the timing of government interventions
[37], [39]. The case for fused data is arguably even greater
for rapid change events; for example, forest fires and floods.
In these circumstances, the argument for frequent updates at
fine resolution is obvious. While these application domains
provide compelling arguments for data fusion, there exist
many challenges including: (i) the data volumes produced at
coarse resolution via sensors such as MODIS and MERIS
are already vast, meaning that fusion of datasets most likely
needs to be undertaken on a case-by-case basis as an on-
demand service and (ii) rapid change events require ultra-fast
processing meaning that speed may outweigh accuracy in such
cases [40]. In summary, data fusion approaches in remote
sensing vary greatly depending on the many considerations
described above, including the sources of the datasets to
be fused. In the following sections, we review data fusion
approaches in remote sensing according to the data sources to
be fused only, but the further considerations introduced above
are relevant in each section.
The remainder of this review is divided into the following
sections. First, we review pansharpening and resolution en-
hancement approaches in Section II. Then, we will move on
by discussing point cloud data fusion in Section III. Section
IV is devoted to hyperspectral and LiDAR data fusion. Section
V presents an overview of multitemporal data fusion. Major
recent advances in big data and social media fusion are pre-
sented in Section IV. Finally, Section VII draws conclusions.
II. PANSHARPENING AND RESOLUTION ENHANCEMENT
Optical Earth observation satellites have trade-offs in spa-
tial, spectral, and temporal resolutions. Enormous efforts have
been made to develop data fusion techniques for reconstructing
synthetic data that have the advantages of different sensors.
Depending on which pair of resolutions has a tradeoff, these
technologies can be divided into two categories: (1) spatio-
spectral fusion to merge fine-spatial and fine-spectral reso-
lutions [see Fig. 2(a)]; (2) spatio-temporal fusion to blend
fine-spatial and fine-temporal resolutions [see Fig. 2(b)]. This
Fig. 2: Schematic illustrations of (a) spatio-spectral fusion and
(b) spatio-temporal fusion.
section provides overviews of these technologies with recent
advances.
A. Spatio-spectral fusion
Satellite sensors such as WorldView and Landsat ETM+ can
observe the Earth’s surface at different spatial resolutions in
different wavelengths. For example, the spatial resolution of
the eight-band WorldView multispectral image is 2 m, but the
single band panchromatic (PAN) image has a spatial resolution
of 0.5 m. Spatio-spectral fusion is a technique to fuse the fine
spatial resolution images (e.g., 0.5 m WorldView PAN image)
with coarse spatial resolution images (e.g., 2 m WorldView
multispectral image) to create fine spatial resolution images for
all bands. Spatio-spectral fusion is also termed pan-sharpening
when the available fine spatial resolution image is a single
PAN image. When multiple fine spatial resolution bands are
available, spatio-spectral fusion is referred to as multiband im-
age fusion, where two optical images with a trade-off between
spatial and spectral resolutions are fused to reconstruct fine-
spatial and fine-spectral resolution imagery. Multiband image
fusion tasks include multiresolution image fusion of single-
satellite multispectral data (e.g., MODIS and Sentinel-2) and
hyperspectral and multispectral data fusion [41].
IEEE GRSM DRAFT 2018 5
CS
MRA
Geostatistical
Subspace
Sparse
Fig. 3: The history of the representative literature of five
approaches in spatio-spectral fusion. The size of each cir-
cle is proportional to the annual average number of ci-
tations. For each category, from left to right, circles cor-
respond to [42]–[50] for CS, [51]–[57] for MRA, [58]–
[61], [27], [62], [31], [63] for Geostatistical, [64]–[69] for
Subspace, and [70]–[72] for Sparse.
Over the past decades, spatio-spectral fusion has motivated
considerable research in the remote sensing community. Most
spatio-spectral fusion techniques can be categorized into at
least one of five approaches: 1) component substitution (CS),
2) multiresolution analysis (MRA), 3) geostatistical analysis,
4) subspace representation, and 5) sparse representation. Fig. 3
shows the history of representative literature with different col-
ors (or rows) representing different categories of techniques.
The size of each circle is proportional to the annual average
number of citations (obtained by Google Scholar on January
20, 2018), which indicates the impact of each approach in the
field. The main concept and characteristics of each category
are described below.
1) Component Substitution: CS-based pan-sharpening
methods spectrally transform the multispectral data into an-
other feature space to separate spatial and spectral information
into different components. Typical transformation techniques
include intensity-hue-saturation (IHS) [44], principal compo-
nent analysis (PCA) [43], and Gram-Schmidt [46] transfor-
mations. Next, the component that is supposed to contain the
spatial information of the multispectral image is substituted
by the PAN image after adjusting the intensity range of
the PAN image to that of the component using histogram
matching. Finally, the inverse transformation is performed on
the modified data to obtain the sharpened image.
Aiazzi et al. (2007) proposed the general CS-based pan-
sharpening framework, where various methods based on dif-
ferent transformation techniques can be explained in a unified
way [48]. In this framework, each multispectral band is
sharpened by injecting spatial details obtained as the differ-
ence between the PAN image and a coarse-spatial-resolution
synthetic component multiplied by a band-wise modulation
coefficient. By creating the synthetic component based on
linear regression between the PAN image and the multispectral
image, the performances of traditional CS-based techniques
were greatly increased, mitigating spectral distortion.
CS-based fusion techniques have been used widely owing
to the following advantages: i) high fidelity of spatial details
in the output, ii) low computational complexity, and iii)
robustness against misregistration. On the other hand, the
CS methods suffer from global spectral distortions when the
overlap of spectral response functions (SRFs) between the two
sensors is limited.
2) Multiresolution Analysis: As shown in Fig. 3, great
effort has been devoted to the study of MRA-based pan-
sharpening algorithms particularly between 2000 and 2010
and they have been used widely as benchmark methods for
more than ten years. The main concept of MRA-based pan-
sharpening methods is to extract spatial details (or high-
frequency components) from the PAN image and inject the
details multiplied by gain coefficients into the multispectral
data. MRA-based pan-sharpening techniques can be charac-
terized by 1) the algorithm used for obtaining spatial details
(e.g., spatial filtering or multiscale transform), and 2) the
definition of the gain coefficients. Representative MRA-based
fusion techniques are based on box filtering [54], Gaussian
filtering [56], bilateral filtering [73], wavelet transform [53],
[55], and curvelet transform [57]. The gain coefficients can be
computed either locally or globally.
Selva et al. (2015) proposed a general framework called hy-
persharpening that extends MRA-based pan-sharpening meth-
ods to multiband image fusion by creating a fine spatial
resolution synthetic image for each coarse spatial resolution
band as a linear combination of fine spatial resolution bands
based on linear regression [74].
The main advantage of the MRA-based fusion techniques is
its spectral consistency. In other words, if the fused image is
degraded in the spatial domain, a degraded image is spectrally
consistent with the input coarse-spatial and fine-spectral reso-
lution image. The main shortcoming is that its computational
complexity is greater than that of CS-based techniques.
3) Geostatistical Analysis: Geostatistical solutions provide
another family of approaches for spatio-spectral fusion. This
type of approach can preserve the spectral properties of the
original coarse images. That is, when the downscaled predic-
tion is upscaled to the original coarse spatial resolution, the
result is identical to the original one (i.e., perfect coherence).
Pardo-Iguzquiza et al. [58] developed a downscaling cokriging
(DSCK) method to fuse the Landsat ETM+ multispectral
images with the PAN image. DSCK treats each multispectral
image as the primary variable and the PAN image as the
secondary variable. DSCK was extended with a spatially
adaptive filtering scheme [60], in which the cokriging weights
are determined on a pixel basis, rather than being fixed in
the original DSCK. Atkinson et al. [59] extended DSCK to
downscaled the multispectral bands to a spatial resolution finer
than any input images, including the PAN image. DSCK is
a one-step method, and it involves auto-semivariogram and
cross-semivariogram modeling for each coarse band [61].
Sales et al. [61] developed a kriging with external drift
(KED) method to fuse 250 m Moderate Resolution Imaging
Spectroradiometer (MODIS) bands 1-2 with 500 m bands
3-7. KED requires only auto-semivariogram modeling for
the observed coarse band and simplifies the semivariogram
modeling procedure, which makes it easier to implement
than DSCK. As admitted in Sales et al. [61], however, KED
suffers from expensive computational cost, as it computes
![Fig. 9: Low-rank models. The use of DR2 is optional. However, the studies investigated in [136], [142], [138] and [143] recommend the consideration of this extra extra step in order to provide more accurate classification maps.](/figures/fig-9-low-rank-models-the-use-of-dr2-is-optional-however-the-2vu59at2.webp){kind=icon}
![Fig. 1: (a) The multiscale nature of diverse datasets captured by multisensor data (spaceborne, airborne, and UAV sensors) in Nambia [14]; (b) The trade-off between spectral and spatial resolutions; (c) Elevation information obtained by LiDAR sensors from the University of Houston; (d) Time-series data analysis for assessing the dynamic of changes using RGB and urban images captured from 2001 to 2006 in Dubai.](/figures/fig-1-a-the-multiscale-nature-of-diverse-datasets-captured-36fcn6db.webp)