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tendency toward confirmation
bias (e.g., with attention maps) Models that are more understandable and
therefore more reliable and trustworthy;
models that can be queried (and
challenged) by humans about their inner
reasoning.
6 Learn cause-and-effect
relationships—not just correla -
tions—from observations and
assumptions about the
underlying generating process
and system.[19]–[22] Models cannot work with
unevenly sampled time series
or nonstationary/noisy
processes; they extrapolate
poorly.Machines that automatically blend domain
knowledge, observational data, and as -
sumptions to learn the causal graph and
generate causal-narrative explanations of
the problem.
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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2021 knowledge. Here, the transparency of the model is achieved
by construction because the product is a self-explanatory
causal graph. All three fields (explainability, hybrid model -
ing, and causal inference) also have in common the need
of an active and tight interaction among domain experts
and computer scientists to make a decisive, nonincremen -
tal leap in Earth sciences.
With this position article, we present six research direc -
tions that we think hold particular promise for the future of
Earth observation data analysis. In the following, we argue
their potential and relevance and provide some pointers to
pertinent resources. Our goal is to trigger curiosity and fos -
ter successful research to truly advance the field of Earth
sciences with AI.
DIRECTION 1: REASONING AND
HUMAN–MACHINE DIALOGS
Current research on the interface between ML and remote
sensing focuses largely on the direct recognition of mate -
rials and objects or on estimating geophysical parameters.
Reasoning goes beyond the concept of recognition and
aims at mimicking how people think and learn. It is cen -
tered around tasks such as induction, deduction, spatial
and temporal reasoning, and structural inference [ 24].
To date, only a few pioneering studies have been pub -
lished on reasoning for remote sensing tasks. In computer
vision, reasoning is mostly interpreted as the capability to
link meaningful transformations of entities over space or
time. This is a fundamental property of intelligent species
and is also the way people understand visual data. Recent -
ly, papers implementing reasoning in convolutional neural
networks (CNNs) have started to appear. Santoro et al. pro -
posed a relational reasoning network as a simple plug-and-
play module to solve problems requiring the understand -
ing of arbitrary relationships between objects (ordering or
comparisons of relative positions/sizes) and applied it to
the problem of visual question answering (VQA) [ 25] (for
more information on VQA, see direction 3 in Table 1 ). A
second pioneering work concerns temporal relationships
in video sequences. When it comes to understanding what
takes place between two sampled video frames, humans
can easily infer the temporal relationships and transfor -
mations between observations, unlike neural networks.
In [26], the authors proposed a temporal relationship net -
work, which learns intuitive and interpretable common-
sense knowledge in videos.
WHY SHOULD RELATIONAL REASONING
MATTER IN REMOTE SENSING?
Earth observation images carry strong spatial and temporal
information because each pixel is precisely referenced and
connected to neighbors in space and time. When consider -
ing land processes (and, in particular, the geophysical ones),
relevant relationships can be learned by using models. In
[6], the authors explicitly modeled long-range relationships
for semantic segmentation in aerial scenes. With the goal of increasing the representation capacity of a fully convo -
lutional network, two tailored relationship modules were
used: one describing relationships between observations
in convolved images and another producing relationship-
augmented feature representations. Given that convolutions
operate by blending spatial and cross-channel information
together, they captured relationships in both the spatial and
channel domains.
PERSPECTIVES
The work mentioned in the previous section showcases how
spatial-relational reasoning helps with improving semantic
understanding of remote sensing images, and many other
problems may also benefit from visual reasoning. One excit -
ing example is temporal reasoning for the analysis of multi -
temporal data/aerial videos, e.g., for event recognition. This
is a new and exciting field where one is concerned with un -
derstanding complex events being imaged or filmed, such
as cultural events, manifestations, or locating people in dis -
tress. Using reasoning enables knowing whether a person
on a roof during a flood is in need of actual help or whether
the video of a crowd of people is related to a peaceful or
violent demonstration. This could be of interest to various
stakeholders, including local authorities. To foster this re -
search direction, Mou et al. [ 7] have introduced the data set
Event Recognition in Aerial videos (ERA), which consists of