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RemoteSensingCorpus

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3,000 unmanned aerial vehicle videos manually annotated
into dozens of types of events ( Figure 1 ).
In addition to videos, other interesting examples in -
clude VQA (see direction 3 in Table 1 ), captioning [ 27], and
audiovisual reasoning, i.e., linking remote sensing images
to in situ audio signals [ 28]. In the long run, we hope that
reasoning Earth observation systems will be capable of de -
ducing clues and making structural inferences to explain
processes (see direction 5 in Table 1 ) and understand causal
structures in the Earth system (see direction 6 in Table 1 ).
DIRECTION 2: EXTREMELY MULTIMODAL
REMOTE SENSING
Remote sensing is no longer restricted to observation with
airborne or satellite sensors. Today, we can monitor our
planet’s health and status with social media data and socio -
economic indicators as well as all kinds of imagery, audio,
and text, in addition to satellite imagery [ 29]. This direc -
tion raises several questions related to the importance of
the different sources and their adequacy for specific tasks.
For this research direction, we discuss some of these aspects
at the crossroads between (sometimes) extremely different
data sources.
THE TRADITIONAL APPROACH
The first step in creating a traditional remote sensing system
is to identify a property of interest y (e.g., land use or snow
depth). We typically restrict the properties to a set of loca -
tions l, often described as a grid of points within a polygon , and
times t. We can formalize this as modeling a conditional
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JUNE 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINEdistribution, (| ,), Py lt over property y for a given set of
locations and times. Using the traditional approach, one
would resort to a pixel- or object-based classifier learning
input–output relationships from a labeled set of image pix -
els. The goal is to provide an accurate estimate of the uncer -
tainty over property y and to have this be generalized to new
locations and times.
The traditional approach is limited in several ways:
1) It requires a person able to label the training satellite im -
agery, either in the field or through manual image inter -
pretation. This can be expensive and limiting, especially
when the annotator is asked label difficult, fine-grained
label spaces.
2) It is able to make distinctions only among phenomena
that are easily visible from an overhead perspective. It
cannot, for example, see inside buildings.
3) It is tightly coupled to the geographic region, task, and
source of data. This approach has led to a profusion of
remote sensing papers that use minor variations of the
same computational methods.
MULTIMODAL APPROACHES WITH SOCIAL MEDIA
Social media data can be used to address our fundamental
task, the estimation of (| ,). Py lt We begin by considering
how social media can be incorporated as an input, by using
l and t to query for nearby media content, and then how it
can be used to expand the types of properties, y, that can
be estimated.SOCIAL MEDIA AS AN ALTERNATIVE-INPUT MODALITY
Traditional remote sensing largely ignores social media and
uses only l and t to index the image; however, today count -
less photographic, audio, and textual data from cellphones
are being collected. These data are often associated with
the location and time of capture, making each a potentially
useful source of information about the state of the world
(see Figure 2 , where a system based on both remote sens -
ing and ground-level images uses both modalities in syn -
ergy to provide likelihoods about the presence of objects).
People use these data to make decisions on a daily basis
(e.g., when reading reviews and looking at photographs
while trying to make travel plans). With rapid advances in
the automatic interpretation of imagery, audio, and text, we
can start thinking of these data as potential inputs for re -
mote sensing. Recent work [ 30]–[32] has begun to explore
the use of social media imagery, especially for fine-grained
land-use classification. A new methodological framework
of information fusion with ML is actually emerging [ 33].
These methods tend to use black-box models to extract vec -
tor-valued features from ground images and combine them
with remote sensing features. They also tend to ignore the
rich geometric information the images contain.
SOCIAL MEDIA AS A SOURCE OF SUPERVISION
An untrained person can easily interpret a wide array of
properties from a single social media object. The interpreta -
tion will be fine grained and include subjective properties
FIGURE 1. An overview of the ERA data set, a benchmark for event recognition from aerial videos [7]. For each class, the first (top left) and
last (bottom right) frames of an example video are shown.
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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2021such as “dangerousness” or “scenicness,” which are gener -
ally not visible in overhead images. With CNNs approach -
ing, and sometimes exceeding, human-level performance
for ground-level image interpretation, we can now consider
using the output of CNNs as a semantic description of a
given place and time. We can then use this description to
train a remote sensing model, which might take only sat -
ellite imagery as input. In this way, we can extend the in -
formation received from social media to areas where these
media are absent and, simultaneously, reduce the need to
manually annotate satellite images. This approach has been
applied for a variety of tasks, including for mapping scenes
categories [ 34] and for time-varying visual attributes [ 35].