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gremlin97
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RemoteSensingCorpus

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PresenceComparison
Area
Rural/UrbanQuestions catalog
(a) Question construction procedure. Dash lines represent optional paths
Road"How many roads are present in the image?"
"Is there a small retail place?"
"Is there more buildings at the top of a circular religious place than roads in the image?"Road Count Base question
Retail Size Presence Base question
Building
Religious ShapePositional
Comparison Base question
(b) Construction path for sample questions.
Fig. 2. Illustration of the question construction procedure.
generate remote sensing-oriented VQA datasets from already
available human annotations in section II and generate two
datasets. We then use this newly-generated data to train
our proposed RSVQA model with a non-spatial fusion step
described in section III. Finally, the results are evaluated and
discussed in section IV.
Our contribution are the following:
•a method to generate remote sensing-oriented VQA
datasets;
•2 datasets;
•the proposed RSVQA model.
This work extends the preliminary study of [6] by considering
and disclosing a second larger dataset consisting of very high
resolution images. This second dataset helps testing the spatial
generalization capability of VQA and provides an extensive
discussion highlighting remaining challenges. The method to
generate the dataset, the RSVQA model and the two datasets
are available on https://rsvqa.sylvainlobry.com/.
II. D ATASETS
A. Method
As seen in the introduction, a main limiting factor for
VQA is the availability of task-specific datasets. As such,
we aim at providing a collection of remote sensing images
with questions and answers associated to them. To do so,
we took inspiration from [31], in which the authors build
a dataset of question/answer pairs about synthetic images
following an automated procedure. However, in this study
we are interested in real data (discussed in subsection II-B).
Therefore, we use the openly accessible OpenStreetMap data
containing geo-localized information provided by volunteers.
By leveraging this data, we can automatically extract theinformation required to obtain question/answer pairs relevant
to real remotely sensed data and create a dataset made of
(image, question, answer) triplets.
The first step of the database construction is to create
the questions. The second step is to compute the answers
to the questions, using the OSM features belonging to the
image footprint. Note that multiple question/answer pairs are
extracted for each image.
1) Question contruction: Our method to construct the
questions is illustrated in Figure 2. It consists of four main
components:
1) choice of an element category (highlighted in red in
Figure 2(a));
2) application of attributes to the element (highlighted in
green in Figure 2(a));
3) selection based on the relative location to another ele-
ment (highlighted in green in Figure 2(a))
4) construction of the question (highlighted in blue in
Figure 2(a)).
Examples of question constructions are shown in Figure 2(b).
These four components are detailed in the following.
Element category selection : First, an element category is ran-
domly selected from the element catalog. This catalog is built
by extracting the elements from one of the following OSM
layers: road,water area ,building andland use . While roads
and water areas are directly treated as elements, buildings
and land use related objects are defined based on their ”type”
field, as defined in the OSM data specification. Examples of
land use objects include residential area, construction area,
religious places, . . . Buildings are divided in two categories:
commercial (e.g. retail, supermarket, ...) and residential (e.g.
house, apartments, . . . ).
Attributes application : The second (optional) step is to
refine the previously selected element category. To do so,
we randomly select from one of the two possible attribute
categories:
•Shape : each element can be either square, rectangular
or circular. Whether an element belongs to one of these
shape types is decided based on basic geometrical prop-
erties (i.e. hard thresholds on area-to-perimeter ratio and
area-to-circumscribed circle area ratio).
•Size: using hard thresholds on the surface area, elements
can be considered ”small”, ”medium” or ”large”. As we
are interested in information at different scales in the
two datasets, we use different threshold values, which
are described in Table I.
Relative position : Another possibility to refine the element is
to look at its relative position compared to another element.
We define 5 relations: ”left of”, ”top of”, ”right of”, ”bottom
of”, ”next to”. Note that these relative positions are understood
in the image space (i.e. geographically). The special case of
PRE-PRINT. FINAL VERSION IN IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 4
Scale Small Medium Large
Low resolution <3000m2<10000m2≥10000m2
High resolution <100m2<500m2≥500m2
TABLE I
THRESHOLDS FOR SIZE ATTRIBUTES ACCORDING TO THE DATASET