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gremlin97
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RemoteSensingCorpus

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substantially from a larger dataset.
Restricted set of questions:
While not appearing in the numerical evaluation, an important
issue with our results is the relative lack of diversity in the
dataset. Indeed, due to the source of our data (OSM), the
questions are only on a specific set of static objects (e.g.
buildings, roads, . . . ). Other objects of interest for applications
of a VQA system to remote sensing would also include differ-
ent static objects (e.g. thatched roofs mentioned in section I),
moving objects (e.g. cars), or seasonal aspects (e.g. for crop
monitoring). Including these objects would require another
source of data, or manual construction of question/answer
pairs.
Another limitation comes from the dataset construction
method described in subsection II-A. We defined five types
of questions (count, comparison, presence, area, rural/urban
classification). However, they only start to cover the range of
questions which would be of interest. For instance, questions
about the distance between two points (defined by textual
descriptions), segmentation questions (e.g. ”where are the
buildings in this image?”) or higher semantic level question
(e.g. ”does this area feel safe?”) could be added.
While the first limitation (due to the data source) could be
tackled using other databases (e.g. from national institutes) and
the second limitation (due to the proposed method) could be
solved by adding other question construction functions to the
model, it would be beneficial to use human annotators using
a procedure similar to [5] to diversify the samples.
V. C ONCLUSION
We introduce the task of Visual Question Answering from
remote sensing images as a generic and accessible way of ex-
tracting information from remotely sensed data. We present a
method for building datasets for VQA, which can be extended
and adapted to different data sources, and we proposed two
datasets targeting different applications. The first dataset uses
Sentinel-2 images, while the second dataset uses very high
resolution (30cm) aerial orthophotos from USGS.
We analyze these datasets using a model based on deep
learning, using both convolutional and recurrent neural
networks to analyze the images and associated questions. The
most probable answer from a predefined set is then selected.
This first analysis shows promising results, suggesting the
potential for future applications of such systems. These re-
sults outline future research directions which are needed to
overcome language biases and difficult tasks such as counting.
The former can be tackled using an attention mechanism [24],
while the latter could be tackled by using dedicated compo-
nents for counting questions [33] in a modular approach.
PRE-PRINT. FINAL VERSION IN IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 11
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(a) Test set 1
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(b) Test set 2
Fig. 11. Subsets of the confusion matrices for the high resolution dataset (counts are at logarithm scale) on both test sets. Red lines group answers by type
(”Yes/No”, areas, numbers).
Fig. 12. Evolution of the accuracies (evaluated on the first HR test set) after
training with subsets of different size of the HR training set.
Issues regarding the current database raised in section IV
also need to be addressed to obtain a system capable of
answering a more realistic range of questions. This can be
done by making the proposed dataset construction method
more complex or by using human annotators.
ACKNOWLEDGMENT
The authors would like to thank CNES for the funding of