Datasets:
gremlin97
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RemoteSensingCorpus

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of Philadelphia, which is not seen during the training. Note
that this second test set also uses another sensor (marked as
unknown on the USGS data catalog), not seen during training.
Differences between the two datasets :
Due to their characteristics, the two datasets represent two
different possible use cases of VQA:
•The LR dataset allows for large spatial and temporal
coverage thanks to the frequent acquisitions made by
Sentinel-2. This characteristic could be of interest for
future applications of VQA such as large scale queries
(e.g. rural/urban questions) or temporal (which is out of
the scope of this study). However, due to the relatively
low resolution (10m), some objects can not be seen on
such images (such as small houses, roads, trees, . . . ).
This fact severely limits the questions to which the model
2https://earthexplorer.usgs.gov/could give an accurate answer.
•Thanks to the much finer resolution of the HR dataset,
a quantity of information of interest to answer typical
questions is present. Therefore, in contrast to the LR
dataset, questions concerning objects’ coverage or count-
ing relatively small objects can possibly be answered
from such data. However, data of such resolution is
generally less frequently updated and more expensive to
acquire.
Based on these differences, we constructed different types
of questions for the two datasets. Questions concerning the
area of objects are only asked in the HR dataset. On the other
hand, questions about urban/rural area classification are only
asked in the LR dataset, as the level of zoom of images from
the HR dataset would prevent a meaningful answer from being
provided.
To account for the data distributions and error margins we
also quantize different answers in both datasets:
•Counting in LR: as the coverage is relatively large
(6.55km2), the number of small objects contained in one
tile can be high, giving a heavy tailed distribution for the
numerical answers, as shown in Figure 6. More precisely,
while 26.7% of the numerical answers are ’0’ and 50%
of the answers are less than ’7’, the highest numerical
answer goes up to ’17139’. In addition to making the
problem complex, we can argue that allowing such a
range of numerical answer does not make sense on data
of this resolution. Indeed, it would be in most cases
impossible to distinguish 17139 objects on an image of
65536 pixels. Therefore, numerical answers are quantized
into the following categories:
–’0’;
–’between 1 and 10’;
–’between 11 and 100’;
–’between 101 and 1000’;
–’more than 1000’.
•In a similar manner, we quantize questions regarding
the area in the HR dataset. A great majority (60.9%) of
the answer of this type are ’0m2’, while the distribution
also presents a heavy tail. Therefore, we use the same
quantization as the one proposed for counts for the LR
dataset. Note that we do not quantize purely numerical
answers (i.e. answers to questions of type ’count’) as
the maximum number of objects is 89 in our dataset.
Counting answers therefore correspond to 89 classes in
the model in this case (see section III).
C. Discussion
Questions/Answers distributions :
We show the final distribution of answers per question type
for both datasets in Figure 5. We can see that most question
types (with the exception of ’rural/urban’ questions in the
LR dataset, asked only once per image) are close to evenly
distributed by construction. The answer ’no’ is dominating
the answers’ distribution for the HR dataset with a frequency
of 37.7%. In the LR dataset, the answer ’yes’ occurs 34.9%
of the time while the ’no’ frequency is 34.3%. The strongest
PRE-PRINT. FINAL VERSION IN IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 6
(a) Distribution of answers for the LR dataset
yesno
yes
no0m2between 10m2 and 100m2more than 1000m2presencecount
comparea (b) Distribution of answers for the HR dataset (numerical answers are ordered,
and 0 is the most frequent)
Fig. 5. Distributions of answers in the Low resolution (LR) and High resolution (HR) datasets.
Fig. 6. Frequencies of exact counting answers in the LR dataset. Only the
left part of the histogram is shown (until 200 objects), the largest (single)
count being 17139. 50% of the answers are less than 7 objects in the tile.
imbalance occurs for the answer ’0’ in the HR dataset
(with a frequency of 60.9% for the numerical answer). This
imbalance is greatly reduced by the quantization process
described in the previous paragraph.
Limitations of the proposed method :
While the proposed method for image/question/answer triplets
generation has the advantage of being automatic and easily
scalable while using data annotated by humans, a few lim-
itations have been observed. First, it can happen that some
annotations are missing or badly registered [4]. Furthermore,
it was not possible to match the acquisition date of the imagery
to the one of OSM. The main reason being that it is impossible
to know if a newly added element appeared at the same time
in reality or if it was just entered for the first time in OSM.
As OSM is the main source of data for our process, errors in
OSM will negatively impact the accuracy of our databases.
Furthermore, due to the templates used to automatically
construct questions and provide answers, the set of questions