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RemoteSensingCorpus

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SCALE . W HEN DEALING WITH LOW RESOLUTION DATA ,VISIBLE OBJECTS
OF INTEREST ARE LARGER . TO DEAL WITH THIS DISPARITY ,WE ADAPT
THE SIZE THRESHOLDS TO THE RESOLUTION OF THE IMAGES .
”next to” is handled as a hard threshold on the relative distance
between the two objects (less than 1000m). When looking at
relative positions, we select the second element following the
procedure previously defined.
Question construction : At this point of the procedure, we
have an element (e.g. road), with an optional attribute (e.g.
small road) and an optional relative position (e.g. small road
on the left of a water area). The final step is to generate
a ”base question” about this element. We define 5 types of
questions of interest (”Question catalog” in Figure 2(a)), from
which a specific type is randomly selected to obtain a base
question. For instance, in the case of comparison questions, we
randomly choose among ”less than”, ”equals to” and ”more
than” and construct a second element.
This base question is then turned into a natural language
question using pre-defined templates for each question type
and object. For some question types (e.g. count), more than
one template is defined (e.g. ’How many are there?’,
’What is the number of ?’ or ’What is the amount of ?’).
In this case, the template to be used is randomly selected. The
stochastic process ensures the diversity, both in the question
types and the question templates used.
2) Answer construction: : To obtain the answer to the
constructed question, we extract the objects from the OSM
database corresponding to the image footprint. The objects b
corresponding to the element category and its attributes are
then selected and used depending on the question type:
•Count : In the case of counting, the answer is simply the
number of objects b.
•Presence : A presence question is answered by comparing
the number of objects bto 0.
•Area : The answer to a question about the area is the sum
of the areas of the objects b.
•Comparison : Comparison is a specific case for which
a second element and the relative position statement is
needed. This question is then answered by comparing the
number of objects bto the ones of the second element.
•Rural/Urban : The case of rural/urban questions is han-
dled in a specific way. In this case, we do not cre-
ate a specific element, but rather count the number of
buildings (both commercial or residential). This number
of buildings is then thresholded to a predefined number
depending on the resolution of the input data (to obtain
a density) to answer the question. Note that we are using
a generic definition of rural and urban areas but this can
be easily adapted using the precise definition of each
Fig. 3. Images selected for the LR dataset over the Netherlands. Each point
represent one Sentinel-2 image which was later split into tiles. Red points
represent training samples, green pentagon represents the validation image,
and blue triangle is for the test image. Note that one training image is not
visible (as it overlaps with the left-most image).
country.
B. Data
Following the method presented in subsection II-A, we
construct two datasets with different characteristics.
Low resolution (LR) : this dataset is based on Sentinel-2
images acquired over the Netherlands. Sentinel-2 satellites
provide 10m resolution (for the visible bands used in this
dataset) images with frequent updates (around 5 days) at a
global scale. These images are openly available through ESA’s
Copernicus Open Access Hub1.
To generate the dataset, we selected 9 Sentinel-2 tiles cover-
ing the Netherlands with a low cloud cover (selected tiles are
shown in Figure 3). These tiles were divided in 772images of
size 256×256(covering 6.55km2) retaining the RGB bands.
From these, we constructed 77′232 questions and answers
following the methodology presented in subsection II-A. We
split the data in a training set ( 77.8%of the original tiles), a
validation set ( 11.1%) and a test set ( 11.1%) at the tile level
(the spatial split is shown in Figure 3). This allows to limit
spatial correlation between the different splits.
High resolution (HR) : this dataset uses 15cm resolution aerial
RGB images extracted from the High Resolution Orthoim-
agery (HRO) data collection of the USGS. This collection
1https://scihub.copernicus.eu/
PRE-PRINT. FINAL VERSION IN IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 5
Fig. 4. Extent of the HR dataset with a zoom on the Portland, Manhattan (New
York City) and Philadelphia areas. Each point represent one image (generally
of size 5000×5000 ) which was later split into tiles. The images cover the
New York City/Long Island region, Philadelphia and Portland. Red points
represent training samples, green pentagons represent validation samples, and
blue indicators are for the test sets (blue triangles for test set 1, blue stars for
test set 2).
covers most urban areas of the USA, along with a few areas
of interest (e.g. national parks). For most areas covered by the
dataset, only one tile is available with acquisition dates ranging
from year 2000 to 2016, with various sensors. The tiles are
openly accessible through USGS’ EarthExplorer tool2.
From this collection, we extracted 161 tiles belonging to
the North-East coast of the USA (see Figure 4) that were split
into10′659images of size 512×512(each covering 5898m2).
We constructed 1′066′316questions and answers following the
methodology presented in subsection II-A. We split the data
in a training set ( 61.5%of the tiles), a validation set ( 11.2%),
and test sets ( 20.5%for test set 1, 6.8%for test set 2). As it
can be seen in Figure 4, test set 1 covers similar regions as
the training and validation sets, while test set 2 covers the city