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of the LR and HR datasets. In both cases, 3 model runs have |
been trained and we report both the average and the standard |
deviation of our results to limit the variability coming from |
the stochastic nature of the optimization. |
The numerical evaluation is achieved using the accuracy, |
defined in our case as the ratio of correct answers. We report |
the accuracy per question type (see subsection II-A), the |
average of these accuracies (AA) and the overall accuracy |
(OA). |
We show some predictions of the model on the different |
test sets in Figure 8 and Figure 9 to qualitatively assess the |
results. Numerical performance of the proposed model on the |
LR dataset is reported in Table II and the confusion matrix |
is shown in Figure 10. The performance on both tests sets of |
the HR dataset are reported in Table III and the confusion |
matrices are shown in Figure 11. |
General accuracy assessment: |
The proposed model achieves an overall accuracy of 79% |
PRE-PRINT. FINAL VERSION IN IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 8 |
What is the area covered by residential buildings? |
0 m² 0 m²Ground truth Prediction |
(a) HR, test set 1 |
What is the area covered by rectangular buildings? |
Between 100 and |
1000 m²Between 100 and |
1000 m²Ground truth Prediction |
(b) HR, test set 1 |
Is there a residential building at the bottom |
of the place of worship? |
yes yesGround truth Prediction |
(c) HR, test set 1 |
How many residential buildings at the bottom |
of a road are there? |
4 3Ground truth Prediction |
(d) HR, test set 1 |
What is the amount of large buildings? |
3 1Ground truth Prediction |
(f) HR, test set 1 |
How many buildings on the left of a road |
are there in the image? |
38 0Ground truth Prediction |
(e) HR, test set 1 |
What is the area covered by rectangular parkings? |
between 100m2 and |
1000m2between 100m2 and |
1000m2Ground truth Prediction |
(g) HR, test set 2 |
What is the amount of small buildings? |
9 0Ground truth Prediction |
(h) HR, test set 2 |
What is the amount of large residential buildings? |
2 1Ground truth Prediction |
(i) HR, test set 2 |
Fig. 8. Samples from the high resolution test sets: (a)-(f) are from the first set of the HR dataset, (g)-(i) are from the second set of the HR dataset. |
PRE-PRINT. FINAL VERSION IN IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 9 |
TABLE II |
RESULTS ON THE TEST SET OF THE LOW RESOLUTION DATASET . THE |
STANDARD DEVIATION IS REPORTED IN BRACKETS . |
Type Accuracy |
Count 67.01% (0.59%) |
Presence 87.46% (0.06%) |
Comparison 81.50% (0.03%) |
Rural/Urban 90.00% (1.41%) |
AA 81.49% (0.49%) |
OA 79.08% (0.20%) |
TABLE III |
RESULTS ON BOTH TEST SETS OF THE HIGH RESOLUTION DATASET . THE |
STANDARD DEVIATION IS REPORTED IN BRACKETS . |
Type Accuracy Accuracy |
Test set 1 Test set 2 |
Count 68.63% (0.11%) 61.47% (0.08%) |
Presence 90.43% (0.04%) 86.26% (0.47%) |
Comparison 88.19% (0.08%) 85.94% (0.12%) |
Area 85.24% (0.05%) 76.33% (0.50%) |
AA 83.12% (0.03%) 77.50% (0.29%) |
OA 83.23% (0.02%) 78.23% (0.25%) |
on the low resolution dataset (see Table II) and of 83% on |
the first test set of the high resolution dataset (Table III), |
indicating that the task of automatically answering question |
based on remote sensing images is possible. When looking at |
the accuracies per question type (in Tables II and III), it can |
be noted that the model performs inconsistently with respect |
to the task the question is tackling: while a question about the |
presence of an object is generally well answered (87.46% in |
the LR dataset, 90.43% in the first test set of the HR dataset), |
counting questions gives poorer performances (67.01% and |
68.63% respectively). This can be explained by the fact that |
presence questions can be seen as simplified counting ques- |
tions to which the answers are restricted to two options: ”0” |
or ”1 or more”. Classical VQA models are known to struggle |
with the counting task [38]. An issue which partly explains |
these performances in the counting task is the separation of |
connected instances. This problem has been raised for the case |
of buildings in [33] and is illustrated in Figure 8(f), where the |
ground truth is indicating three buildings, which could also be |
only one. We found another illustration of this phenomenon |
in the second test set in Figure 8(i). This issue mostly arises |
when counting roads or buildings. |
Thanks to the answers’ quantization, questions regarding the |
areas of objects are generally well answered with an accuracy |
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