text stringlengths 0 820 |
|---|
answering for remote sensing data (RSVQA). Remote sensing |
images contain a wealth of information which can be useful |
for a wide range of tasks including land cover classification, |
object counting or detection. However, most of the available |
methodologies are task-specific, thus inhibiting generic and easy |
access to the information contained in remote sensing data. As |
a consequence, accurate remote sensing product generation still |
requires expert knowledge. With RSVQA, we propose a system to |
extract information from remote sensing data that is accessible to |
every user: we use questions formulated in natural language and |
use them to interact with the images. With the system, images |
can be queried to obtain high level information specific to the |
image content or relational dependencies between objects visible |
in the images. Using an automatic method introduced in this |
article, we built two datasets (using low and high resolution data) |
of image/question/answer triplets. The information required to |
build the questions and answers is queried from OpenStreetMap |
(OSM). The datasets can be used to train (when using supervised |
methods) and evaluate models to solve the RSVQA task. We |
report the results obtained by applying a model based on |
Convolutional Neural Networks (CNNs) for the visual part and on |
a Recurrent Neural Network (RNN) for the natural language part |
to this task. The model is trained on the two datasets, yielding |
promising results in both cases. |
Index Terms —Visual Question Answering, Deep learning, |
Dataset, Natural Language, Convolution Neural Networks, Re- |
current Neural Networks, Very High Resolution, OpenStreetMap |
I. I NTRODUCTION |
REMOTE sensing data is widely used as an indirect |
source of information. From land cover/land use to |
crowd estimation, environmental or urban area monitoring, |
remote sensing images are used in a wide range of tasks |
of high societal relevance. For instance, remote sensing data |
can be used as a source of information for 6 of the 17 sus- |
tainable development goals as defined by the United Nations |
[1]. Due to the critical nature of the problems that can be |
addressed using remote sensing data, significant effort has |
been made to increase its availability in the last decade. For |
instance, Sentinel-2 satellites provide multispectral data with |
a relatively short revisiting time, in open-access. However, |
while substantial effort has been dedicated to improving the |
means of direct information extraction from Sentinel-2 data |
Sylvain Lobry, Diego Marcos, Jesse Murray and Devis Tuia are with |
Laboratory of Geo-Information Science and Remote Sensing, Wageningen |
University, The Netherlands email: work@sylvainlobry.com |
Classification |
Is it a rural or urban area?Regression |
How many buildings are there? |
Detection |
Is there a road?Classical tasks |
Regression |
What is the area covered by small |
buildings?Detection |
Is there a road at the top of the image?Specific tasks |
Regression / Detection |
What is the number of roads next to a |
park?Detection |
Is there a building next to a parking?Mix of tasksFig. 1. Example of tasks achievable by a visual question answering model |
for remote sensing data. |
in the framework of a given task (e.g. classification [2], [3]), |
the ability to use remote sensing data as a direct source of |
information is currently limited to experts within the remote |
sensing and computer vision communities. This constraint, |
imposed by the technical nature of the task, reduces both the |
scale and variety of the problems that could be addressed with |
such information as well as the number of potential end-users. |
This is particularly true when targeting specific applications |
(detecting particular objects, e.g.thatched roofs or buildings in |
a developing country [4]) which would today call for important |
research efforts. The targeted tasks are often multiple and |
changing in the scope of a project calls for strong expert |
knowledge, limiting the information which can be extracted |
from remote sensing data. To address these constraints, we |
introduce the problem of visual question answering (VQA) |
for remote sensing data.arXiv:2003.07333v2 [cs.CV] 14 May 2020 |
PRE-PRINT. FINAL VERSION IN IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2 |
VQA is a new task in computer vision, introduced in its |
current form by [5]. The objective of VQA is to answer a |
free-form and open-ended question about a given image. As |
the questions can be unconstrained, a VQA model applied |
to remote sensing data could serve as a generic solution |
to classical problems involving remote sensing data (e.g. |
”Is there a thatched roof in this image?” for thatched roof |
detection), but also very specific tasks involving relations |
between objects of different nature (e.g. ”Is there a thatched |
roof on the right of the river?”). Examples of potential |
questions are shown in Figure 1. |
To the best of our knowledge, this is the first time (after |
the first exploration in [6]) that VQA has been applied to |
extract information from remote sensing data. It builds on the |
task of generating descriptions of images through combining |
image and natural language processing to provide the user |
with easily accessible, high-level semantic information. These |
descriptions are then used for image retrieval and intelligence |
generation [7]. As seen in this introduction, VQA systems rely |
on the recent advances in deep learning. Deep learning based |
methods, thanks to their ability to extract high-level features, |
have been successfully developed for remote sensing data as |
reviewed in [8]. Nowadays, this family of methods is used to |
tackle a variety of tasks; for scene classification, an early work |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.