Sylvain Lobry

Paris Descartes, CPSC | Paris 5 · UFR de Mathématiques et Informatique

This article introduces the task of visual question 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 gener...

Visual question answering (VQA) has recently been introduced to remote sensing to make information extraction from overhead imagery more accessible to everyone. VQA considers a question (in natural language, therefore easy to formulate) about an image and aims at providing an answer through a model based on computer vision and natural language proc...

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose a new adversarial regularization {scheme} based on the Wasserstein distance. Using this dista...

Vegetation change, permafrost degradation and their interactions affect greenhouse gas fluxes, hydrology and surface energy balance in Arctic ecosystems. The Arctic shows an overall “greening” trend (i.e. increased plant biomass and productivity) attributed to expansion of shrub vegetation. However, Arctic shrub dynamics show strong spatial variabi...

Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in the neurons and can be made explicit by teaching the model to recognize semantically interpreta...

SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. While for both data sources images are usually provided in a georeferenced manner, the geo-local...

Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in the neurons and can be made explicit by teaching the model to recognize semantically interpreta...

Visual Question Answering for Remote Sensing (RSVQA) aims at extracting information from remote sensing images through queries formulated in natural language. Since the answer to the query is also provided in natural language, the system is accessible to non-experts, and therefore dramatically increases the value of remote sensing images as a sourc...

This paper introduces the task of visual question 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 an...

One of the main objectives of the surface water and ocean topography (SWOT) mission, scheduled for launch in 2021, is to measure inland water levels using synthetic aperture radar (SAR) interferometry. A key step toward this objective is to precisely detect water areas. In this article, we present a method to detect water in SWOT images. Water is d...

A main issue preventing the use of Convolutional Neural Networks (CNN) in end user applications is the low level of transparency in the decision process. Previous work on CNN interpretability has mostly focused either on localizing the regions of the image that contribute to the result or on building an external model that generates plausible expla...

We present an Active Learning (AL) strategy for reusing a deep Convolutional Neural Network (CNN)-based object detector on a new data set. This is of particular interest for wildlife conservation: given a set of images acquired with an Unmanned Aerial Vehicle (UAV) and manually labeled ground truth, our goal is to train an animal detector that can...

We present an Active Learning (AL) strategy for re-using a deep Convolutional Neural Network (CNN)-based object detector on a new dataset. This is of particular interest for wildlife conservation: given a set of images acquired with an Unmanned Aerial Vehicle (UAV) and manually labeled gound truth, our goal is to train an animal detector that can b...

Noisy labels often occur in vision datasets, especially when they are issued from crowdsourcing or Web scraping. In this paper, we propose a new regularization method which enables one to learn robust classifiers in presence of noisy data. To achieve this goal, we augment the virtual adversarial loss with a Wasserstein distance. This distance allow...

Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas. Rural building annotations exist in OpenStreetMap (OSM), but their quality and quantity are not sufficient for training models that can create accurate rural building maps. The problems with these annotations essentiall...

We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational...

We study the effect of injecting local scale equivariance into Convolutional Neural Networks. This is done by applying each convolutional filter at multiple scales. The output is a vector field encoding for the maximally activating scale and the scale itself, which is further processed by the following convolutional layers. This allows all the inte...

This paper presents a study on the use of freely available, geo-referenced pictures from Google Street View to model and predict land-use at theurban-objects scale. This task is traditionally done manually and via photointerpretation, which is very time consuming. We proposeto use amachine learning approachbased on deep learning and to model land-u...

We study the effect of injecting local scale equivariance into Convolutional Neural Networks. This is done by applying each convolutional filter at multiple scales. The output is a vector field encoding for the maximally activating scale and the scale itself, which is further processed by the following convolutional layers. This allows all the inte...

This paper presents a study on the use of freely available, geo-referenced pictures from Google Street View to model and predict land-use at the urban-objects scale. This task is traditionally done manually and via photointerpretation, which is very time consuming. We propose to use a machine learning approach based on deep learning and to model la...

Afin d’obtenir une meilleure couverture, à la fois spatiale et temporelle de leurs mesures les hydrologues utilisent des données spatiales en plus de celles acquises sur place. Fruit d’une collaboration entre les agences spatiales française (le CNES) et américaine (JPL, NASA), la future mission SWOT a notamment pour but de fournir des mesures de ha...

Speckle phenomenon in synthetic aperture radar (SAR) images makes their visual and automatic interpretation a difficult task. To reduce strong fluctuations due to speckle, total variation (TV) regularization has been proposed by several authors to smooth out noise without blurring edges. A specificity of SAR images is the presence of strong scatter...

SAR images have distinctive characteristics compared to optical images: speckle phenomenon produces strong fluctuations, and strong scatterers have radar signatures several orders of magnitude larger than others. We propose to use an image decomposition approach to account for these peculiarities. Several methods have been proposed in the field of...