295 citations · 448 across the 10 of their papers we have counts for
11 papers
Atomizer-IO: Beyond Pixels, Patches and Grids
Hugo Riffaud de Turckheim, Sylvain Lobry, Nicolas Houdré +3
Most vision architectures assume that observations lie on a regular grid, an effective abstraction for natural images but a restrictive one for sensing data whose channels, tempora…
RAMEN: Resolution-Adjustable Multimodal Encoder for Earth Observation
Nicolas Houdré, Diego Marcos, Hugo Riffaud de Turckheim +4
Earth observation (EO) data spans a wide range of spatial, spectral, and temporal resolutions, from high-resolution optical imagery to low resolution multispectral products or rada…
Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars
Hugo Riffaud de Turckheim, Sylvain Lobry, Roberto Interdonato +1
The growing number of Earth observation satellites has led to increasingly diverse remote sensing data, with varying spatial, spectral, and temporal configurations. Most existing m…
Visual Question Answering on Multiple Remote Sensing Image Modalities
Hichem Boussaid, Lucrezia Tosato, Flora Weissgerber +3
The extraction of visual features is an essential step in Visual Question Answering (VQA). Building a good visual representation of the analyzed scene is indeed one of the essentia…
SAR Strikes Back: A New Hope for RSVQA
Lucrezia Tosato, Flora Weissgerber, Laurent Wendling +1
Remote Sensing Visual Question Answering (RSVQA) is a task that extracts information from satellite images to answer questions in natural language, aiding image interpretation. Whi…
An LLM Agent for Automatic Geospatial Data Analysis
Yuxing Chen, Weijie Wang, Sylvain Lobry +1
Large language models (LLMs) are being used in data science code generation tasks, but they often struggle with complex sequential tasks, leading to logical errors. Their applicati…
Can SAR improve RSVQA performance?
Lucrezia Tosato, Sylvain Lobry, Flora Weissgerber +1
Remote sensing visual question answering (RSVQA) has been involved in several research in recent years, leading to an increase in new methods. RSVQA automatically extracts informat…
Segmentation-guided Attention for Visual Question Answering from Remote Sensing Images
Lucrezia Tosato, Hichem Boussaid, Flora Weissgerber +3
Visual Question Answering for Remote Sensing (RSVQA) is a task that aims at answering natural language questions about the content of a remote sensing image. The visual features ex…
Deep learning for classification of noisy QR codes
Rebecca Leygonie, Sylvain Lobry, ) +1
We wish to define the limits of a classical classification model based on deep learning when applied to abstract images, which do not represent visually identifiable objects.QR cod…
RSVQA: Visual Question Answering for Remote Sensing Data
Sylvain Lobry, Diego Marcos, Jesse Murray +1
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 ra…
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning
Benjamin Kellenberger, Diego Marcos, Sylvain Lobry +1
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 wildli…