1.1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementFR33 papers
- Centre National de la Recherche ScientifiqueFR23 papers
- Centre de Coopération Internationale en Recherche Agronomique pour le DéveloppementFR20 papers
- AgroParisTechFR16 papers
- Université de MontpellierFR13 papers
- Institute for Cross-Disciplinary Physics and Complex SystemsES7 papers
- Laboratoire d'Informatique, de Robotique et de Microélectronique de MontpellierFR6 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR5 papers
- Universitat de les Illes BalearsES4 papers
- Aix-Marseille UniversitéFR3 papers
- CEA Paris-SaclayFR3 papers
- Centre de physique des particules de MarseilleFR3 papers
10 papers · 1 filter
Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration
Francisco Mena, Dino Ienco, Cassio F. Dantas +2
Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality pred…
TimeSenCLIP: A Time Series Vision-Language Model for Remote Sensing
Pallavi Jain, Diego Marcos, Dino Ienco +2
Vision-language models (VLMs) have shown significant promise in remote sensing applications, particularly for land-use and land-cover (LULC) mapping via zero-shot classification an…
Open-Canopy: Towards Very High Resolution Forest Monitoring
Fajwel Fogel, Yohann Perron, Nikola Besic +8
Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision with critical environmental applications. However,…
Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights
Ibrahim Fayad, Philippe Ciais, Martin Schwartz +8
Accurate and timely monitoring of forest canopy heights is critical for assessing forest dynamics, biodiversity, carbon sequestration as well as forest degradation and deforestatio…
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
Martin Schwartz, Philippe Ciais, Catherine Ottlé +11
In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) i…
Channel-Based Attention for LCC Using Sentinel-2 Time Series
Hermann Courteille, A. Benoît, N Méger +2
Deep Neural Networks (DNNs) are getting increasing attention to deal with Land Cover Classification (LCC) relying on Satellite Image Time Series (SITS). Though high performances ca…
Galaxy Image Translation with Semi-supervised Noise-reconstructed Generative Adversarial Networks
Qiufan Lin, Dominique Fouchez, Jérôme Pasquet
Image-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronom…
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
Roberto Interdonato, Dino Ienco, Raffaele Gaetano +1
Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spa…
A framework for remote sensing images processing using deep learning technique
Rémi Cresson
Deep learning techniques are becoming increasingly important to solve a number of image processing tasks. Among common algorithms, Convolutional Neural Networks and Recurrent Neura…
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks
Dino Ienco, Raffaele Gaetano, Claire Dupaquier +1
Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to effici…