2 citations · 3 across the 8 of their papers we have counts for
8 papers
Detection of Christmas tree plantations from high-resolution aerial imagery. A case study in the French Morvan
Francesca Razzano, Emanuele Dalsasso, Adrien Baysse-Lainé +3
Christmas tree plantations are economically relevant, yet a largely unexplored application domain in Remote Sensing (RS). Their delineation is challenging because of high planting…
SAE-Xplainers: Rule-Based Feature Interpretation for Extreme Earth Events
Hugo Porta, Emanuele Dalsasso, Chang Xu +2
The emergence of large-scale Weather and Climate (W&C) datasets offers new opportunities for modeling extreme Earth events (ExEE) and their impacts using deep learning. However, th…
SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images
Gencer Sumbul, Chang Xu, Emanuele Dalsasso +1
From optical sensors to microwave radars, leveraging the complementary strengths of remote sensing (RS) sensors is crucial for achieving dense spatio-temporal monitoring of our pla…
CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities
Hugo Porta, Emanuele Dalsasso, Jessica L. McCarty +1
Canada experienced in 2023 one of the most severe wildfire seasons in recent history, causing damage across ecosystems, destroying communities, and emitting large quantities of CO2…
Multi-Scale and Multimodal Species Distribution Modeling
Nina van Tiel, Robin Zbinden, Emanuele Dalsasso +3
Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs…
POLO -- Point-based, multi-class animal detection
Giacomo May, Emanuele Dalsasso, Benjamin Kellenberger +1
Automated wildlife surveys based on drone imagery and object detection technology are a powerful and increasingly popular tool in conservation biology. Most detectors require train…
Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation
Hugo Porta, Emanuele Dalsasso, Diego Marcos +1
Prototypical part learning is emerging as a promising approach for making semantic segmentation interpretable. The model selects real patches seen during training as prototypes and…
Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks
Denis Coquenet, Clément Rambour, Emanuele Dalsasso +1
Vision-language foundation models such as CLIP have shown impressive zero-shot performance on many tasks and datasets, especially thanks to their free-text inputs. However, they st…