1 citations · 3 across the 9 of their papers we have counts for
11 papers
Learning Regional Snow Water Equivalent and Snow Height Variations from Sentinel-1 InSAR Acquisitions
Luca Barco, Lorenzo Innocenti, Bianca Bartoli +3
Managing water resources in mountainous regions depends heavily on reliable Snow Water Equivalent (SWE) and Snow Height (HS) data, yet these variables remain difficult to track at…
Temporal Modelling for Burn Scars on Sentinel-3
Luca Barco, Edoardo Arnaudo, Andrea Bragagnolo +2
Rapid and accurate burn scar delineation from satellite imagery is essential for post-fire damage assessment. Sentinel-3 OLCI, with daily revisit and 21 spectral bands, suits rapid…
PBLH Estimation from Satellite Radiances via a Dual-Encoder Transformer
Lorenzo Innocenti, Luca Catalano, Edoardo Arnaudo +4
Estimating the Planetary Boundary Layer Height (PBLH) from satellite observations is a challenging regression problem due to the indirect relationship between top-of-atmosphere rad…
GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation
Gaetano Chiriaco, Luca Barco, Andrea Bragagnolo +2
Geospatial foundation models aim to learn representations that transfer across regions and sensors, yet evaluating them on specific tasks requires large, high-quality, multi-modal…
FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models
Edoardo Arnaudo, Jacopo Lungo Vaschetti, Lorenzo Innocenti +4
Very-High Resolution (VHR) remote sensing imagery is increasingly accessible, but often lacks annotations for effective machine learning applications. Recent foundation models like…
Robust Burned Area Delineation through Multitask Learning
Edoardo Arnaudo, Luca Barco, Matteo Merlo +1
In recent years, wildfires have posed a significant challenge due to their increasing frequency and severity. For this reason, accurate delineation of burned areas is crucial for e…
A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe
Angelica Urbanelli, Luca Barco, Edoardo Arnaudo +1
The increasing frequency of catastrophic natural events, such as wildfires, calls for the development of rapid and automated wildfire detection systems. In this paper, we propose a…
Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery
Marco Galatola, Edoardo Arnaudo, Luca Barco +2
Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management. However, generating accurate LC maps i…
Hierarchical Instance Mixing across Domains in Aerial Segmentation
Edoardo Arnaudo, Antonio Tavera, Fabrizio Dominici +2
We investigate the task of unsupervised domain adaptation in aerial semantic segmentation and discover that the current state-of-the-art algorithms designed for autonomous driving…
Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images
Antonio Tavera, Edoardo Arnaudo, Carlo Masone +1
In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial imagery. We observe that the existing methods used for this task are designed without con…
A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images
Edoardo Arnaudo, Fabio Cermelli, Antonio Tavera +2
Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets. A major issue concerning cur…