5 citations · 9 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…
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…
Turin3D: Evaluating Adaptation Strategies under Label Scarcity in Urban LiDAR Segmentation with Semi-Supervised Techniques
Luca Barco, Giacomo Blanco, Gaetano Chiriaco +6
3D semantic segmentation plays a critical role in urban modelling, enabling detailed understanding and mapping of city environments. In this paper, we introduce Turin3D: a new aeri…
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…
Maximum Temperature Prediction Using Remote Sensing Data Via Convolutional Neural Network
Lorenzo Innocenti, Giacomo Blanco, Luca Barco +1
Urban heat islands, defined as specific zones exhibiting substantially higher temperatures than their immediate environs, pose significant threats to environmental sustainability a…
Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing Data
Luca Barco, Angelica Urbanelli, Claudio Rossi
Rapid detection and well-timed intervention are essential to mitigate the impacts of wildfires. Leveraging remote sensed data from satellite networks and advanced AI models to auto…
Urban Air Pollution Forecasting: a Machine Learning Approach leveraging Satellite Observations and Meteorological Forecasts
Giacomo Blanco, Luca Barco, Lorenzo Innocenti +1
Air pollution poses a significant threat to public health and well-being, particularly in urban areas. This study introduces a series of machine-learning models that integrate data…
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…