23 citations · 53 across the 13 of their papers we have counts for
13 papers
Far from the Crowd: Scalable Self-Supervised Learning via Geographic Isolation
Daniele Rege Cambrin, Francesco Rossi, Mattia Varile
Self-supervised pretraining on remote sensing imagery typically treats all samples as equally informative, despite large variability in geographic and visual structure. We propose…
Losses that Cook: Topological Optimal Transport for Structured Recipe Generation
Mattia Ottoborgo, Daniele Rege Cambrin, Paolo Garza
Cooking recipes are complex procedures that require not only a fluent and factual text, but also accurate timing, temperature, and procedural coherence, as well as the correct comp…
CLOSP: A Unified Semantic Space for SAR, MSI, and Text in Remote Sensing
Daniele Rege Cambrin, Lorenzo Vaiani, Giuseppe Gallipoli +2
Retrieving relevant imagery from vast satellite archives is crucial for applications like disaster response and long-term climate monitoring. However, most text-to-image retrieval…
HydroChronos: Forecasting Decades of Surface Water Change
Daniele Rege Cambrin, Eleonora Poeta, Eliana Pastor +4
Forecasting surface water dynamics is crucial for water resource management and climate change adaptation. However, the field lacks comprehensive datasets and standardized benchmar…
Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation
Daniele Rege Cambrin, Luca Colomba, Paolo Garza
In crisis management and remote sensing, image segmentation plays a crucial role, enabling tasks like disaster response and emergency planning by analyzing visual data. Neural netw…
Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning
Daniele Rege Cambrin, Giuseppe Gallipoli, Irene Benedetto +2
Large Language Models (LLMs) have demonstrated impressive performance across various tasks. However, current training approaches combine standard cross-entropy loss with extensive…
Level Up Your Tutorials: VLMs for Game Tutorials Quality Assessment
Daniele Rege Cambrin, Gabriele Scaffidi Militone, Luca Colomba +3
Designing effective game tutorials is crucial for a smooth learning curve for new players, especially in games with many rules and complex core mechanics. Evaluating the effectiven…
KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation
Daniele Rege Cambrin, Eleonora Poeta, Eliana Pastor +3
Segmentation of crop fields is essential for enhancing agricultural productivity, monitoring crop health, and promoting sustainable practices. Deep learning models adopted for this…
Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation
Daniele Rege Cambrin, Isaac Corley, Paolo Garza
Estimating global tree canopy height is crucial for forest conservation and climate change applications. However, capturing high-resolution ground truth canopy height using LiDAR i…
Estimating Earthquake Magnitude in Sentinel-1 Imagery via Ranking
Daniele Rege Cambrin, Isaac Corley, Paolo Garza +1
Earthquakes are commonly estimated using physical seismic stations, however, due to the installation requirements and costs of these stations, global coverage quickly becomes impra…
QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1
Daniele Rege Cambrin, Paolo Garza
Earthquake monitoring is necessary to promptly identify the affected areas, the severity of the events, and, finally, to estimate damages and plan the actions needed for the restor…
DQNC2S: DQN-based Cross-stream Crisis event Summarizer
Daniele Rege Cambrin, Luca Cagliero, Paolo Garza
Summarizing multiple disaster-relevant data streams simultaneously is particularly challenging as existing Retrieve&Re-ranking strategies suffer from the inherent redundancy of mul…
CaBuAr: California Burned Areas dataset for delineation
Daniele Rege Cambrin, Luca Colomba, Paolo Garza
Forest wildfires represent one of the catastrophic events that, over the last decades, caused huge environmental and humanitarian damages. In addition to a significant amount of ca…