activity
20242026
most citedCaBuAr: California Burned Areas dataset for delineation

23 citations · 53 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CV2026

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…

cs.CL2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025★ 4 cited

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…

cs.CL2024

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…