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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

S. Ullo

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Preprint Aug 2026

Detection of Christmas tree plantations from high-resolution aerial imagery. A case study in the French Morvan

Christmas tree plantations are economically relevant, yet a largely unexplored application domain in Remote Sensing (RS). Their delineation is challenging because of high planting density, short rotation cycles, visual confusion with surrounding vegetation, the availability of dense labels for one reference year only,...

Francesca Razzano, Emanuele Dalsasso, Adrien Baysse-Lainé et al. · 0 citations
Preprint Sep 2026

AquaCubeAI-Powered Monitoring Turbidity on-board {\Phi}sat-2

Timely monitoring of coastal water quality is critical for environmental protection, yet conventional satellite workflows rely on downlink and ground processing, introducing latency that can limit responsiveness to rapidly evolving turbidity events. To address this limitation, we propose AquaCubeAI, a lightweight machi...

Pietro Di Stasio, Francesca Razzano, Elisa Liparulo et al. · 0 citations
Open access Jul 2026

AI-based multi-temporal analysis of urban dynamics using Sentinel-2 data. A case study over Osmaniye, Turkey

This study presents a multi-temporal framework for urban monitoring that integrates Sentinel-2 multispectral imagery with semantic segmentation techniques, and highlights the potential of Artificial Intelligence and Remote Sensing for continuous urban monitoring, disaster impact assessment, and data-driven urban planni...

A. Pigna, Pietro Di Stasio, D. Tapete et al. · 0 citations
Preprint Aug 2026

Multi-Sensor Mapping of Vulnerable Urban Settlements Using SAR, Multispectral, and Hyperspectral Imagery: A Case Study in C\'ordoba, Argentina

Informal settlements represent a major urban challenge in rapidly expanding cities, yet their identification from Earth Observation (EO) data remains difficult because of their heterogeneous appearance and incomplete official inventories. This work presents a multi-sensor deep learning (DL) framework for slum-likelihoo...

Luigi Russo, A. Ferral, S. Ullo et al. · 0 citations
Jul 2026

Monitoring Post-Disaster Urban Recovery Using High-Resolution SAR Time Series and Unsupervised Learning: Evidence from the 2023 Türkiye-Syria Earthquake

An unsupervised framework for recovery monitoring based on multi-temporal synthetic aperture radar (SAR) observations and deep-learning anomaly detection and COSMO-SkyMed time series is applied to four cities severely affected by the 2023 Turkiye-Syria earthquakes, revealing heterogeneous reconstruction dynamics across...

Luigi Russo, D. Tapete, S. Ullo et al. · 0 citations
Open access Jul 2026

A Deep Learning Framework for Rapid Building Damage Detection through Multimodal Data Fusion: Application to the 2025 Myanmar Earthquake

This study formulates post-disaster building damage detection (BDD) as a binary image classification task (damaged vs. undamaged buildings) using multimodal satellite data and a unified ResNet-18 backbone to enable a controlled comparison of fusion strategies.

Luigi Russo, D. Tapete, S. Ullo et al. · 1 citation

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