5 papers
Adaptive federated learning for ship detection across diverse satellite imagery sources
Tran-Vu La, Minh-Tan Pham, Yu Li +2
We investigate the application of Federated Learning (FL) for ship detection across diverse satellite datasets, offering a privacy-preserving solution that eliminates the need for…
Urban Flood Mapping Using Satellite Synthetic Aperture Radar Data: A Review of Characteristics, Approaches and Datasets
Jie Zhao, Ming Li, Yu Li +2
Understanding the extent of urban flooding is crucial for assessing building damage, casualties and economic losses. Synthetic Aperture Radar (SAR) technology offers significant ad…
Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications
João Gabriel Vinholi, Marco Chini, Anis Amziane +3
Comparing images captured by disparate sensors is a common challenge in remote sensing. This requires image translation -- converting imagery from one sensor domain to another whil…
Early Flood Warning Using Satellite-Derived Convective System and Precipitation Data -- A Retrospective Case Study of Central Vietnam
Tran-Vu La, Thanh Huy Nguyen, Patrick Matgen +1
This paper addresses the challenges of an early flood warning caused by complex convective systems (CSs), by using Low-Earth Orbit and Geostationary satellite data. We focus on a s…
Insight Into the Collocation of Multi-Source Satellite Imagery for Multi-Scale Vessel Detection
Tran-Vu La, Minh-Tan Pham, Marco Chini
Ship detection from satellite imagery using Deep Learning (DL) is an indispensable solution for maritime surveillance. However, applying DL models trained on one dataset to others…