6 citations · 8 across the 11 of their papers we have counts for
11 papers
Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling
Xuran Hu, Mingzhe Zhu, Djordje Stanković +4
Multiplicative noise poses a challenge in coherent and signal-dependent imaging owing to its intensity-dependent variance and frequently non-Gaussian distribution. We propose a det…
Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation
Ritu Yadav, Andrea Nascetti, Yifang Ban
Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring,…
Uncertainty-Aware Cross-Modal Remote Sensing Image-Text Retrieval via Evidential Learning
Zhuoyue Wang, Xueqian Wang, Gang Li +3
In cross-modal remote sensing image-text retrieval (CMRSITR), test-time remote sensing (RS) images and textual descriptions may deviate from well-curated benchmark conditions due t…
Low-Rank Adaptation of Geospatial Foundation Models for Wildfire Mapping Using Sentinel-2 Data
Ali Shibli, Andrea Nascetti, Yifang Ban
Wildfire burned-area mapping is essential for damage assessment, emissions modeling, and understanding fire-climate interactions across diverse ecological regions. Recent geospatia…
Noise2Map: End-to-End Diffusion Model for Semantic Segmentation and Change Detection
Ali Shibli, Andrea Nascetti, Yifang Ban
Semantic segmentation and change detection are two fundamental challenges in remote sensing, requiring models to capture either spatial semantics or temporal differences from satel…
GeoHeight-Bench: Towards Height-Aware Multimodal Reasoning in Remote Sensing
Xuran Hu, Zhitong Xiong, Zhongcheng Hong +3
Current Large Multimodal Models (LMMs) in Earth observation are predominantly evaluated on planar optical tasks and often neglect the vertical dimension, although vertical geometri…
RADARSAT Constellation Mission Compact Polarisation SAR Data for Burned Area Mapping with Deep Learning
Yu Zhao, Yifang Ban
Monitoring wildfires has become increasingly critical due to the sharp rise in wildfire incidents in recent years. Optical satellites like Sentinel-2 and Landsat are extensively ut…
TS-SatFire: A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and Prediction
Yu Zhao, Sebastian Gerard, Yifang Ban
Wildfire monitoring and prediction are essential for understanding wildfire behaviour. With extensive Earth observation data, these tasks can be integrated and enhanced through mul…
SAR Despeckling via Region-Aware Sparse Representation and Statistical Noise Approximation
Xuran Hu, Mingzhe Zhu, Djordje Stanković +3
Synthetic Aperture Radar (SAR) imagery are widely utilized in remote sensing due to their all-weather, all-day imaging capabilities. However, SAR images are highly susceptible to n…
PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
Valerio Marsocci, Yuru Jia, Georges Le Bellier +12
Geospatial Foundation Models (GFMs) have emerged as powerful tools for extracting representations from Earth observation data, but their evaluation remains inconsistent and narrow.…
Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration
Sebastian Hafner, Heng Fang, Hossein Azizpour +1
Urbanization advances at unprecedented rates, leading to negative environmental and societal impacts. Remote sensing can help mitigate these effects by supporting sustainable devel…