27 citations · 40 across the 14 of their papers we have counts for
15 papers
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,…
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…
NeighborMAE: Exploiting Spatial Dependencies between Neighboring Earth Observation Images in Masked Autoencoders Pretraining
Liang Zeng, Valerio Marsocci, Wufan Zhao +2
Masked Image Modeling has been one of the most popular self-supervised learning paradigms to learn representations from large-scale, unlabeled Earth Observation images. While incor…
Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation
Keiller Nogueira, Codrut-Andrei Diaconu, Dávid Kerekes +9
High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and often affected by noise…
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?
Yuru Jia, Valerio Marsocci, Ziyang Gong +3
Self-supervised learning (SSL) has revolutionized representation learning in Remote Sensing (RS), advancing Geospatial Foundation Models (GFMs) to leverage vast unlabeled satellite…
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.…
A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series
Ritu Yadav, Andrea Nascetti, Yifang Ban
Accurate estimation of building heights is essential for urban planning, infrastructure management, and environmental analysis. In this study, we propose a supervised Multimodal Bu…
Context-Aware Change Detection With Semi-Supervised Learning
Ritu Yadav, Andrea Nascetti, Yifang Ban
Change detection using earth observation data plays a vital role in quantifying the impact of disasters in affected areas. While data sources like Sentinel-2 provide rich optical i…
Investigating Imbalances Between SAR and Optical Utilization for Multi-Modal Urban Mapping
Sebastian Hafner, Yifang Ban, Andrea Nascetti
Accurate urban maps provide essential information to support sustainable urban development. Recent urban mapping methods use multi-modal deep neural networks to fuse Synthetic Aper…
Unsupervised Flood Detection on SAR Time Series
Ritu Yadav, Andrea Nascetti, Hossein Azizpour +1
Human civilization has an increasingly powerful influence on the earth system. Affected by climate change and land-use change, natural disasters such as flooding have been increasi…
Building Change Detection using Multi-Temporal Airborne LiDAR Data
Ritu Yadav, Andrea Nascetti, Yifang Ban
Building change detection is essential for monitoring urbanization, disaster assessment, urban planning and frequently updating the maps. 3D structure information from airborne lig…
Attentive Dual Stream Siamese U-net for Flood Detection on Multi-temporal Sentinel-1 Data
Ritu Yadav, Andrea Nascetti, Yifang Ban
Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency resp…
Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning
Sebastian Hafner, Yifang Ban, Andrea Nascetti
In this study, a Semi-Supervised Learning (SSL) method for improving urban change detection from bi-temporal image pairs was presented. The proposed method adapted a Dual-Task Siam…
How geodesy can contribute to the understanding and prediction of earthquakes
G. F. Panza, A. Peresan, F. Sansò +3
Earthquakes cannot be predicted with precision, but algorithms exist for intermediate-term middle range prediction of main shocks above a pre-assigned threshold, based on seismicit…