From the 1 of 7 linked papers with an AI index.
7 papers
Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation
Ritu Yadav, Andrea Nascetti, Yifang Ban
The paper proposes two deep learning methods to estimate per‑pixel uncertainty for Earth observation regression tasks such as building height, canopy height, and above‑ground bioma…
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 +12
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…