644 citations · 790 across the 10 of their papers we have counts for
10 papers
LDPoly: Latent Diffusion for Polygonal Road Outline Extraction in Large-Scale Topographic Mapping
Weiqin Jiao, Hao Cheng, George Vosselman +1
Polygonal road outline extraction from high-resolution aerial images is an important task in large-scale topographic mapping, where roads are represented as vectorized polygons, ca…
RoIPoly: Vectorized Building Outline Extraction Using Vertex and Logit Embeddings
Weiqin Jiao, Hao Cheng, Claudio Persello +1
Polygonal building outlines are crucial for geographic and cartographic applications. The existing approaches for outline extraction from aerial or satellite imagery are typically…
PolyR-CNN: R-CNN for end-to-end polygonal building outline extraction
Weiqin Jiao, Claudio Persello, George Vosselman
Polygonal building outline extraction has been a research focus in recent years. Most existing methods have addressed this challenging task by decomposing it into several subtasks…
Globally Scalable Glacier Mapping by Deep Learning Matches Expert Delineation Accuracy
Konstantin A. Maslov, Claudio Persello, Thomas Schellenberger +1
Accurate global glacier mapping is critical for understanding climate change impacts. Despite its importance, automated glacier mapping at a global scale remains largely unexplored…
DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation
Xianwei Lv, Claudio Persello, Wangbin Li +3
Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery…
Multiresolution Fully Convolutional Networks to detect Clouds and Snow through Optical Satellite Images
Debvrat Varshney, Claudio Persello, Prasun Kumar Gupta +1
Clouds and snow have similar spectral features in the visible and near-infrared (VNIR) range and are thus difficult to distinguish from each other in high resolution VNIR images. W…
Deep Learning and Earth Observation to Support the Sustainable Development Goals
Claudio Persello, Jan Dirk Wegner, Ronny Hänsch +4
The synergistic combination of deep learning models and Earth observation promises significant advances to support the sustainable development goals (SDGs). New developments and a…
Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data
Devis Tuia, Claudio Persello, Lorenzo Bruzzone
The success of supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the…
Despeckling Polarimetric SAR Data Using a Multi-Stream Complex-Valued Fully Convolutional Network
Adugna G. Mullissa, Claudio Persello, Johannes Reiche
A Polarimetric Synthetic Aperture Radar (PolSAR) sensor is able to collect images in different polarization states, making it a rich source of information for target characterizati…
Recurrent Multiresolution Convolutional Networks for VHR Image Classification
John Ray Bergado, Claudio Persello, Alfred Stein
Classification of very high resolution (VHR) satellite images has three major challenges: 1) inherent low intra-class and high inter-class spectral similarities, 2) mismatching res…