CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation

no code implementations • 2 May 2024 • Chenying Liu, Conrad Albrecht, Yi Wang, Xiao Xiang Zhu

We study the potential of noisy labels y to pretrain semantic segmentation models in a multi-modal learning framework for geospatial applications.

Image Segmentation Semantic Segmentation +1

Task Specific Pretraining with Noisy Labels for Remote sensing Image Segmentation

no code implementations • 25 Feb 2024 • Chenying Liu, Conrad Albrecht, Yi Wang, Xiao Xiang Zhu

Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations.

Image Segmentation Segmentation +2

Map Generation from Large Scale Incomplete and Inaccurate Data Labels

no code implementations • 20 May 2020 • Rui Zhang, Conrad Albrecht, Wei zhang, Xiaodong Cui, Ulrich Finkler, David Kung, Siyuan Lu

Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc..

Disaster Response Management