all AI news
BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge. (arXiv:2201.10389v1 [cs.CV])
Jan. 26, 2022, 2:11 a.m. | Ali Ismail, Mariette Awad
cs.LG updates on arXiv.org arxiv.org
Change detection is instrumental to localize damage and understand
destruction in disaster informatics. While convolutional neural networks are at
the core of recent change detection solutions, we present in this work, BLDNet,
a novel graph formulation for building damage change detection and enable
learning relationships and representations from both local patterns and
non-stationary neighborhoods. More specifically, we use graph convolutional
networks to efficiently learn these features in a semi-supervised framework
with few annotated data. Additionally, BLDNet formulation allows for the …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Technology Consultant Master Data Management (w/m/d)
@ SAP | Walldorf, DE, 69190
Research Engineer, Computer Vision, Google Research
@ Google | Nairobi, Kenya