Noisy Supervision for Correcting Misaligned Cadaster Maps Without Perfect Ground Truth Data
Inria（法国国家信息与自动化研究所）；Inria Saclay（法国国家信息与自动化研究所萨克雷分部）；LuxCarta Technology（LuxCarta科技公司）
英文摘要：
In machine learning the best performance on a certain task is achieved by fully supervised methods when perfect ground truth labels are available. However, labels are often noisy, especially in remote sensing where manually curated public datasets are rare. We study the multi-modal cadaster map alignment problem for which available annotations are mis-aligned polygons, resulting in noisy supervision. We subsequently set up a multiple-rounds training scheme which corrects the ground truth annotations at each round to better train the model at the next round. We show that it is possible to reduce the noise of the dataset by iteratively training a better alignment model to correct the annotation alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
