#Minnesota department of commerce thermal grid mapping code#
Our method, which we release with all the code including trained models, can also be used as an open science benchmark for the Sentinel-1 released dataset.Īuthors: Siddha Ganju (Nvidia Corporation) Sayak Paul (Carted) Our approach sets a high score, and a new state-of-the-art on the Sentinel-1 dataset for the ETCI competition with 0.7654 IoU, an impressive improvement over the 0.60 IOU baseline. Additionally, we post process our results with Conditional Random Fields. This cyclical process is repeated until the performance improvement plateaus. This assimilated dataset is used for the next round of training ensemble models. Concretely, we use a cyclical approach involving multiple stages (1) training an ensemble model of multiple U-Net architectures with the provided high confidence hand-labeled data and, generated pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combine the generated labels with the previously available high confidence hand-labeled dataset. We propose a semi-supervised learning pseudo-labeling scheme that derives confidence estimates from U-Net ensembles, thereby progressively improving accuracy. The NASA Impact Emerging Techniques in Computational Intelligence (ETCI) competition on Flood Detection tasked participants with predicting flooded pixels after training with synthetic aperture radar (SAR) images in a supervised setting. Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised LearningĪbstract: Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies.
0 kommentar(er)
