Measuring the impact of label noise on semantic segmentation using rastervision
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Quantifying Error in Training Data for Mapping and Monitoring the Earth System - A Workshop on Quantifying Error in Training Data for Mapping and Monitoring the Earth System was held on January 8-9, 2019 at Clark University, with support from Omidyar Networks Property Rights Initiative, now PlaceFund.
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- 1. Measuring the impact of label noise on semantic segmentation using Lewis Fishgold
- 2. Outline Motivation: Using noisy OSM labels for building segmentation Prior experiments on effects of label noise Our experiments on building segmentation Implementation using Raster Vision
- 3. Motivation: Building Segmentation using Noisy OSM Labels Training chip with small label errors Predictions over Georgetown, Guyana
- 4. Prior Work From Deep Learning is Robust to Massive Label Noise, Rolnick et al. 2018
- 5. Prior Work From Deep Learning is Robust to Massive Label Noise, Rolnick et al. 2018
- 6. Our Experiments How does noise affect accuracy for our use case? Use Spacenet Vegas Buildings dataset Semantic segmentation Satellite Imagery More realistic noise Deleting labels Shifting labels Warning: very preliminary work
- 19. References Deep Learning is Robust to Massive Labeling Noise: https://arxiv.org/abs/1705.10694 Spacenet Dataset: https://spacenetchallenge.github.io/ Raster Vision: https://github.com/azavea/raster-vision Code for these experiments: https://github.com/azavea/raster-vision-experiments/pull/1