Comments (3)
Actual image source likely to be Sentinel 2 and/or images from Google static maps API.
Leaving this open, since model pre-trained on another dataset (e.g., Kaggle DSTL) may be useful for processing actual data (transfer learning, bagging, ensembles etc)
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- DSTL - Kaggle
- Draper Satellite Image Chronology - Kaggle
- Understanding the Amazon from Space - Kaggle
- Ships in Satellite Imagery - Kaggle - From Open California dataset.
- 2D Semantic Labelling - Vaihingen data - This is from a true orthophotographic survey (from a plane). The data is 9cm resolution!
- SAT-4 and SAT-6 airborne datasets - very high res (1m) whole of US. 65 Terabytes. (DeepSat) 1m/px. (2015). SAT-4: barren land, trees, grassland, other. SAT-6: barren land, trees, grassland, roads, buildings, water bodies. (RGB + NIR)
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https://github.com/developmentseed/skynet-data
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Related Issues (20)
- Clean code + repo
- Write up initial findings + do quick show and tell. HOT 1
- feedforward + visualise HOT 2
- Cache training data
- Error when checking model target
- Setup cuda on gpu machine HOT 1
- split training data into training/validation subsets
- log training epoch errors and persist network weights at training checkpoints
- Class rebalancing
- Get theano working on macbook
- Model parameter versioning
- Slides for GEOS visit
- feed forward single image
- Dockerise
- test
- Skywalker HOT 4
- tidy up and make repo public.
- no pre-trained weights provided HOT 1
- Unexpected token error when trying to build using npm
- skynet data
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