• Investigate using multiscale grids in a Vision Transformer Masked Autoencoder.

• Will it be worth the computational requirements?

• Smallest will likely do

• https://github.com/techmn/satmae_pp
• A few notes : https://github.com/RichardScottOZ/satmae_pp
• LICENSE - Apache 2.0
  • https://github.com/techmn/satmae_pp/blob/main/LICENSE

• Not run this one yet
• https://www.research-collection.ethz.ch/handle/20.500.11850/581338 - Self-Supervised Representation Learning for Remote Sensing https://github.com/RichardScottOZ/satmae_pp/tree/main

• V100s - 16GB?

• Trained on https://github.com/fMoW/dataset [70GB tarball] https://purl.stanford.edu/vg497cb6002
  • To investigate structure
  • Presumably 3 band groupings for 10, 20 and 60m resolution patches around pictures of locations of interest - airports, zoos, etc.
  • Designed to classify these

• Metadata file - csv with location/polygon coordinates, class type etc.
  • These are here https://raw.githubusercontent.com/fMoW/dataset/master/LICENSE

category	location_id	image_id	timestamp	polygon

0 airport 0 6 2015-07-25T08:45:14Z POLYGON ((32.666164117900003 39.932541952376475, 32.711078120537337 39.932541952376475, 32.711078120537337 39.967113357199999, 32.666164117900003 39.967113357199999, 32.666164117900003 39.932541952376475))

• pytorch as pre instructions
  • https://pytorch.org/get-started/locally/
• geopandas to get bonus gdal
• rasterio via conda-forge
• tensorboard via conda-forge
• pip install timm
• pip install opencv-python
• [so far]

• I had started with a python 3.10 and default installed the rest which gave timm 0.9.16 and an error

• satmae advises

  • python 3.8
  • pytorch 1.10
  • cuda 11.1
  • timm 0.4.12

• rasterio.errors.RasterioIOError: '/dataset/fmow_sentinel/fmow-sentinel/train\parking_lot_or_garage/parking_lot_or_garage_927/parking_lot_or_garage_927_109.tif' does not exist in the file system, and is not recognized as a supported dataset name.

• Multiscale adaptation for segmentation based on general layers

• Could be remote sensing, but any domain for geoscience, geophysics, geology, structure etc.

• might be continuous or one hot
• assume mse default for testing this and getting to work

• To keep it in human finger space [and patch space]
• Take a set of geophysics grids at 100m resolution
• Take another set at 200m resolution

• Planets, surface etc. - not rectangles

• Likely want on the fly grid slicing into tiles, not directory structures full of sliced up grids in folders

• Is this useful for autoencoders here beyond smoothing reasons

• Needs to be general

• Resolution groupings - this is a satmae parameter already

• Might be fun to get an xarray based training loop going
  • zen3geo: https://zen3geo.readthedocs.io/en/v0.2.0/chipping.html
  • https://earthmover.io/blog/cloud-native-dataloader
  • https://xbatcher.readthedocs.io/en/latest/

• Assume all files the same
• Read from a data directory
• Stack

python -m main_pretrain.py
--batch_size 8 --accum_iter 16
--epochs 1 --warmup_epochs 1
--input_size 96 --patch_size 8
--mask_ratio 0.75
--model_type group_c
--dataset_type grid
--grouped_bands 0 --grouped_bands 1
--blr 0.0001 --num_workers 8
--output_dir ./output_dir
--log_dir ./output_dir

• python -m main_pretrain.py --batch_size 8 --accum_iter 16 --epochs 3 --warmup_epochs 1 --input_size 96 --patch_size 8 --mask_ratio 0.75 --model_type group_c --dataset_type grid --grouped_bands 0 --grouped_bands 1 --blr 0.0001 --num_workers 8 --output_dir ./output_dir --log_dir ./output_dir

python main_pretrain.py --batch_size 8 --accum_iter 16 --epochs 1 --warmup_epochs 1 --input_size 96 --patch_size 8 --mask_ratio 0.75 --model_type group_c --dataset_type grid --grouped_bands 0 --grouped_bands 1 --blr 0.0001 --num_workers 8 --input_channels 2 --output_dir ./output_dir --log_dir ./output_dir

#parser.add_argument('--model', default='mae_vit_base_patch16', type=str, metavar='MODEL', help='Name of model to train')

python main_pretrain.py --model mae_vit_base_patch16_small --batch_size 8 --accum_iter 16 --epochs 30 --warmup_epochs 1 --input_size 96 --patch_size 8 --mask_ratio 0.75 --model_type group_c --dataset_type grid --grouped_bands 0 --grouped_bands 1 --blr 0.0001 --num_workers 8 --input_channels 2 --output_dir ./output_dir_small --log_dir ./output_dir_small

python ww_test.py --model mae_vit_base_patch16_small --batch_size 8 --accum_iter 16 --epochs 30 --warmup_epochs 1 --input_size 96 --patch_size 8 --mask_ratio 0.75 --model_type group_c --dataset_type grid --grouped_bands 0 --grouped_bands 1 --blr 0.0001 --num_workers 8 --input_channels 2 --output_dir ./output_dir_small --log_dir ./output_dir_small --weightwatcher_path ww_test_details_small.csv

• Training
  • Handle nodata [same thing as below basically]
• Handle nodata
• Handle valid data
• Handle one hot data [although mostly interested in other things here]
  • Handle different loss functions
• The Hard Part
  • geospatial inference [BASICS DONE]
    • Check for edge cases
  • reference dataset - take from first of the list [currently hardcoded a trial]