Add Antarctic Snow Accumulation dataset by Arthern et al. 2006 #146
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Adding snow accumulation dataset from Arthern 2006 (the 1000m spatial resolution resampled version from bedmap2 repository) as a new miscellaneous dataset to feed into our neural network. Also enabled zip file extraction after download, extending similar code on tar.gz archives in 66b0628.
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Adding the InSAR-based Antarctic grounding line dataset from Mouginot et al., 2017. The vector polygon is stored in a shapefile format, and we download it from the PGC Quantarctica archive. Also ignoring the .shp, .shx and .dbf data files now. Moving forward, I really need to standardize on the citekey naming scheme (that uses either the dataset or literature citation's first author, dubious years, etc)...
Add rtree from PyPI to make geopandas.sjoin work, and for that we need libspatialindex from conda-forge.
Constrain our square training tiles to only exist within the grounded ice area (give or take). To do that, we first buffer the MEASURES InSAR-based grounding line (actually a polygon of grounded ice areas) by 10km, and then select all the tiles which are contained within this buffered polygon. Makes use of geopandas' sjoin (spatial join). Also, the re-saved geojson files appear to show some decimal differences for tiles_4326.geojson but not tiles_3031.geojson, perhaps because there has been some changes in the pyproj library or something else?
Closes #147 Add InSAR-based Antarctic grounding line to subset training tiles within grounded ice boundary.
Finally we have Arthern Accumulation, and at a spatial resolution of 1000m just like BEDMAP2! Putting it down as conditional input W3, after REMA W1 and MEASURES Ice Velocity W2. We are down from 2499 to 2347 tiles due to the new grounding line restriction from #147, though this subset should be scientifically better. Time to spin up our GPUs for some neural network training!
weiji14
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May 29, 2019
Closes #146 Add Antarctic Snow Accumulation dataset by Arthern et al. 2006.
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Adding Antarctic snow accumulation dataset from Arthern et al., 2006, specifically the 1000m spatial resolution resampled version from the bedmap2 repository here.
References:
TODO: