eSPA for RTS

Goal of this project is to utilize the entropy-optimal Scalable Probabilistic Approximations algorithm (eSPA) to create a model which can estimate the density of Retrogressive-Thaw-Slumps (RTS) across the globe with different levels of detail. Hoping, that a successful training could gain new knowledge about RTX-proxies.

Setup

uv sync

Project Plan

1. Create global hexagon grids with h3
2. Enrich the grids with data from various sources and with labels from DARTS v2
3. Use eSPA for simple classification: hex has [many slumps / some slumps / few slumps / no slumps]
4. use SPARTAn for regression: one for slumps density (area) and one for total number of slumps

Data Sources and Engineering

• Labels
  • "year": Year of observation
  • "area": Total land-area of the hexagon
  • "rts_density": Area of RTS divided by total land-area
  • "rts_count": Number of single RTS instances
• ERA5 (starting 40 years from "year")
  • "temp_yearXXXX_qY": Y-th quantile temperature of year XXXX. Used to enter the temperature distribution into the model.
  • "thawing_days_yearXXXX": Number of thawing-days of year XXXX.
  • "precip_yearXXXX_qY": Y-th quantile precipitation of year XXXX. Similar to temperature.
  • "temp_5year_diff_XXXXtoXXXX_qY": Difference of the Y-th quantile temperature between year XXXX and XXXX. Always 5 years difference.
  • "temp_10year_diff_XXXXtoXXXX_qY": Difference of the Y-th quantile temperature between year XXXX and XXXX. Always 10 years difference.
  • "temp_diff_qY": Difference of the Y-th quantile temperature between year XXXX and XXXX. Always 10 years difference.
• ArcticDEM
  • "dissection_index": Dissection Index, (max - min) / max
  • "max_elevation": Maximum elevation
  • "elevationX_density": Area where the elevation is larger than X divided by the total land-area
• TCVIS
  • ???
• Wildfire???
• Permafrost???
• GroundIceContent???
• Biome

About temporals Every label has its own year - all temporal dependent data features, e.g. "temp_5year_diff_XXXXtoXXXX_qY" are calculated respective to that year. The number of years added from a dataset is always the same, e.g. for ERA5 for an observation in 2024 the ERA5 data would start in 1984 and for an observation from 2023 in 1983.