DisSCube

Overview

DisSCube is the spatial data cube engine of the DisSModel ecosystem. It converts raw geospatial sources (rasters and vectors) into derived variables aligned to land use and cover change (LUCC) modeling grids, ready for cellular automata models and spatio-temporal analysis.

SpatialSource  →  Derivation  →  Variable  →  DerivedVariable (Zarr)

A source goes through a derivation that applies an operator (percentage, majority, mean, minimum distance, …) on a grid, producing a derived variable registered in a SQLite catalog and stored in Zarr.

Pipeline files

A whole data preparation — grid, sources and derived variables — can be declared in one TOML file, the counterpart of a TerraME fill script, and run from the command line:

schema = 1
name = "Quickstart"

[grid]
name = "demo/300m"
crs = "EPSG:31983"
bbox = [570000.0, 9708000.0, 582000.0, 9720000.0]
resolution = 300

[[source]]
id = "landuse"
type = "file"
path = "../data/quickstart/landuse.tif"
crs = "EPSG:31983"

[[derive]]
target = "forest_pct"
source = "landuse"
operator = "percentage"
class_code = 3

disscube validate examples/pipelines/quickstart.toml
disscube run      examples/pipelines/quickstart.toml --workspace outputs/quickstart

Key features

• Declarative or direct API: describe derivations in TOML or build them in Python.
• Provenance by default: every exported GeoTIFF band and netCDF variable records the specification hash, the content hash of the stored data and the checksum of the input it came from.
• Standard outputs: load results as xarray datasets, or export to GeoTIFF and CF-1.8 netCDF.
• Independent of DisSModel: DisSCube works on its own; with disscube[dissmodel], cube.to_raster_backend(...) hands a cube directly to a DisSModel model.

Installation

pip install disscube                 # from PyPI
pip install "disscube[dissmodel]"    # + hand-off of the cube to DisSModel models

DisSCube is in alpha: the core pipeline API is stable, while declarative models are still evolving.