API Reference

This page provides an auto-generated summary of alsDB’s public API. For usage examples and conceptual background, refer to the alsDB User Guide guide.

Core classes

ALSDatabase([storage_type, uri, schema_cfg, ...])	Write interface for the ALS TileDB array.
ALSProvider([storage_type, uri, url, ...])	Read interface for the ALS TileDB array.
ALSTile(path[, classification_filter, ...])	Processes a single LAZ tile into arrays ready for TileDB ingestion.

Storage

alsdb.storage.ALSZarrStore(path[, mode, ...])

Zarr-backed store for ALS gridded products.

TileDB backend

TileDBProvider([storage_type, uri, url, ...])	Base class for managing low-level interactions with TileDB arrays for ALS data.
TileDBSchemaConfig([tile_extent_x, ...])	Schema and domain configuration for the ALS TileDB array.
create_schema(cfg)	Build a sparse TileDB array schema for LAS point-cloud data.

Tile and tile name

Tile(path)	Wrapper around any LAZ/LAS tile file.
PNOATileName(year, region, tile_x_km, ...)	Metadata derived from a PNOA LAZ tile filename.
parse_tile_filename(filename)	Parse a PNOA LAZ filename and return a PNOATileName.

Processing

Canopy Height Model

alsdb.processing.chm.compute_chm(provider, store)	Compute a Canopy Height Model and write it into store.
alsdb.processing.chm.compute_dtm(provider, store)	Interpolate ground points (Class 2) to a DTM and write into store.
alsdb.processing.chm.compute_dsm(provider, store)	Rasterize maximum return elevation to a DSM and write into store.
alsdb.processing.chm.compute_all(provider, store)	Compute DTM, DSM, and CHM in one call, writing all into store.

Gap fraction and LAI

alsdb.processing.gap.compute_gap_fraction(...)

Compute gap fraction (and optionally effective LAI) and write into store.

Structural metrics and biomass

alsdb.processing.biomass.compute_metrics(...)	Compute LiDAR structural metrics and write them into store.
alsdb.processing.biomass.compute_biomass(...)	Estimate Above-Ground Biomass (AGB) and write into store.
alsdb.processing.biomass.naesset_model(metrics)	Næsset-style power-law AGB model (Mg ha⁻¹).
alsdb.processing.biomass.calibrate_naesset(...)	Fit Næsset power-law AGB coefficients (a, b, c) to field-plot data.
alsdb.processing.biomass.wrap_sklearn_model(...)	Wrap a fitted scikit-learn estimator as a model_fn for compute_biomass.

Multi-temporal change detection

alsdb.processing.change.compute_change(...)

Compute per-pixel change between two survey years for variable.

Individual tree segmentation

alsdb.processing.trees.segment_trees(provider)

Segment individual trees using filters.litree.

Waveform simulation

alsdb.processing.waveform.simulate_waveform(...)	Simulate a GEDI large-footprint waveform at a given UTM location.
alsdb.processing.waveform.simulate_batch(...)	Simulate waveforms for a batch of shot locations in parallel.

Tiling utilities

alsdb.processing._tiling.tile_bboxes(bbox, ...)	Partition bbox into a grid of sub-tiles.
alsdb.processing._tiling.run_tiled(...)	Run worker_fn over all tiles, sequentially or in a thread pool.
alsdb.processing._tiling.array_crs(provider)	Return the CRS string stored in the TileDB array metadata.
alsdb.processing._tiling.array_data_bbox(...)	Return the bounding box of actually stored data via nonempty_domain.
alsdb.processing._tiling.check_year_exists(...)	Return True if year falls within the ingested year range.
alsdb.processing._tiling.check_bbox_overlap(...)	Return True if requested overlaps the stored data extent.

Visualisation

2-D point-cloud plots

alsdb.utils.viz.plot_overview(df[, ...])	Four-panel overview figure: DSM with hillshade, RGB, intensity, classification.
alsdb.utils.viz.plot_dsm(df[, resolution, ...])	Plot a Digital Surface Model (max Z per cell), optionally with hillshade.
alsdb.utils.viz.plot_rgb(df[, resolution, ...])	Plot an RGB orthoimage from the colourised point cloud.
alsdb.utils.viz.plot_intensity(df[, ...])	Plot a mean-intensity raster (greyscale).
alsdb.utils.viz.plot_classification(df[, ...])	Plot a classification map with standard LAS colour coding.
alsdb.utils.viz.plot_waveform(result[, ...])	Two-panel waveform plot for a WaveformResult.
alsdb.utils.viz.plot_rh_profile(result[, ...])	Two-panel figure matching the GEDI L2A canonical waveform representation.
alsdb.utils.viz.plot_waveforms_3d(results[, ...])	3-D waterfall plot of simulated GEDI-like waveforms.
alsdb.utils.viz.plot_pointcloud_3d(df[, ...])	3-D scatter plot of the point cloud.

Gridded product plots

alsdb.utils.viz_raster.plot_chm(store[, ...])	Plot the Canopy Height Model from store.
alsdb.utils.viz_raster.plot_dtm(store[, ...])	Plot the Digital Terrain Model from store, optionally with hillshade.
alsdb.utils.viz_raster.plot_dsm(store[, ...])	Plot the Digital Surface Model from store, optionally with hillshade.
alsdb.utils.viz_raster.plot_agb(store[, ...])	Plot Above-Ground Biomass (AGB) from store.
alsdb.utils.viz_raster.plot_gap(store[, ...])	Plot gap fraction from store.
alsdb.utils.viz_raster.plot_lai(store[, ...])	Plot effective LAI from store.
alsdb.utils.viz_raster.plot_metrics(store[, ...])	Multi-panel overview of LiDAR structural metrics.
alsdb.utils.viz_raster.plot_products(store)	Three-panel overview: DTM | DSM | CHM.
alsdb.utils.viz_raster.plot_products_agb(store)	Four-panel overview: DTM | DSM | CHM | AGB.

Utilities

alsdb.utils.constants.ALSProduct(*values)	Enum representing PNOA ALS data product types.
alsdb.setup_logging([level])	Configure alsdb logging.