autofloods

Pre-release

This release is a pre-release and may not be stable for production use.

AutoFloods

AutoFloods is a Python package for automated flood mapping at scale from Sentinel-1 SAR imagery, with pluggable data sources (Microsoft Planetary Computer, NASA OPERA) and detection methods (Z-score, Otsu).

Quickstart

OPERASource requires a free NASA Earthdata Login; MPCSource works with no credentials at all (a free subscription key just raises rate limits) — see Authentication.

Pre-release: pip install --pre autofloods

Basic usage:

from autofloods import flood_mapper
from autofloods.sources import OPERASource

fm = flood_mapper(
    # AOI grid; needs id_col, dry_date_col, zone columns
    grid_shapefile='resources/india_utm_fishnet_buffer.gpkg',

    # which AOI IDs from the grid to process
    grid_id_list=[321],

    # dry-season years to build the baseline from
    dry_years=[2024, 2024],

    # where the terrain-slope mask is cached
    slope_dir='resources/slope/',

    # wet-season date range to classify
    wet_duration=['2024/07', '2024/10'],

    # or MPCSource() (the default)
    source=OPERASource(),

    # root dir for all outputs and caches
    output_dir='output/my_run',
)

# read each AOI's dry season into self.dry_months
fm.get_dry_dates()

# turn dry_months into per-year search date ranges
fm.generate_dry_date_ranges()

# STAC-search the source for dry-season scenes
fm.get_s1_items(dry_wet='dry')

# download + read those scenes
fm.read_scenes(dry_wet='dry', overview_level=None, max_workers=6)

# fit the dry-season Z-score baseline per AOI
fm.generate_mean_std_by_aoi()

# compute/cache the terrain-slope mask
fm.prepare_slope(dem_overview=0, buffer=500)

# search, read, reproject wet-season scenes
fm.prepare_wet_scenes(overview_level=None, max_workers=6)

# classify each scene against the baseline
fm.map_floods(vv_thd=-2.5, vh_thd=-2.5, rel_slope_thd=20,
              export_raster=False, export_vector=False, export_maps=False)

# collapse per-scene results into one band per date
fm.merge_floods_by_date(export_raster=True)

# per-pixel count of scenes with data gaps
fm.generate_number_of_scenes(export_raster=True)

# aggregate per-date results into per-month flood-day counts
fm.monthly_sum()

See the documentation for full usage, API reference, and citation details: https://autofloods.readthedocs.io/en/latest/

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