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Recent events (e.g. floods, landslides, and fires) coincident with the OPERA RTC-S1 dataset

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OPERA-Cal-Val/dist-s1-events

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Disturbance S1 Events

This is a code-base is used to the generate coregistered datasets for DIST-S1 calibration and validation. The actual datasets are derived from publicaly available datasets (see Datasets below). We utilize geojsons in the external_validation_data_db, each of which is curated from publicly available datasets. The provenance of these datasets is included in the properties and the associated event yml in the events/ directory. This is still very much a work in progress and more information about its use and application will be added as it is refined.

Installation

Intall the environment and notebook kernel:

mamba env update -f environment.yml
conda activate dist-s1
python -m ipykernel install --user --name dist-s1

Generating datasets

Examples:

python run_events.py --event all
python run_events.py --event 'benghazi_flood_2023 chiapas_fire_2024'
python run_events.py --event all --exclude_event 'bangladesh_coastal_flood_2024 yajiang_fire_2024'

The datasets should be generated in an out directory. The total size currently is about 60 GB of data for all the possible events.

Datasets

We use the following sources for generating these datasets.

  1. The Copernicus Emergency Management Service, specifically the rapid mapping of these events: https://rapidmapping.emergency.copernicus.eu/
  2. The UNOSAT data available through humanitarian data exchange: https://data.humdata.org/ (search "flood extents" for example!)
  3. The Wildland Fire Interagency Geospatial Services from the National Interagency Fire Center: https://data-nifc.opendata.arcgis.com/datasets/nifc::wfigs-current-interagency-fire-perimeters/about
  4. Hand drawn delineations

There will be additional sources used to derive forthecoming sites. For now, all the sites in this repository (i.e. in events/) are derived from the above 3 sources. We note that all the datasets are mostly delineated using optical sensors (either Sentinel-2, Landsat, planet or other VHR sensors and the exact provenance of each dataset can be traced using the source data). There are few that use Sentinel-1 SAR sensor that we will use for disturbance mapping (e.g. demak_flood_2024). Generally, optically-derived delineations are valuable datasets for calibrating/validating SAR disturbances as some aspects of the event will be visible in one sensor but not the other and vice versa. We highlight that validating any imagery across sensors can be impacted by the differences in acquisition time, particularly when imaging dynamic events like floods. In other words, care must be used for each event!

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Recent events (e.g. floods, landslides, and fires) coincident with the OPERA RTC-S1 dataset

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