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UrbanFloodCast
==============

Overview
--------

``urbanfloodcast`` adds an urban-focused raster baseline to the PyHazards inundation benchmark stack.

At a Glance
-----------

.. grid:: 1 2 4 4
   :gutter: 2
   :class-container: catalog-grid

   .. grid-item-card:: Hazard Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Flood

      .. container:: catalog-stat-note

         Public catalog grouping used for this model.

   .. grid-item-card:: Maturity
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Implemented

      .. container:: catalog-stat-note

         Catalog maturity label used on the index page.

   .. grid-item-card:: Tasks
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         1

      .. container:: catalog-stat-note

         Inundation

   .. grid-item-card:: Benchmark Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         :doc:`Flood Benchmark </benchmarks/flood_benchmark>`

      .. container:: catalog-stat-note

         Primary benchmark-family link used for compatible evaluation coverage.


Description
-----------

``urbanfloodcast`` adds an urban-focused raster baseline to the PyHazards inundation benchmark stack.

The implementation keeps the shared spatiotemporal tensor contract used by the synthetic inundation smoke dataset.

Benchmark Compatibility
-----------------------

**Primary benchmark family:** :doc:`Flood Benchmark </benchmarks/flood_benchmark>`

**Mapped benchmark ecosystems:** :doc:`FloodCastBench </benchmarks/floodcastbench>`

External References
-------------------

**Paper:** `UrbanFloodCast: WMO Urban Flooding Forecasting Challenge <https://arxiv.org/abs/2405.21179>`_ | **Repo:** `Repository <https://github.com/HydroPML/UrbanFloodCast>`__

Registry Name
-------------

Primary entrypoint: ``urbanfloodcast``

Supported Tasks
---------------

- Inundation

Programmatic Use
----------------

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(name="urbanfloodcast", task="regression", in_channels=3, history=4)
   preds = model(torch.randn(2, 4, 3, 16, 16))
   print(preds.shape)

Notes
-----

- Outputs are next-horizon inundation depth rasters.