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WildfireSpreadTS
================

Overview
--------

``wildfirespreadts`` models wildfire spread as a sequence-to-mask prediction task.

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

         Wildfire

      .. 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

         Spread

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

      .. container:: catalog-stat-value

         :doc:`Wildfire Benchmark </benchmarks/wildfire_benchmark>`

      .. container:: catalog-stat-note

         Primary benchmark-family link used for compatible evaluation coverage.


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

``wildfirespreadts`` models wildfire spread as a sequence-to-mask prediction task.

The PyHazards adapter uses a compact 3D convolution stack that consumes short raster history windows and predicts the next spread mask.

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

**Primary benchmark family:** :doc:`Wildfire Benchmark </benchmarks/wildfire_benchmark>`

**Mapped benchmark ecosystems:** :doc:`WildfireSpreadTS </benchmarks/wildfirespreadts_ecosystem>`

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

**Paper:** `WildfireSpreadTS: A Dataset of Multi-Modal Time Series for Wildfire Spread Prediction <https://openreview.net/forum?id=RgdGkPRQ03>`_ | **Repo:** `Repository <https://github.com/SebastianGer/WildfireSpreadTS>`__

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

Primary entrypoint: ``wildfirespreadts``

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

- Spread

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

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(
       name="wildfirespreadts",
       task="segmentation",
       history=4,
       in_channels=6,
   )
   logits = model(torch.randn(2, 4, 6, 16, 16))
   print(logits.shape)

Notes
-----

- The smoke dataset uses temporal wildfire spread tensors rather than single-frame rasters.