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ASUFM
=====

Overview
--------

``asufm`` is a compact temporal convolution baseline for next-window wildfire activity prediction.

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

         Forecasting

   .. 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
-----------

``asufm`` is a compact temporal convolution baseline for next-window wildfire activity prediction.

PyHazards exposes it through the shared wildfire benchmark and config workflow.

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

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

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

**Paper:** `Wildfire Spread Prediction in North America Using Satellite Imagery and Vision Transformer <https://doi.ieeecomputersociety.org/10.1109/CAI59869.2024.00278>`_ | **Repo:** `Repository <https://github.com/bronteee/fire-asufm>`__

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

Primary entrypoint: ``asufm``

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

- Forecasting

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

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(
       name="asufm",
       task="forecasting",
       input_dim=7,
       output_dim=5,
       lookback=12,
   )
   preds = model(torch.randn(2, 12, 7))
   print(preds.shape)

Notes
-----

- The smoke path uses weekly wildfire count windows with seasonal time features.