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CNN-ASPP
========

Overview
--------

``wildfire_aspp`` is the backward-compatible public PyHazards entrypoint for the CNN + ASPP wildfire spread model.

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
-----------

``wildfire_aspp`` is the backward-compatible public PyHazards entrypoint for the CNN + ASPP wildfire spread model.

PyHazards keeps the alias for compatibility while the implementation delegates to the native ``wildfire_cnn_aspp`` builder under the hood.

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

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

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

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

**Paper:** `Application of Explainable Artificial Intelligence in Predicting Wildfire Spread <https://ieeexplore.ieee.org/document/10568207>`_

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

Primary entrypoint: ``wildfire_aspp``

Aliases: ``wildfire_cnn_aspp``

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

- Spread

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

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(
       name="wildfire_aspp",
       task="segmentation",
       in_channels=12,
   )

   x = torch.randn(2, 12, 64, 64)
   logits = model(x)
   print(logits.shape)

Notes
-----

- ``wildfire_cnn_aspp`` remains available as an alias for the same public model.