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# Sphinx build info version 1
# This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
config: 18654414e97a2833d588ab35b458397a
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:orphan:

pyhazards
=========

.. toctree::
:maxdepth: 4

pyhazards
45 changes: 45 additions & 0 deletions docs/docs/_sources/api/pyhazards.datasets.rst.txt
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pyhazards.datasets package
==========================

Submodules
----------

pyhazards.datasets.base module
------------------------------

.. automodule:: pyhazards.datasets.base
:members:
:undoc-members:
:show-inheritance:

pyhazards.datasets.registry module
-----------------------------------

.. automodule:: pyhazards.datasets.registry
:members:
:undoc-members:
:show-inheritance:

pyhazards.datasets.transforms package
-------------------------------------

.. automodule:: pyhazards.datasets.transforms
:members:
:undoc-members:
:show-inheritance:

pyhazards.datasets.hazards package
-----------------------------------

.. automodule:: pyhazards.datasets.hazards
:members:
:undoc-members:
:show-inheritance:

Module contents
---------------

.. automodule:: pyhazards.datasets
:members:
:undoc-members:
:show-inheritance:
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pyhazards.engine package
========================

Submodules
----------

pyhazards.engine.trainer module
-------------------------------

.. automodule:: pyhazards.engine.trainer
:members:
:undoc-members:
:show-inheritance:

pyhazards.engine.distributed module
------------------------------------

.. automodule:: pyhazards.engine.distributed
:members:
:undoc-members:
:show-inheritance:

pyhazards.engine.inference module
----------------------------------

.. automodule:: pyhazards.engine.inference
:members:
:undoc-members:
:show-inheritance:

Module contents
---------------

.. automodule:: pyhazards.engine
:members:
:undoc-members:
:show-inheritance:
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pyhazards.metrics package
=========================

.. automodule:: pyhazards.metrics
:members:
:undoc-members:
:show-inheritance:
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pyhazards.models package
========================

Submodules
----------

pyhazards.models.backbones module
----------------------------------

.. automodule:: pyhazards.models.backbones
:members:
:undoc-members:
:show-inheritance:

pyhazards.models.heads module
------------------------------

.. automodule:: pyhazards.models.heads
:members:
:undoc-members:
:show-inheritance:

pyhazards.models.builder module
--------------------------------

.. automodule:: pyhazards.models.builder
:members:
:undoc-members:
:show-inheritance:

pyhazards.models.registry module
---------------------------------

.. automodule:: pyhazards.models.registry
:members:
:undoc-members:
:show-inheritance:

Module contents
---------------

.. automodule:: pyhazards.models
:members:
:undoc-members:
:show-inheritance:
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pyhazards package
=================

Subpackages
-----------

.. toctree::
:maxdepth: 4

pyhazards.datasets
pyhazards.models
pyhazards.engine
pyhazards.metrics
pyhazards.utils

Module contents
---------------

.. automodule:: pyhazards
:members:
:undoc-members:
:show-inheritance:
:exclude-members: GraphTemporalDataset, graph_collate, WildfireMamba, wildfire_mamba_builder
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pyhazards.utils package
=======================

Submodules
----------

pyhazards.utils.hardware module
--------------------------------

.. automodule:: pyhazards.utils.hardware
:members:
:undoc-members:
:show-inheritance:

pyhazards.utils.common module
------------------------------

.. automodule:: pyhazards.utils.common
:members:
:undoc-members:
:show-inheritance:

Module contents
---------------

.. automodule:: pyhazards.utils
:members:
:undoc-members:
:show-inheritance:
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How to Cite
===========
If you find it useful, please considering cite the following work:

.. .. code-block:: bibtex

.. @article{li2025intellectual,
.. title={Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses},
.. author={Li, Lincan and Shen, Bolin and Zhao, Chenxi and Sun, Yuxiang and Zhao, Kaixiang and Pan, Shirui and Dong, Yushun},
.. journal={arXiv preprint arXiv:2508.19641},
.. year={2025}
.. }
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ERA5
====

Global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF),
providing high-resolution meteorological fields for weather, climate, and hazard-related applications.

----

Overview
--------

**ERA5** is the fifth-generation global reanalysis developed by ECMWF under the Copernicus Climate Change
Service (C3S). It combines vast amounts of historical observations with a modern data assimilation system
to generate temporally consistent, high-resolution estimates of the atmospheric state.

ERA5 is widely adopted as a standard meteorological baseline for climate studies and natural hazard
modeling, including wildfire danger assessment, flood risk analysis, and extreme weather attribution.

----

Data Characteristics
--------------------

- **Spatial coverage:** Global
- **Spatial resolution:** ~0.25° latitude × 0.25° longitude
- **Temporal resolution:** Hourly
- **Vertical structure:** Single-level fields and optional pressure/model levels
- **Data format:** GRIB and NetCDF
- **Coordinate system:** Regular latitude–longitude grid

----

Variables
---------

ERA5 provides a comprehensive set of atmospheric and surface variables, including:

- Near-surface meteorology (2 m temperature, dewpoint, wind)
- Precipitation, radiation, and surface fluxes
- Atmospheric pressure-level variables (temperature, winds, geopotential)
- Boundary-layer and land-surface diagnostics

----

Typical Use Cases
-----------------

- Meteorological forcing for wildfire, flood, and extreme weather prediction models
- Climate variability and trend analysis
- Environmental covariates for spatiotemporal machine learning tasks
- Benchmark reanalysis input for weather–climate modeling pipelines

----

Access
------

ERA5 data are distributed via the Copernicus Climate Data Store (CDS):

- `ERA5 single levels <https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview>`_
- `Copernicus Climate Data Store <https://cds.climate.copernicus.eu/>`_

----

Reference
---------

Hersbach, H., Bell, B., Berrisford, P., *et al.* (2020).
*The ERA5 global reanalysis*.
Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049.
`https://doi.org/10.1002/qj.3803 <https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803>`_
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FIRMS
=====

Near-real-time global active fire detection dataset provided by NASA, based on satellite thermal anomaly
observations for operational wildfire monitoring and hazard analysis.

----

Overview
--------

**FIRMS (Fire Information for Resource Management System)** is a NASA-operated system that distributes
active fire and thermal anomaly detections derived from satellite sensors, including MODIS and VIIRS.
Each record corresponds to a time-stamped hotspot detection associated with a potential fire event.

FIRMS is widely used for operational wildfire monitoring, rapid situational awareness, and as event-level
labels or targets in wildfire prediction pipelines when combined with meteorological and land-surface data.

----

Data Characteristics
--------------------

- **Spatial coverage:** Global
- **Spatial resolution:** Sensor-dependent (e.g., ~375 m for VIIRS)
- **Temporal resolution:** Near real time (multiple updates per day)
- **Data structure:** Event-based point detections (not gridded tensors)
- **Data format:** CSV, Shapefile, GeoJSON, KML
- **Coordinate system:** Geographic latitude–longitude

----

Variables
---------

Each FIRMS detection record typically includes:

- Detection time and satellite overpass information
- Geographic location (latitude and longitude)
- Fire radiative power (FRP) or thermal anomaly indicators
- Confidence or quality flags (sensor- and product-specific)

----

Typical Use Cases
-----------------

- Operational wildfire monitoring and early detection
- Event labeling for supervised wildfire prediction models
- Spatiotemporal analysis of fire occurrence and activity patterns
- Integration with meteorological and fuel datasets for hazard modeling

----

Access
------

FIRMS data are publicly accessible through NASA Earthdata services:

- `FIRMS portal <https://firms.modaps.eosdis.nasa.gov/>`_
- `NASA Earthdata <https://earthdata.nasa.gov/>`_

----

Reference
---------

Schroeder, W., Oliva, P., Giglio, L., & Csiszar, I. (2014).
*The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment*.
Remote Sensing of Environment, 143, 85–96.
`https://doi.org/10.1016/j.rse.2013.08.008 <https://doi.org/10.1016/j.rse.2013.08.008>`_
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