                    GNU GENERAL PUBLIC LICENSE
                       Version 2, June 1991

  Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
  51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
  Everyone is permitted to copy and distribute verbatim copies
  of this license document, but changing it is not allowed.

  beaconGIS — Building Damage Assessment QGIS Plugin
  Copyright (C) 2026 Adem Özel

  This program is free software; you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation; either version 2 of the License, or
  (at your option) any later version.

  This program is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.

  You should have received a copy of the GNU General Public License along
  with this program; if not, write to the Free Software Foundation, Inc.,
  51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.

  Full license text: https://www.gnu.org/licenses/gpl-2.0.html


================================================================================
NOTICE — separate license for the AI model weights
================================================================================

The PLUGIN CODE in this repository is licensed GPL v2+ (above), matching
the QGIS host application's license.

The AI MODEL WEIGHTS that the plugin downloads on first run are a SEPARATE
work, distributed under a DIFFERENT license:

    Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
    (CC BY-NC-SA 4.0)
    https://creativecommons.org/licenses/by-nc-sa/4.0/

The weights are distributed via the plugin's GitHub release page:

    https://github.com/azeldev03/beaconGIS/releases

Why two licenses?

The plugin code is software, and the QGIS Plugin Repository expects
GPL-compatible licensing. The weights are training-derived artifacts that
inherit a CC BY-NC-SA 4.0 share-alike obligation from the upstream xView2
/ xBD dataset (Gupta et al. 2019), which itself is licensed CC BY-NC-SA
4.0. We carry that obligation forward as required by the dataset terms.

In practice this means:

  * The plugin CODE may be forked, modified, and re-shipped under GPL.
  * The model WEIGHTS may be used for academic, research, humanitarian,
    and educational purposes only — never for commercial products or
    paid services without separate written permission from the author.
  * Any model fine-tuned or distilled from these weights must itself be
    released under CC BY-NC-SA 4.0 or a one-way-compatible license.

================================================================================
THIRD-PARTY ATTRIBUTIONS
================================================================================

This plugin and the upstream model weights are derivatives of, or depend
on at runtime, the following works. Their licenses must be preserved when
this plugin is redistributed.

1. xView2 / xBD Dataset
   Gupta R., Goodman B., Patel N. et al. (2019)
   arXiv:1911.09296
   License: CC BY-NC-SA 4.0
   https://xview2.org/

2. timm (PyTorch Image Models)
   Wightman R., maintainers et al.
   License: Apache 2.0
   The SeResNeXt-50 (32x4d) encoder architecture used during training and
   referenced in model.py.
   https://github.com/huggingface/pytorch-image-models

3. ONNX Runtime
   Microsoft and the ONNX Runtime contributors
   License: MIT
   Used at plugin runtime for inference.
   https://github.com/microsoft/onnxruntime

4. PyTorch
   Meta AI / PyTorch Foundation
   License: BSD 3-Clause
   Used during training and during one-off ONNX export. NOT a plugin
   runtime dependency.
   https://pytorch.org

5. QGIS
   QGIS Development Team
   License: GPL v2+
   Host application for the plugin.
   https://qgis.org/

6. Inria Aerial Image Labeling Dataset
   Maggiori E. et al. (2017)
   License: research use; see source.
   Used for mid-training of the localization U-Net.
   https://project.inria.fr/aerialimagelabeling/

================================================================================
CITATION
================================================================================

If you use this plugin or its weights in research, please cite both:

    Özel, A. (2026). beaconGIS: AI Building Damage Detection for QGIS.
        QGIS plugin. https://github.com/azeldev03/beaconGIS

    Gupta R. et al. (2019). xBD: A Dataset for Assessing Building Damage
        from Satellite Imagery. arXiv:1911.09296.
