Combine related files, resize images, and reformat landing page layout

docs/conf.py

@@ -83,6 +83,7 @@
     "navbar_center": ["navbar2"],
     "header_links_before_dropdown": 5,
     "secondary_sidebar_items": ["page-toc", "searchbox", "edit-this-page", "sourcelink"],
+    "github_url": "https://github.com/weecology/DeepForest",
 }
 html_sidebars: Dict[str, Any] = {
     "index": [],

docs/index.rst

@@ -3,46 +3,20 @@ Welcome to DeepForest!
 
 DeepForest is a python package for airborne object detection and classification.
 
-**Tree crown prediction using DeepForest**
 
-.. image:: ../www/OSBS_sample.png
-   :width: 400
-   :alt: Alternative text
+|Tree crown prediction using DeepForest| |Bird detection using DeepForest|
 
-**Bird detection using DeepForest**
+.. |Tree crown prediction using DeepForest| image:: ../www/OSBS_sample.png
+   :width: 45%
 
-.. image:: ../www/bird_panel.jpg
-   :width: 400
-   :alt: Alternative text
+.. |Bird detection using DeepForest| image:: ../www/bird_panel.jpg
+   :width: 45%
 
 
-
-Why DeepForest?
-===============
-
-Observing the abundance and distribution of individual organisms is one of the foundations of ecology. Connecting broad-scale changes in organismal ecology, such as those associated with climate change, shifts in land use, and invasive species require fine-grained data on individuals at wide spatial and temporal extents.
-
-To capture these data, ecologists are turning to airborne data collection from uncrewed aerial vehicles, piloted aircraft, and earth-facing satellites. Computer vision, a type of image-based artificial intelligence, has become a reliable tool for converting images into ecological information by detecting and classifying ecological objects within airborne imagery.
-
-There have been many studies demonstrating that, with sufficient labeling, computer vision can yield near-human-level performance for ecological analysis. However, almost all of these studies rely on isolated computer vision models that require extensive technical expertise and human data labeling.
-In addition, the speed of innovation in computer vision makes it difficult for even experts to keep up with new innovations.
-
-To address these challenges, the next phase of ecological computer vision needs to reduce the technical barriers and move towards general models that can be applied across space, time, and taxa.
-
-DeepForest aims to be **simple**, **customizable**, and **modular**. DeepForest makes an effort to keep unnecessary complexity hidden from the ordinary user by developing straightforward functions like "predict_tile." The majority of machine learning projects actually fail due to poor data and project management, not clever models. DeepForest makes an effort to generate straightforward defaults, utilize already-existing labeling tools and interfaces, and minimize the effect of learning new APIs and code.
-
-Where can I get help, learn from others, and report bugs?
-=========================================================
-
-Given the enormous array of forest types and image acquisition environments, it is unlikely that your image will be perfectly predicted by a prebuilt model. Below are some tips and some general guidelines to improve predictions.
-
-Get suggestions on how to improve a model by using the [discussion board](https://github.com/weecology/DeepForest/discussions). Please be aware that only feature requests or bug reports should be posted on the issues page. The most helpful thing you can do is leave feedback on DeepForest `issue page`_. No feature or issue, or positive affirmation is too small. Please do it now!
-
-
-`Source code`_ is available on GitHub.
+**DeepForest** is a python package for training and predicting ecological objects in airborne imagery. DeepForest comes with prebuilt models for immediate use and fine-tuning by annotating and training custom models on your own data.
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
 
    ./introduction/index
    ./installation_and_setup/index
@@ -56,6 +30,8 @@ Get suggestions on how to improve a model by using the [discussion board](https:
    ./related_work/index
 
 
+`Source code`_ is available on GitHub.
+
 Indices and tables
 ==================
 * :ref:`genindex`

docs/introduction/index.rst

@@ -1,10 +1,79 @@
-Introduction
-============
+What is DeepForest?
+===================
 
-Welcome to the introduction section.
+DeepForest is a python package for training and predicting ecological objects in airborne imagery. DeepForest comes with prebuilt models for immediate use and fine-tuning by annotating and training custom models on your own data. DeepForest models can also be extended to species classification based on new data. DeepForest is designed for:
 
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents:
+1. Applied researchers with limited machine learning experience
+2. Applications with limited data that can be supported by prebuilt models
+3. Scientists looking for an easy-to-use baseline to compare methods against
 
-   landing
+DeepForest uses deep learning object detection networks to predict the location of ecological objects in airborne imagery. The design of DeepForest is intended to be simple, modular, and reproducible.
+
+.. image:: ../../www/image.png
+   :align: center
+
+For more about the motivation behind DeepForest, see some recent talks we have given on computer vision for ecology and practical applications to machine learning in environmental monitoring.
+
+Airborne Ecology
+----------------
+
+.. raw:: html
+
+    <iframe width="560" height="315" src="https://www.youtube.com/embed/O4K95-0W5FE?si=Vw8-yFLgRWaVIdbu" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+Practical Intro to Computer Vision in Ecology Research
+------------------------------------------------------
+
+.. raw:: html
+
+    <a href="https://youtu.be/wRBG74STulc?si=SRMWh6n9VlRU8kff">
+        <iframe width="560" height="315" src="https://www.youtube.com/embed/r7zqn4AZmb0?start=1080" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+    </a>
+
+Where can I get help, learn from others, and report bugs?
+---------------------------------------------------------
+Given the enormous array of forest types and image acquisition environments, it is unlikely that your image will be perfectly predicted by a prebuilt model. Below are some tips and general guidelines to improve predictions.
+
+Get suggestions on how to improve a model by using the `discussion board <https://github.com/weecology/DeepForest/discussions>`_. Please be aware that only feature requests or bug reports should be posted on the issues page. The most helpful thing you can do is leave feedback on the DeepForest `issue page`_. No feature, issue, or positive affirmation is too small. Please do it now!
+
+
+Why DeepForest?
+---------------
+
+Observing the abundance and distribution of individual organisms is one of the foundations of ecology. Connecting broad-scale changes in organismal ecology, such as those associated with climate change, shifts in land use, and invasive species require fine-grained data on individuals at wide spatial and temporal extents.
+
+To capture these data, ecologists are turning to airborne data collection from uncrewed aerial vehicles, piloted aircraft, and earth-facing satellites. Computer vision, a type of image-based artificial intelligence, has become a reliable tool for converting images into ecological information by detecting and classifying ecological objects within airborne imagery.
+
+There have been many studies demonstrating that, with sufficient labeling, computer vision can yield near-human-level performance for ecological analysis. However, almost all of these studies rely on isolated computer vision models that require extensive technical expertise and human data labeling.
+In addition, the speed of innovation in computer vision makes it difficult for even experts to keep up with new innovations.
+
+To address these challenges, the next phase of ecological computer vision needs to reduce the technical barriers and move towards general models that can be applied across space, time, and taxa.
+
+DeepForest aims to be **simple**, **customizable**, and **modular**. DeepForest makes an effort to keep unnecessary complexity hidden from the ordinary user by developing straightforward functions like "predict_tile." The majority of machine learning projects actually fail due to poor data and project management, not clever models. DeepForest makes an effort to generate straightforward defaults, utilize already-existing labeling tools and interfaces, and minimize the effect of learning new APIs and code.
+
+How can I contribute?
+---------------------
+
+DeepForest is an open-source python project that depends on user contributions. Users can help by:
+
+* Making recommendations to the API and workflow. Please open an issue for anything that could help reduce friction and improve user experience.
+* Leading implementations of new features. Check out the 'good first issue' tag on the repo and get in touch with the maintainers and tell us about your skills.
+* Data contributions! The DeepForest backbone tree and bird models are not perfect. Please consider posting any annotations you make on Zenodo, or sharing them with DeepForest maintainers. Open an `issue <https://github.com/weecology/DeepForest/issues>`_ and tell us about the RGB data and annotations. For example, we are collecting tree annotations to create an `open-source benchmark <https://milliontrees.idtrees.org/>`_. Please consider sharing data to make the models stronger and benefit you and other users.
+
+License
+-------
+
+Free software: `MIT license <https://github.com/weecology/DeepForest/blob/master/LICENSE>`_
+
+Citation
+--------
+
+Most usage of DeepForest should cite two papers:
+
+The first is the DeepForest paper, which describes the python package:
+
+Weinstein, B.G., Marconi, S., Aubry‐Kientz, M., Vincent, G., Senyondo, H. and White, E.P., 2020. DeepForest: A Python package for RGB deep learning tree crown delineation. Methods in Ecology and Evolution, 11(12), pp.1743-1751. https://doi.org/10.1111/2041-210X.13472
+
+The second is the paper describing the particular model. See `prebuilt` for citations for each model.
+
+.. _issue page: https://github.com/weecology/DeepForest/issues