microsoft · rain-Brian · Jun 3, 2026 · Jun 3, 2026
diff --git a/docs/bioacoustics.md b/docs/bioacoustics.md
@@ -1,4 +1,5 @@
 ---
+title: "MegaDetector-Acoustic: Bioacoustic Species Identification"
 description: "MegaDetector-Acoustic: open-source bioacoustic AI for wildlife monitoring. Audio classification, bird detection, and species identification from passive acoustic recordings."
 tags:
   - MegaDetector-Acoustic
@@ -15,27 +16,27 @@ tags:
 ## What's included
 
 - **CLI scripts** for dataset preparation (`prepare_dataset.py`), training (`train.py`), and inference (`inference.py`)
-- **`ResNetClassifier`** — PyTorch Lightning module for spectrogram classification (binary and multiclass)
+- **`ResNetClassifier`**: PyTorch Lightning module for spectrogram classification (binary and multiclass)
 - **Mel-spectrogram preprocessing** with optional GPU acceleration
 - **Annotation readers** (COCO-like JSON), including support for the PteroSet / Raven Pro format
-- **`MD_AudioBirds_V1`** — a pre-trained bird classifier distributed as ONNX for direct inference
+- **`MD_AudioBirds_V1`**: a pre-trained bird classifier distributed as ONNX for direct inference
 
 See the [MegaDetector-Acoustic model zoo](model_zoo/bioacoustics.md) for the released models.
 
 ## Demo
 
 The end-to-end notebook at [microsoft/MegaDetector-Acoustic](https://github.com/microsoft/MegaDetector-Acoustic) walks through:
 
-1. **Data exploration** — annotation counts, species distribution
-2. **Inference** — run `MD_AudioBirds_V1` on real recordings, visualise predictions vs. ground truth
-3. **Training** — build COCO-style annotations, binary classification (target vs. noise), multiclass classification
+1. **Data exploration**: annotation counts, species distribution
+2. **Inference**: run `MD_AudioBirds_V1` on real recordings, visualise predictions vs. ground truth
+3. **Training**: build COCO-style annotations, binary classification (target vs. noise), multiclass classification
 
 It uses recordings from the [PteroSet](https://zenodo.org/records/19137071) dataset.
 
 ## Projects using this module
 
-- **[PteroSet](https://github.com/microsoft/PteroSet)** — Machine-learning pipeline for detecting and classifying tropical bird vocalisations from passive acoustic monitoring, with leave-one-project-out cross-validation.
-- **[CookInlet_Belugas](https://github.com/microsoft/CookInlet_Belugas)** — Passive acoustic monitoring for endangered Cook Inlet beluga whales. A two-stage pipeline covering cetacean signal detection and multi-species classification (beluga, humpback, killer whale), plus an active-learning loop for domain adaptation.
+- **[PteroSet](https://github.com/microsoft/PteroSet)**: Machine-learning pipeline for detecting and classifying tropical bird vocalisations from passive acoustic monitoring, with leave-one-project-out cross-validation.
+- **[CookInlet_Belugas](https://github.com/microsoft/CookInlet_Belugas)**: Passive acoustic monitoring for endangered Cook Inlet beluga whales. A two-stage pipeline covering cetacean signal detection and multi-species classification (beluga, humpback, killer whale), plus an active-learning loop for domain adaptation.
 
 ## Install
 

diff --git a/docs/build_mkdocs.md b/docs/build_mkdocs.md
@@ -1,4 +1,5 @@
 ---
+title: "Build the PyTorch-Wildlife Documentation Site"
 description: "How to build and deploy the PyTorch-Wildlife MkDocs documentation site locally and to GitHub Pages."
 tags:
   - PyTorch-Wildlife

diff --git a/docs/cite.md b/docs/cite.md
@@ -1,4 +1,5 @@
 ---
+title: "Cite PyTorch-Wildlife and MegaDetector"
 description: "Cite PyTorch-Wildlife and MegaDetector in your research. BibTeX entries for Hernandez et al. 2024 (PyTorch-Wildlife) and Beery et al. 2019 (MegaDetector)."
 tags:
   - cite PyTorch-Wildlife

diff --git a/docs/contribute.md b/docs/contribute.md
@@ -1,4 +1,5 @@
 ---
+title: "Contributing to PyTorch-Wildlife"
 description: "Contribute to PyTorch-Wildlife: guidelines for submitting issues, pull requests, and new models to the Microsoft Biodiversity open-source conservation AI platform."
 tags:
   - contribute PyTorch-Wildlife
@@ -19,7 +20,7 @@ Thanks for your interest in collaborating on PyTorch-Wildlife! Here you can find
 4. 🤝 Once we get your comments, we’ll move the issue to **“In progress”** and assign you as the collaborator.  
 5. 🛠 Work on it at your own pace, and when it’s ready, submit a pull request to the **`Collaborations`** branch. 
 6. 🔍 We’ll review your PR, run tests, and merge it into our **pre‑release** branch ahead of the next version.
-7. 🎉 After merging, we’ll mark the issue/task as **“Done”** and credit your contribution in the release notes—thank you!
+7. 🎉 After merging, we’ll mark the issue/task as **“Done”** and credit your contribution in the release notes. Thank you!
 
 ---
 

diff --git a/docs/contributors.md b/docs/contributors.md
@@ -1,5 +1,6 @@
 ---
-description: "PyTorch-Wildlife contributors — the researchers and engineers behind the Microsoft AI for Good Lab biodiversity tools."
+title: "PyTorch-Wildlife Contributors"
+description: "PyTorch-Wildlife contributors: the researchers and engineers behind the Microsoft AI for Good Lab biodiversity tools."
 tags:
   - PyTorch-Wildlife
   - contributors

diff --git a/docs/core_features.md b/docs/core_features.md
@@ -1,4 +1,5 @@
 ---
+title: "PyTorch-Wildlife Core Features: Detection, Classification, and Model Zoo"
 description: "PyTorch-Wildlife core features: unified framework for wildlife AI combining detection models, pre-trained weights, datasets, and utilities for conservation research."
 tags:
   - PyTorch-Wildlife features

diff --git a/docs/ecosystem-standards.md b/docs/ecosystem-standards.md
@@ -0,0 +1,80 @@
+---
+title: "Ecosystem Documentation Standards for Microsoft Biodiversity Projects"
+description: "The shared documentation standards every Microsoft Biodiversity ecosystem repo follows: site structure, metadata, structured data, cross-linking, and topic ownership."
+tags:
+  - documentation standards
+  - open source conservation AI
+  - Microsoft biodiversity AI
+---
+
+# Ecosystem Documentation Standards
+
+Every project in the Microsoft Biodiversity ecosystem publishes its documentation the same way, so
+the sites read as one family and each ranks as the canonical source for its own topic. This page is
+the reference for bringing a new repo up to that standard, distilled from the work on
+[MegaDetector](https://microsoft.github.io/MegaDetector/), which sets the pattern.
+
+## Site structure
+
+Each repo ships a MkDocs Material site under `docs/`, deployed to
+`https://microsoft.github.io/<Repo>/`. Use a single `<h1>` per page that matches the page title,
+keep a clear navigation tree, and give every concept its own page rather than one long README. A
+docs homepage leads with the value proposition in the first sentence, then links out to the
+detailed pages.
+
+## Page metadata
+
+Every content page carries front matter:
+
+- a `title` that is unique and leads with the page's primary topic,
+- a `description` of roughly 110 to 160 characters that reads as a search snippet,
+- a `tags` list scoped to that page's topic.
+
+`mkdocs.yml` sets `site_url` and a keyword-rich `site_description`. Descriptions stay distinct from
+one repo to the next so previews and snippets do not blur together.
+
+## Structured data and social cards
+
+A theme override (`overrides/main.html`) injects JSON-LD, gated per page so each block only appears
+where it belongs. A homepage carries a connected graph: a `WebSite` node, the project as
+`SoftwareSourceCode` (the hub uses `CollectionPage` plus an `ItemList` of the projects), and the Lab
+as the publishing `Organization`, cross-linked by `@id`. Add `sameAs` links to the GitHub repo and
+any package, paper, or model page so search engines can resolve the project as one entity. Interior
+pages carry a `BreadcrumbList`, and pages with a FAQ carry a `FAQPage`. Every page also emits Open
+Graph and Twitter Card tags with an absolute share image.
+
+## Build and freshness
+
+The site uses the `callouts` plugin so GitHub-style admonitions render in both the README and the
+docs, and `git-revision-date-localized` to show a real last-updated date. The deploy workflow checks
+out full history (`fetch-depth: 0`) so those dates are accurate. A self-hosted favicon and logo live
+in `docs/assets/` rather than loading from an external host.
+
+## Cross-linking and topic ownership
+
+The hub and the projects link to each other so the ecosystem reads as a connected graph rather than
+separate sites:
+
+- The hub links out to every project with a topic-specific anchor.
+- Each project links back to the hub with an umbrella anchor.
+- Each project carries a short "related projects" section, with one sentence on how each neighbor
+  differs, rather than an identical block of links on every page.
+
+Topic ownership keeps the projects from competing with each other. The hub owns the umbrella terms
+for Microsoft biodiversity AI and open-source conservation AI. Each project owns its own area:
+
+| Project | Owns |
+|---|---|
+| [MegaDetector](https://microsoft.github.io/MegaDetector/) | camera-trap image detection and blank-frame filtering |
+| MegaDetector-Acoustic (documentation coming soon) | terrestrial bioacoustic classification and species identification from sound |
+| [SPARROW](https://microsoft.github.io/SPARROW/) | solar-powered edge hardware, field deployment, and remote connectivity |
+| [PyTorch-Wildlife](https://microsoft.github.io/Pytorch-Wildlife/) | the deep learning framework, model zoo, training, and inference |
+
+When one project's page mentions a topic another project owns, it links to that owner rather than
+trying to rank for the term itself.
+
+## Writing
+
+Documentation is written to be read by people. Keep prose specific and sourced to the repository,
+vary sentence structure, and avoid filler. New pages add real material rather than restating the
+README. Claims about accuracy, scale, or performance point to a source or are softened.
diff --git a/docs/ecosystem.md b/docs/ecosystem.md
@@ -0,0 +1,91 @@
+---
+title: "The Microsoft Biodiversity Ecosystem: Open-Source Conservation AI Projects"
+description: "A guide to the Microsoft AI for Good Lab biodiversity ecosystem: which open-source conservation AI project fits camera traps, audio, field devices, or model development."
+tags:
+  - Microsoft biodiversity AI
+  - open source conservation AI
+  - wildlife monitoring AI tools
+  - AI for Good biodiversity
+  - conservation AI
+---
+
+# The Microsoft Biodiversity Ecosystem
+
+The Microsoft AI for Good Lab maintains a family of open-source projects for biodiversity
+monitoring and conservation. Each one owns a distinct part of the workflow, from detecting
+animals in imagery to classifying sounds, deploying field hardware, and building custom models.
+This page is the directory for the ecosystem and a guide to picking the right starting point.
+
+## Which project should I use?
+
+Start from what you already have or what you are trying to build.
+
+- **I have camera-trap photos and want to find the animals in them.** Use
+  [MegaDetector](https://microsoft.github.io/MegaDetector/), the detector that locates animals,
+  people, and vehicles in camera-trap images and clears the empty frames out of your review queue.
+- **I have audio recordings and want to identify what is calling.** Use
+  MegaDetector-Acoustic (documentation coming soon) for terrestrial
+  bioacoustic classification and species identification from sound.
+- **I am deploying a sensor in the field and need it to run on its own power.** Use
+  [SPARROW](https://microsoft.github.io/SPARROW/), the solar-powered edge device that runs these
+  models on site and sends results back over remote connectivity.
+- **I want to build, train, or integrate conservation models in my own code.** Use
+  [PyTorch-Wildlife](https://microsoft.github.io/Pytorch-Wildlife/), the deep learning framework
+  and model zoo that ties the ecosystem together in Python.
+- **I want to adapt a species classifier to my own region or dataset.** Use
+  [MegaDetector-Classifier](https://github.com/microsoft/MegaDetector-Classifier) to fine-tune
+  classification on top of detections.
+
+## Projects
+
+### Detection
+
+- **[MegaDetector](https://microsoft.github.io/MegaDetector/)** is where the ecosystem started.
+  It detects animals, people, and vehicles in camera-trap imagery and filters blank frames, which
+  removes the bulk of manual review on large datasets. See the
+  [camera-trap image detection documentation](https://microsoft.github.io/MegaDetector/) for run
+  paths, the model zoo, and output format.
+- **[MegaDetector-Overhead](https://github.com/microsoft/MegaDetector-Overhead)** brings detection
+  to aerial and drone imagery with point-based wildlife localization from overhead views.
+- **[MegaDetector-Sonar](https://github.com/microsoft/MegaDetector-Sonar)** extends detection to
+  sidescan sonar imagery for underwater wildlife surveys.
+
+### Audio
+
+- **MegaDetector-Acoustic (documentation coming soon)** handles the
+  audio side of monitoring: classifying calls and identifying terrestrial species from sound
+  recordings, the bioacoustic counterpart to image-based detection.
+
+### Classification
+
+- **[MegaDetector-Classifier](https://github.com/microsoft/MegaDetector-Classifier)** fine-tunes
+  species classification on top of detections, so you can adapt a model to the species and regions
+  in your own data.
+
+### Field hardware
+
+- **[SPARROW](https://microsoft.github.io/SPARROW/)**, the Solar-Powered Acoustic and Remote
+  Recording Observation Watch, is the edge device for remote deployments. It captures imagery and
+  audio, runs ecosystem models on site, and reports results back over satellite or cellular links.
+
+### Framework
+
+- **[PyTorch-Wildlife](https://microsoft.github.io/Pytorch-Wildlife/)** is the collaborative deep
+  learning framework and model zoo for conservation AI. It loads detection and classification
+  models in a few lines of Python and is the integration point for building on top of the ecosystem.
+
+## How the projects fit together
+
+A typical pipeline moves from capture to insight. A field unit such as SPARROW collects imagery
+and audio in a remote site. MegaDetector finds the animals in the images and discards empty frames.
+MegaDetector-Classifier or a model from the PyTorch-Wildlife zoo names the species in those
+detections, while MegaDetector-Acoustic does the same for the audio. PyTorch-Wildlife is the common
+framework that runs each model and lets you assemble these steps into your own workflow.
+
+## Get involved
+
+The projects share a home in the [Microsoft Biodiversity organization](https://github.com/microsoft/Biodiversity)
+and a community on [Discord](https://discord.gg/TeEVxzaYtm). See the list of teams already using
+these tools on the [collaborators page](collaborators.md), and the
+[ecosystem documentation standards](ecosystem-standards.md) if you are bringing a new project into
+the family.
diff --git a/docs/index.md b/docs/index.md
@@ -31,7 +31,9 @@ tags:
 ## 👋 Welcome to PyTorch-Wildlife
 **PyTorch-Wildlife** is an AI platform designed for the AI for Conservation community to create, modify, and share powerful AI conservation models. It allows users to directly load a variety of models including [MegaDetector](https://github.com/microsoft/MegaDetector), [DeepFaune](https://www.deepfaune.cnrs.fr/en/), and [HerdNet](https://github.com/Alexandre-Delplanque/HerdNet) from our ever expanding [model zoo](model_zoo/megadetector.md) for both animal detection and classification.
 
-Our scope now spans well beyond camera-trap imagery — we have active work in [MegaDetector-Acoustic](bioacoustics.md) for bioacoustic species identification, [MegaDetector-Overhead](model_zoo/other_detectors.md) for aerial wildlife detection, and edge computing for remote field deployments.
+Our scope now spans well beyond camera-trap imagery, with active work in [MegaDetector-Acoustic](bioacoustics.md) for bioacoustic species identification, [MegaDetector-Overhead](model_zoo/other_detectors.md) for aerial wildlife detection, and edge computing for remote field deployments.
+
+**New here?** The [Microsoft Biodiversity ecosystem guide](ecosystem.md) walks through every open-source project in the family and helps you pick the right one for camera-trap images, audio, field hardware, or model development. See who is already using these tools on the [collaborators page](collaborators.md).
 
 > **Coming from an older version?** OWL is now **MegaDetector-Overhead**, the bioacoustics module is now **MegaDetector-Acoustic**, and the repo has moved from `microsoft/CameraTraps` to `microsoft/Biodiversity` (old links redirect automatically). See the [full naming changes](releases/release_notes.md#naming-changes) in the v1.3.0 release notes.
 

diff --git a/docs/installation.md b/docs/installation.md
@@ -1,4 +1,5 @@
 ---
+title: "Install PyTorch-Wildlife: pip, conda, and Docker Setup"
 description: "Install PyTorch-Wildlife for camera trap AI and wildlife detection. Supports conda, pip, and Docker on Windows, macOS, and Linux with optional CUDA GPU acceleration."
 tags:
   - PyTorch-Wildlife installation

diff --git a/docs/license.md b/docs/license.md
@@ -1,4 +1,5 @@
 ---
+title: "PyTorch-Wildlife License"
 description: "PyTorch-Wildlife license information: MIT for the core package and utilities. Model-specific licenses (AGPL-3.0, CC BY-SA) are listed in the model zoo."
 tags:
   - PyTorch-Wildlife license

diff --git a/docs/megadetector.md b/docs/megadetector.md
@@ -1,4 +1,5 @@
 ---
+title: "MegaDetector in PyTorch-Wildlife: Camera-Trap Animal Detection"
 description: "MegaDetector: AI model for detecting animals, people, and vehicles in camera-trap images. MegaDetectorV5 and MegaDetectorV6 available via PyTorch-Wildlife."
 tags:
   - MegaDetector