wildme/scout repository overview

⁠Scout

Scout is a software application designed to support annotators and lab leads processing imagery for the Kavango–Zambezi Transfrontier Conservation Area (KAZA TFCA) aerial survey of animals.

With Scout, you can:

• Ingest high volumes of .ARW and .JPG images collected from survey cameras
• Group images as "Tasks" that can be assigned to other users (e.g., image annotators) or machine learning (ML) models for bounding box creation and species labeling
• Review and ground truth annotated images for accuracy
• Draw division lines for overlapping image sequences with annotations
• Export CSV data files for statistical analysis

⁠System Requirements

NOTE: This software is designed to run on an air-gapped private network only. (Significant security modifications are needed before this platform is suitable for installation on a public network or cloud).

• A fast and stable internet connection.
• An 8GB-minimum thumbdrive.
• A dedicated, powerful laptop with a graphics processing unit (GPU) to run solely as the Scout server.
• Laptops with the Chrome web browser to connect to the Scout server for lab leads and annotators.

⁠Installation

For installation and first-time configuration steps, follow our Server System Setup⁠ documentation guide.

NOTE: Installation will remove all data and any operating system from the Scout Server laptop. Ubuntu Linux will be installed as the operating system.

⁠Important Links & Documentation

Use our Help Documentation⁠ to learn how to use Scout.

Visit Wild Me Professional Services⁠ to view the range of services we can offer.

⁠Support

For assistance with setup and installation, or for interest in development, join our Wild Me Development Discord⁠. For usage issues and customer support, visit our Community page⁠.

⁠Usage

Scout supports three user roles:

• System Administrators install and run Scout.
• Lab leads create and manage user accounts, upload images, coordinate tasks, review and ground truth annotations, draw division lines, and export data.
• Annotators draw and label bounding boxes representing animals on images.

Lab leads view, filter, manage, and export Tasks using the Task table:

Annotators and ML models use bounding boxes, species labels, and annotation labels to indicate animals in images:

⁠Techstack and Features

Scout uses Javascript (Sails.js) and integrated machine learning provided by Scoutbot⁠.

⁠License

Scout is licensed under the MIT open source license⁠.