Case: AI Model¶

We will study the case of a model used for building detection on Aerial image.

Configuration¶

To share your artificial intelligence model on the SharingHub, you need to set up your GitLab repository to include the topics sharinghub:aimodel from Settings and General:

To make your model usable by others, you need to create a README.md file. This file should begin with a YAML section describing your model's metadata, followed by a markdown section:

• The markdown part of your README must contain all the elements needed to train and/or make an inference with your AI model!
• The YAML section is delimited by three --- at the top of your file and at the end of the section. It contains the metadata presented in the [Reference].

Structure¶

The repository tree:

.
├── building_process.py
├── inference.ipynb
├── pretrained_weight.pt
├── README.md
├── requirements.txt
└── train.ipynb

Metadata¶

Here's the project metadata:

title: Unet neural network for building detection on Aerial image

related:
  dataset: https://gitlab.example.com/<dataset-project-repository>

assets:
- "*.ipynb"
- "*.pt"

gsd: 0.3
platform: aerial

providers:
  - name: CS Group
    roles: ["producer"]
    url: https://www.csgroup.eu

label:
    properties: ["Others", "Building"]
    description: "Building detection on Aerial image"
    type: "vector"
    classes: [
        {
            "name": "Others",
            "classes":  [0]
        },
        {
            "name": "Building",
            "classes": [1]
        }
    ]

ml-model:
    type: "ml-model"
    learning_approach: "supervised"
    prediction_type: "segmentation"
    architecture: "U-Net"

Let’s break down the project's metadata.

• title: override the default title, which is the name of the GitLab project "Unet Building Footprint Segmentation Aerial Image". [Ref]
• assets: define the files in the repository that we want to share with SharingHub. [Ref]
• gsd and platform are pure STAC properties. [Ref]
• providers: override the default providers. [Ref]
• label and ml-model are STAC extensions. Our model here uses the adapted STAC extensions with its use-case, label and ml-model. [Ref]