CaBuAr Wildfire Spread Prediction

by amar-atoumco.huggingface.amar-atoum/cabuar-pipeline

Latent diffusion for post-fire burn scar prediction

  • Remote endpoint
  • 2 tools
  • MIT

Uptime

Checked  · every day

100%from 4 days of checks

Up without downtime on 4 of 4 days with data.

  • No downtime
  • Degraded
  • Down over 5 minutes
  • No data
  • Not checked yet

Response time

Median 1069 ms, slowest 5% 1272 ms for the handshake and the tool list, over 30 days.

1500 ms750 ms0

How far each check got

  • Reached the address 100%
  • Completed the handshake 100%
  • Listed its tools 100%

2 tools at the last answer.

Every day
DayResultChecksMedian
3 Nov (today)No downtime11167 ms
2 NovNo downtime1of 11239 ms
1 NovNo downtime1of 11120 ms
31 OctNo downtime11069 ms

A failure counts as down time only when a second check five minutes later fails too. How we check

Connect to the publisher's own endpoint

Add to ~/.config/eca/config.json (global) or .eca/config.json in the project:

{
  "mcpServers": {
    "cabuar-pipeline": {
      "url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
    }
  }
}
For agents: the install prompt

Every step above as one Markdown text, for an assistant installing it for you.

# Install the MCP server CaBuAr Wildfire Spread Prediction (`co.huggingface.amar-atoum/cabuar-pipeline`)

Its page: https://plug.directory/servers/co.huggingface.amar-atoum/cabuar-pipeline

It runs on the publisher's servers; the client connects to its URL.

Use the section for the person's client.

## Claude Desktop

Paste into Settings › Connectors › Add custom connector:

```text
https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse
```

## Claude Code

Run in a terminal:

```sh
claude mcp add --transport sse cabuar-pipeline https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse
```

## Cursor, Devin Desktop, LM Studio, Gemini CLI, oterm, ECA

Merge into the place for the person's client:

- Cursor: ~/.cursor/mcp.json
- Devin Desktop: ~/.config/devin/mcp_config.json
- LM Studio: mcp.json (Program › Install › Edit mcp.json)
- Gemini CLI: ~/.gemini/settings.json
- oterm: config.json in oterm's data folder (~/Library/Application Support/oterm on macOS, ~/.local/share/oterm on Linux, %APPDATA%\oterm on Windows)
- ECA: ~/.config/eca/config.json (global) or .eca/config.json in the project

```json
{
  "mcpServers": {
    "cabuar-pipeline": {
      "url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
    }
  }
}
```

## VS Code

Merge into .vscode/mcp.json:

```json
{
  "servers": {
    "cabuar-pipeline": {
      "type": "sse",
      "url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
    }
  }
}
```

## Zed

This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.

## Cline, AnythingLLM, Augment Code

Merge into the place for the person's client:

- Cline: cline_mcp_settings.json (MCP Servers › Configure › Configure MCP Servers)
- AnythingLLM: anythingllm_mcp_servers.json in the AnythingLLM storage plugins directory
- Augment Code: Augment Settings Panel – MCP section – Import from JSON (VS Code extension)

```json
{
  "mcpServers": {
    "cabuar-pipeline": {
      "type": "sse",
      "url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
    }
  }
}
```

## Continue

Merge into ~/.continue/config.yaml:

```yaml
mcpServers:
  - name: "cabuar-pipeline"
    type: sse
    url: "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
```

## Goose

This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.

## Codex

This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.

## mcp-client-cli, TUUI, BoltAI, Bob Shell, Kiro, Kiro CLI

This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.

## Agenvoy

This server's endpoint speaks only SSE, which this client's documentation does not list among its transports; use another client for it.

## Amp

Merge into ~/.config/amp/settings.json (user) or .amp/settings.json (workspace):

```json
{
  "amp.mcpServers": {
    "cabuar-pipeline": {
      "url": "https://amar-atoum-cabuar-pipeline.hf.space/gradio_api/mcp/sse"
    }
  }
}
```

Then restart the client, or reload its MCP servers, and ask it to list the server's tools to confirm it started.

Tools 2

  • CaBuAr_Pipeline_predict_on_cabuar_test_sample Run the CaBuAr V4 burn-scar projection on a held-out test sample of the CaBuAr ChaBuD split and score it against the ground truth: SSIM, pixel MSE and PSNR against the true post-fire image, and burn-scar IoU, F1, precision and recall against the CaBuAr ground-truth burned-area mask (ΔNBR > 0.05). Returns: Tuple of (pre_fire_false_colour_image, predicted_post_fire_false_colour_image, comparison_image with the true post-fire scene and the predicted burn scar against the ground-truth mask, metrics_markdown).
  • CaBuAr_Pipeline_predict_post_fire_from_pre_fire_npy Project the post-fire Sentinel-2 scene and the burn scar from a pre-fire Sentinel-2 image of an area in which a fire later occurs, using the CaBuAr V4 latent diffusion model. The burn scar is derived with the Normalized Burn Ratio difference, ΔNBR = NBR(pre-fire) - NBR(predicted post-fire), and a pixel counts as burned when ΔNBR > 0.05. The model does not predict whether or where a fire will ignite; it assumes a fire occurs in the scene. If a user passes a file as an input, use the upload_file_to_gradio tool, if present, to upload the file to the gradio app and create a Gradio File Input. Then use the returned path as the input to the tool. Returns: Tuple of (pre_fire_false_colour_image, predicted_post_fire_false_colour_image, delta_nbr_map_image, summary_markdown with the predicted burned fraction).

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