---
title: Flood mapping
emoji: 🌊
colorFrom: blue
colorTo: indigo
sdk: static
pinned: false
short_description: In-browser Sentinel-2 flood mapping with WorldFloods v2
---

# Flood mapping — WorldFloods v2 · Sentinel-2 · in-browser

Draw a study area, pick a pre-flood baseline scene and a post-flood scene, and
run the ml4floods **WorldFloods v2** U-Net **entirely in your browser** — no
server, no account, no API keys.

- **Scenes**: public Sentinel-2 L2A archive on AWS Open Data, searched via the
  [Element84 Earth Search](https://element84.com/earth-search/) STAC API and
  read as cloud-optimized GeoTIFFs with HTTP range requests (geotiff.js).
- **Model**: [WF2_unetv2_bgriswirs](https://huggingface.co/isp-uv-es/ml4floods)
  (6 bands: B2, B3, B4, B8, B11, B12) exported to ONNX with normalization baked
  in, executed with onnxruntime-web (WebGPU when available, WASM otherwise).
- **Flood definition**: water probability ≥ threshold in the post scene on
  pixels that were land in the baseline scene, excluding thick cloud,
  clipped to the drawn polygon.
- **Outputs**: flood polygons (GeoJSON), flood mask (GeoTIFF), overlay PNG,
  stats JSON — all generated client-side.

## Caveats

- The model was trained on Sentinel-2 **L1C (TOA)**; here it runs on **L2A
  (surface reflectance)** from the open archive. Results are close but
  validate against known events before operational use.
- Optical method: clouds are blind spots. Scene ranking uses scene-level cloud
  cover; the app reports the cloud share inside your AOI per run.
- Study areas are capped at 1,000 km² (browser memory). A run downloads
  ~50–120 MB of imagery.

Full method description, assumptions and references: **[METHODOLOGY.md](METHODOLOGY.md)**.

Built by [IWMI](https://www.iwmi.org). Model and method:
[spaceml-org/ml4floods](https://github.com/spaceml-org/ml4floods).