Human Labeling Guide For Water-Change Review¶

This guide explains how people should describe visible water changes in OpenFloodAI review images or video clips.

Simple meaning: before training ML, reviewers need to use the same words for the same things.

This guide does not prove flood danger. It does not create alerts or public warnings.

For the machine-checkable label file format, see Human Label Format.

Why Labels Matter¶

Good labels help the project learn from human review.

Messy labels create messy models later.

Simple example: if one person writes "high river" and another person writes "danger," the computer may not know those mean a similar thing. A shared label like HIGH_WATER is clearer.

What Reviewers Should Look At¶

Start with the configured reference region.

The reference region is the watched part of the image, like a virtual ruler.

Look for:

whether the river or water area is visible
whether water covers more of the watched area than earlier
whether water is close to a bridge support, bank, marker, road, or other reference point
whether the view is too unclear to judge
whether the camera moved or changed view
whether the input is missing or unreadable

Simple example: if the camera watches a bridge pillar, focus on the marked part of that pillar. Do not label based only on sky, trees, traffic, or shadows outside the watched area.

Main Labels¶

Use one main label for each reviewed image, clip, or time window.

Label	Simple Meaning	Use When
`NORMAL_WATER`	Water looks close to the usual visible level.	The watched region looks normal compared with the baseline or earlier frames.
`RISING_WATER`	Water appears to be increasing.	Water covers more of the watched region than earlier in the video.
`HIGH_WATER`	Water appears high in the watched area.	Water is near or above a known marker, bank, bridge support, or reference line.
`WATER_GOING_DOWN`	Water appears to be decreasing.	Water covers less of the watched region than earlier.
`UNCLEAR_VIEW`	The reviewer cannot judge water state safely.	The view is too dark, blurry, blocked, rainy, foggy, glared, or confusing.
`CAMERA_MOVED`	The camera view changed from the expected scene.	The watched area no longer matches the original view.
`POOR_VISIBILITY`	The scene is visible but hard to trust.	Rain, fog, low light, glare, dirty lens, or compression makes review weak.
`MISSING_OR_UNREADABLE_INPUT`	The input cannot be reviewed.	The file, frame, or stream is missing, broken, or unreadable.

Simple Label Examples¶

`NORMAL_WATER`¶

Use this when the river is visible and looks close to the usual level.

Simple example: the same rocks, riverbank, or lower bridge area are visible as before.

`RISING_WATER`¶

Use this when water covers more of the watched region than earlier in the same clip or compared with a trusted baseline.

Simple example: the lower half of a bridge pillar was dry at the start, but later more of it is covered by water.

`HIGH_WATER`¶

Use this when water appears high near a known reference point.

Simple example: water reaches a painted marker, bridge support, road edge, or riverbank area that is normally dry.

Do not use this label to create a public warning. It only means the reviewed image shows high water evidence.

`WATER_GOING_DOWN`¶

Use this when water covers less of the watched region than earlier.

Simple example: more of the bridge pillar or riverbank becomes visible again.

`UNCLEAR_VIEW`¶

Use this when the reviewer cannot judge safely.

Simple example: the scene is too dark, the lens is blocked, or heavy rain makes the river hard to see.

Do not force unclear cases into NORMAL_WATER or HIGH_WATER.

`CAMERA_MOVED`¶

Use this when the camera no longer points at the expected scene.

Simple example: the reference region was supposed to show a riverbank, but now it shows sky or a road.

`POOR_VISIBILITY`¶

Use this when the image can still be seen, but confidence is low.

Simple example: the river is partly visible, but glare or fog makes the water boundary hard to trust.

`MISSING_OR_UNREADABLE_INPUT`¶

Use this when there is no useful image to label.

Simple example: the video file cannot open, the frame is blank, or the stream is missing.

How To Use The Reference Region¶

The reference region should guide the label.

Good reviewer question:

What changed inside the watched box?

Avoid this question:

Did anything change anywhere in the whole image?

Simple example: a car moving on a bridge outside the watched box should not become RISING_WATER.

If the watched box is wrong, add a reviewer note. Do not pretend the label is strong.

How To Mark Uncertainty¶

Use uncertainty when the evidence is not clear.

Good uncertainty notes:

uncertain: true
uncertainty_reason: too dark to judge
uncertainty_reason: heavy rain on lens
uncertainty_reason: camera view changed
uncertainty_reason: water boundary not visible

Simple example: if you think water may be rising but the image is foggy, label the main state as UNCLEAR_VIEW or POOR_VISIBILITY and explain why.

Do not force every frame into flood or no-flood.

Suggested Reviewer Notes¶

Future reviewed examples should include notes like:

what changed
where it changed
how confident the reviewer is
whether the reference region looked correct
whether privacy permission exists
whether the clip can be used for training later

Simple example:

label: RISING_WATER
uncertain: false
watched_region: lower bridge pillar
note: Water covers more of the lower pillar than at the start of the clip.
privacy_permission: approved_for_local_review
training_use: not_decided

Privacy And Permission¶

Do not label or share real camera footage unless the project has permission to use it.

Do not commit reviewed images, private videos, exact private GPS coordinates, camera URLs, passwords, phone numbers, or emails.

Simple example: labels from synthetic images can go in GitHub. Labels tied to a real private camera should stay local unless permission says otherwise.

Human Review Is Not A Public Warning¶

Labels are evidence for review and future testing.

They are not emergency instructions.

OpenFloodAI should keep these things separate:

human label -> training/testing evidence -> future model evaluation

and:

official public warning -> approved local authority or trusted emergency process

Simple example: a reviewer may label a clip as HIGH_WATER. That does not mean OpenFloodAI should send an evacuation message.

Good First Labeling Set¶

Start small.

A useful first local review set could include:

2 NORMAL_WATER clips
2 RISING_WATER clips, if approved examples are available
1 HIGH_WATER clip, if approved examples are available
2 POOR_VISIBILITY or UNCLEAR_VIEW clips
1 CAMERA_MOVED or bad-reference-region example
1 MISSING_OR_UNREADABLE_INPUT case

Simple meaning: a small clear set is better than a large messy set.

Current Boundary¶

This guide does not add ML training code, automatic retraining, uploads, alerts, dashboards, public warnings, or claims of flood detection accuracy.

It only gives reviewers a shared language for describing visible water changes.