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Redeployment is not the end of recovery. It is the start of a new evidence period in which the repaired policy must operate under real scene mix, sensor conditions and delayed reference labels. A calm monitoring design distinguishes ordinary variation from relapse, renews support coverage before it becomes stale and preserves a tested rollback route.
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Chapter contents
Choose the route closest to the decision, or read from Chapter 1. The chapters remain open; these grouped routes are a navigable index.
Route 1 · Define the decision
Route 2 · Work the ledgers
Route 3 · Transfer the method
Route 4 · Check limits and evidence
Route 5 · Ask and continue
CHAPTER 1 OF 12 · DEFINE THE DECISION
1. Start the monitoring clock at redeployment
Declare the redeployed policy version, supported strata, reference baseline and observation start. Mixing pre-rollback and post-redeployment results can hide whether the repair worked.
Track both technical performance and the scene distribution that gives the metric meaning. Stable average accuracy can coexist with loss of support in a rare but consequential stratum.
CHAPTER 2 OF 12 · DEFINE THE DECISION
2. Define relapse as a pattern, not one bad scene
A relapse trigger combines magnitude, persistence and consequence. One difficult cloud scene may invite review; repeated harm in an unsupported biome may require routing restriction or rollback.
Use leading indicators such as support density and routing uncertainty alongside lagging indicators such as confirmed omission error.
CHAPTER 3 OF 12 · WORK THE LEDGERS
3. Build a post-redeployment ledger
A land-cover system monitors four strata after a repaired routing gate returns to service.
| Stratum | Weekly volume | Fresh references | Support score | Monitoring state |
|---|---|---|---|---|
| Urban | 4,800 | 120 | 0.94 | Normal |
| Cropland | 3,100 | 85 | 0.88 | Normal |
| Wetland | 620 | 18 | 0.63 | Renew support |
| Smoke-affected forest | 240 | 6 | 0.39 | Restrict and audit |
The state determines action: normal monitoring, targeted collection, route restriction or rollback. Each transition needs a named threshold and owner.
CHAPTER 4 OF 12 · WORK THE LEDGERS
4. Worked relapse-trigger decision
The redeployed policy has a weekly harm limit of 4%. A two-window rule requires both recent degradation and enough fresh labels to make the estimate usable.
| Window | Confirmed harm | 95% interval | Support floor met? | Action |
|---|---|---|---|---|
| Week 1 | 3.1% | 1.8–5.0% | Yes | Continue |
| Week 2 | 4.8% | 3.1–7.2% | Yes | Warning |
| Week 3 | 5.6% | 3.9–7.9% | Yes | Relapse trigger |
| Smoke stratum | 9.2% | 5.4–14.7% | No | Restrict plus collect |
The policy rolls back only for the affected route while the old safe baseline handles smoke scenes. Targeted references test whether sensor artefacts or routing drift caused the failure.
CHAPTER 5 OF 12 · WORK THE LEDGERS
5. Renew support coverage deliberately
Reference collection should replenish coverage where uncertainty and consequence meet, not merely where labels are cheapest. Reserve some sampling for blind surveillance so new failure modes can appear.
Record acquisition date, geography, season, sensor, label protocol and route exposure. Coverage can decay even when sample count grows.
CHAPTER 6 OF 12 · TRANSFER THE METHOD
6. Keep rollback and recovery paths alive
A fallback that has not been tested recently may fail when needed. Run periodic shadow comparisons, interface checks and small rollback drills.
Redeployment authority should remain conditional until monitoring windows, support renewal and incident response have all operated successfully.
CHAPTER 7 OF 12 · TRANSFER THE METHOD
7. Changed-context transfer: flood mapping
Transfer the workflow to emergency flood mapping where dry-season validation cannot cover turbid water, cloud, urban shadow and sensor saturation during an event.
The transfer check distinguishes a genuine performance relapse from a new support gap. Both may require restriction, but only the evidence trail can show which repair is appropriate.
CHAPTER 8 OF 12 · TRANSFER THE METHOD
8. Connect rollback, safe deployment and collection
Start with the rollback recovery and redeployment guide. Use the support-drift guide for shadow comparisons.
Return to the lookahead audit guide when today’s label changes what can be learned tomorrow.
CHAPTER 9 OF 12 · CHECK LIMITS AND EVIDENCE
9. Limits and common misconceptions
A quiet monitoring dashboard does not prove safety if references arrive too slowly or exclude difficult strata. Missing evidence is not zero harm.
Repeated rollback is not a complete learning system. It must feed a versioned diagnosis, collection plan and acceptance gate.
CHAPTER 10 OF 12 · CHECK LIMITS AND EVIDENCE
10. Practical evidence checklist
Archive deployed policy and gate versions, baseline, route definitions, scene mix, support metrics, reference lag, alert thresholds, overrides and incident owners.
For each trigger, preserve the raw scenes, pre-trigger predictions, confirmed labels, interval calculations, restriction scope, rollback test and reopening evidence.
CHAPTER 11 OF 12 · ASK AND CONTINUE
11. Frequently asked questions
Can the policy remain live while support is renewed?
Only within strata where the declared evidence and consequence permit it. Unsupported routes may need a safe fallback.
Should relapse triggers use one metric?
Usually not. Pair outcome metrics with coverage, routing and reference-lag indicators.
When does monitoring end?
Routine monitoring continues through the deployment lifecycle, though intensity can fall after sustained stable evidence.
CHAPTER 12 OF 12 · ASK AND CONTINUE
12. Sources and next reading
USGS provides field-sampling guidance for developing and verifying satellite remote-sensing models. NIST’s AI RMF Playbook describes post-deployment monitoring and risk response. Laroche and colleagues develop safe policy improvement with baseline bootstrapping. The monitoring ledgers are original teaching examples.
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