How to use Carbon-Eye
From creating an account to reading your first validated result - everything below matches the real dashboard, not a simplified version of it.
Six steps, start to finish
- 1
Create an account
Sign up with an email address (Clerk-hosted, no special setup) and you're taken straight to the dashboard.
- 2
Choose your area
Pick one or more of Kenya's 47 counties - search by name, select a whole region in one tap, or switch to “Draw area” to outline a custom boundary directly on the map.
- 3
Set the accuracy preset, year and units
Quick preview samples fewer pixels for a fast look; High accuracy samples more for a more reliable estimate. Pick a reference year (or average a range of years), and whether results default to carbon, biomass, or both.
- 4
Run the analysis
This kicks off a real Earth Engine pipeline - not a lookup against a pre-computed map. Random Forest, Gradient Tree Boosting and an SVM each train and validate against your exact selection, usually finishing within a few minutes.
- 5
Ask the guide if anything's unclear
Every run has its own “Ask the Environmental Guide” chat, plus a one-click AI-written executive summary - both grounded in that run's actual numbers, not generic answers.
- 6
Save, export or share
Save a run to your portfolio to compare it with others later, download a PDF report or the county CSV, or generate a read-only link to share results without giving someone dashboard access.
The ten results tabs, at a glance
Every finished run opens the same ten tabs. You don't need to read them in order — Map and Briefing cover the headline story; the rest are there when you need more depth.
Map
The default landing tab: a plain-language explanation of the run, captioned headline numbers, and the interactive carbon/AGB map itself, with a model picker and overlay layers.
Validation
Actual-vs-predicted scatter plots and a full RMSE / MAE / bias / MAPE / R² table, one row per model, so you can see exactly how reliable each one was on held-out data.
Model Comparison
Where the three models agree and disagree, spatially: an RF-vs-GTB scatter plot alongside |RF−GTB| and three-model-spread maps.
Data Quality
An explainable 0–100 screening score built from sample volume, validation strength, model agreement and geographic coverage - a quick read on whether to trust this particular run.
Zonal Stats
Per-county table of mean / min / max / total carbon and tree-cover-loss percentage, sortable and exportable as CSV.
Importance
A ranked bar chart of which satellite, climate and terrain predictors the Random Forest and boosting models actually relied on.
Restoration
A growth-curve chart projecting carbon accumulation over a chosen number of years for a restoration scenario, alongside a restoration-suitability map.
Decision Tools
A carbon-change explorer comparing two reference years, plus a simple restoration-project revenue calculator (area × growth rate × carbon price).
Briefing
An AI-generated (or deterministic fallback) executive summary of the whole run - strongest model, uncertainty, county hotspots and cautions - downloadable as text.
Report
Download a shareable PDF summary, and manage your saved-project portfolio with a combined PDF across runs.
First time on the dashboard itself?
The New Analysis form has its own short, interactive tour built in the first time you sign in — look for the “Take the tour” link on the New Analysis card if you want to replay it.