LIRA-AI
Landscape Intelligence for Regeneration and Adaptation
Predictive intelligence for investable landscape regeneration · CGIAR MFL, CASP · Ethiopia
Connecting…
Open Omo-GhibeThe Central Shift
From "Where is degradation happening?" → "Why is the landscape degrading, what will happen next under climate change, what restoration pathway is most robust, who benefits or loses, and which actions should be financed first?"
Analysis Pipeline
Click to open each module1
Evidence Cloud
Fetch Sentinel-2, DEM, SoilGrids, CHIRPS
2
Landscape Diagnosis
Classify 9 indicators → health score
3
Climate Futures
CMIP6 SSP245/SSP585 risk projections
4
Syndromes & Causal Graph
Identify degradation syndrome + drivers
5
Regeneration Pathways
7 pathway packages matched to context
6
Investment Passports
Finance-ready portfolios + priority index
7
Monitoring & MELIA
Before-after tracking + re-prescription
Agent Roster
0 agentsStart backend to see agents
System
API routes—
Python files51
Tests16+ passing
EPSG32637 (UTM 37N)
Omo-Ghibe Basin — Real Data (7/7 sources confirmed)
Open Living LabData Sources — All Real
CMIP6 + GARDIAN →SoilGrids v2.0
SOC 28–68 g/kg confirmed
Sentinel-2 L2A
NDVI 0.10–0.45 confirmed
Copernicus DEM
Elev 977–2194m confirmed
ESA WorldCover 2021
Forest 2–62% confirmed
MODIS MOD13Q1
LPI 0.49–0.87 confirmed
CHIRPS v2.0
Rain 686–1859 mm/yr confirmed
ERA5 / Open-Meteo
T 15–29°C confirmed
NASA NEX GDDP CMIP6
SSP245/585 2030–2070
CGIAR CGSpace
1,659+ Ethiopia results