
Field-ready AI platforms for water and road infrastructure
DroMii connects drone, satellite, GIS, and public datasets into operational workspaces that help teams understand infrastructure conditions and prioritize action.


From field data to operational decisions
Map-based exploration, AI analysis, layer comparison, and visual reporting are combined into one workflow to reduce repetitive review work.
Data Layer
Unify satellite, drone, public, and field data as GIS layers.
AI & Rule Analysis
Transform pollution sources, road damage, and change signals into actionable indicators.
Map Workspace
Review results through maps, layers, and comparison views.
Decision Support
Identify priority areas, risky segments, and next actions.
DroMii Products
Different infrastructure problems, solved through the same data, AI, and GIS operating model.

K-AQUAS
Dam and watershed GIS analytics for water pollution decisions
K-AQUAS integrates satellite imagery, land-cover maps, water quality indicators, and pollution source data around dams and watersheds so managers can identify changes and risks faster.
- Dam and watershed GIS monitoring
- Satellite/drone data comparison
- Land-cover and water layers
- Pollution source detection
- Priority area planning
Dam/weir map, AOI, river, facility, and cadastral layers
NDVI/NDWI, land cover, water quality layers, pollution load
Land-cover comparison, pollution projects, priority areas

D-ROAD
Road asset condition analytics for maintenance decisions
D-ROAD analyzes road imagery and spatial data with AI to detect potholes, cracks, and risk factors while helping maintenance teams organize repair priorities.
- Road damage detection
- Risk factor analysis
- Spatial issue history
- Maintenance prioritization
Road segments, location-based risks, field images
Potholes, cracks, pavement issues, facility risk signals
Inspection visualization, repair priorities, management reports
Validate through demos, then scale with field data.
Use the live demos and existing detail pages to review the product flow before deeper implementation.

