NISAR-Ready Vector Intelligence
Satellite-assisted habitat and human-exposure prioritization for field inspection, with an explicit roadmap for entomological validation.
Explore GeoAI Research
Saptrishi Research
Building evidence-first public-health intelligence.
Saptrishi Research develops transparent, field-oriented methods for community medicine. Current programs include NISAR-ready GeoAI for vector surveillance and deterministic reconstruction of objective resting ECG and treadmill-test measurements.
Current Research Programs
Two problems, one standard: measurable evidence before claims.
Each program publishes its intended inputs, mathematical or computational method, validation status and scientific limits.
Rest-to-Exercise ECG Reconstruction
A deterministic protocol that converts a resting 12-lead ECG and treadmill-test report into auditable objective measurements, without diagnostic prediction.
Open Cardiac Protocol Secure Report Intake
Flagship Project
A clearer path from satellite signals to field inspection
The prototype turns environmental and human-proximity signals into inspection priorities while keeping field evidence at the centre of every decision.
Community medicine research concept
NISAR-Ready Vector Intelligence for Community Medicine
Prepared by Abhishek Dubey, the project is designed to evolve with L-band and S-band SAR inputs. Its operational goal is deliberately practical: help field teams decide where to inspect first after rainfall, complaints or seasonal vector-control alerts.
Current stage
Working prototype
Satellite-context comparison and inspection-priority surfaces are ready for structured field validation.
Next milestone Field evidenceObserve
Read wetness, seasonal surface change, vegetation, terrain and nearby human-exposure signals.
Prioritize
Identify which lanes, drains, plots or peri-domestic spaces may deserve inspection first.
Verify
Use the map to guide field checks, then compare model priorities with observed habitat and mosquito evidence.
Prototype Output
What the prototype produces
Satellite context is translated into adjusted exposure surfaces and local inspection points, creating a practical starting point for field teams.
These maps demonstrate the inspection-priority workflow. They are not presented as NISAR-derived disease maps and do not confirm larvae, adult mosquitoes or disease transmission without field validation.
Concept Understanding
See the idea behind the research
This short concept video introduces the project’s central idea: use AI and geospatial signals to make public-health inspection more focused, while leaving decisions with qualified field teams.
- AI supports prioritization, not final public-health decisions.
- SAR can support inspection planning through cloud cover.
- Dual-band radar can strengthen wetness and surface-change signals.
- Human proximity helps make a habitat signal operationally relevant.
Research Basis
Where NISAR fits
Synthetic Aperture Radar can observe land surfaces day or night and through cloud cover. That makes it especially relevant during monsoon and post-rainfall periods, when optical imagery may be limited.
NISAR L-band SARdeeper landscape response for vegetation structure, wetlands and persistent moisture patterns
NISAR S-band SARsurface-level response that can complement wet terrain and built-up contrast
All-weather imagingsupports monitoring when cloud cover limits ordinary satellite views
Repeat observationhelps compare rainfall windows, seasonal change and hotspot persistence
Field-priority layerconverts SAR-informed signals into inspection guidance, not disease claims
Validation Roadmap
From prototype to field-ready evidence
The model is an inspection-priority prototype. These validation steps must come before any early-warning claim.
Entomological validation
Inspect high, moderate, watch and low score points for stagnant water, larvae, pupae, adult mosquito evidence and habitat type.
Household and container survey
Compare Aedes-oriented outputs with House Index, Container Index and Breteau Index where field surveys are available.
Disease data linkage
Use ward or locality-level case data with rainfall and temperature lags before calling the system an early-warning model.
Scientific and ethical guardrails
- This is not a confirmed disease prediction model.
- The map should trigger field inspection, not automatic chemical application.
- Larvicide or insecticide decisions should follow official public-health protocol.
- If disease case data is added later, it should be aggregated and privacy-safe.
Research Internships
Research interns can apply to work on Saptrishi projects.
We welcome serious students and early researchers who want exposure to applied AI, public-health data, geospatial analysis and field-oriented product thinking. Interns may contribute to literature review, dataset preparation, validation planning, map interpretation, documentation and prototype testing.
Good fit for
- Medical, public health or community medicine students
- Engineering, data science or GIS learners
- Students interested in AI for social impact
- Applicants who can write clearly and work with evidence
Research Application
Apply for research internship or collaboration.
Share your background and the area you want to work on. This form is for research internships, academic discussions and field-collaboration inquiries, not student test enrollment.