InspirationLIVA GeoSense AI was inspired by the need to make geospatial and satellite-derived information more accessible and actionable for communities, farmers, and decision-makers.
Agriculture is highly dependent on changing environmental conditions, yet valuable information from remote sensing can be difficult to interpret without technical expertise. I wanted to build a solution that could bridge that gap by transforming geospatial data into simple, understandable intelligence.
The idea behind LIVA GeoSense AI is to connect remote sensing, geospatial analysis, artificial intelligence, and decision support in one platform. A key part of the current prototype is NDVI analysis, which uses near-infrared (NIR) and red reflectance values to calculate vegetation health and provide an understandable classification.
The project started as an exploration of how technology could help people move from simply observing environmental data to understanding what that data means and making better decisions from it.
LIVA GeoSense AI is being developed as a foundation for a broader geospatial intelligence platform, with the goal of eventually incorporating richer satellite observations, location intelligence, risk analysis, and actionable recommendations.
What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for Liva Geosense
Built With
- css
- geospatial
- github
- html
- javascript
- python
- remotesensing
- render
- resta-api