Case study · Government
Automation of Geographic Information Systems (GIS) for Natural Resource Management (NRM)
How remote sensing, GIS, and AI automate the monitoring and management of natural resources at scale, from change detection to wildlife counts.
- Client
- Natural resource management programs
Additional context
Turning satellite imagery and field data into a living map that flags change, encroachment, and risk on its own.
01 / Challenge
The problem in front of us.
Monitoring and managing natural resources across large areas is slow and manual, and visualising, querying, analysing, and interpreting location-tied data needs the right toolchain.
02 / Approach
How we set the work up.
We combine remote sensing with GIS and layer in AI, machine learning, and deep learning to turn imagery into timely, reliable, accurate information for prompt decisions.
03 / Solution
What we built.
Five applications: AI-assisted GIS automation for faster decisions; convolutional neural networks comparing satellite images over time to detect resource change; automated detection of illegal activity such as forest and river-bed encroachment and disasters like forest fires; mobile apps for field-based monitoring; and AI to detect, count, and identify wildlife for conservation.
04 / Outcome
What it has held up to.
Automation becomes the torchbearer for effective, efficient natural resource management, with faster, more reliable monitoring and timely alerts.
Stack
What it runs on.
- Remote Sensing
- GIS
- Machine Learning
- Deep Learning
- Convolutional Neural Networks
- Mobile applications
Tell us what you're trying to ship.
We'll start with a two-week diagnostic. No slides, no promises we can't keep.