The environmental team found dump sites by eye. We built the detector and the gesture control.
Aerial imagery was reviewed frame by frame, and field operators could not work a touchscreen on site. Neuramonks delivered a dump site detection AI solution built from drone and satellite captures, driven by gesture in the field.
Delivered for the environmental team
- Aerial
- Drone and satellite
- Hands-free
- Gesture, not touchscreen
- Geo-accurate
- Coordinates to pixels
- In-field
- Works on site
Delivered for the L&D team
- Aerial
- Drone and satellite
- Hands-free
- Gesture, not touchscreen
- Geo-accurate
- Coordinates to pixels
- In-field
- Works on site
The Client's Problem
Sites found by eye, and a tablet nobody could use outdoors.
- Aerial imagery reviewed frame by frame
- Dump sites detected automatically
- Detections logged by tapping a screen
- Controlled by gesture, hands free
- Coordinates translated by hand
- GeoJSON mapped straight to image pixels
What We Delivered
Six pieces of work, one field system.
- 01 · Computer Vision
- Dump site detection
- A YOLO model trained for illegal dumping detection from drone imagery and satellite captures.
- 02 · Applied Research
- Geospatial coordinate mapping
- Powers geospatial waste detection mapping by converting GeoJSON coordinates into image pixels.
- 03 · Data Engineering
- Aerial imagery pipeline
- Handles raw .tif captures without manual preparation.
- 04 · IoT Engineering
- Gesture input integration
- Field devices driven by hand, not by touchscreen.
- 05 · Product Engineering
- Map overlay and review
- Detections shown on the terrain they belong to.
- 06 · Applied AI
- Complex terrain handling
- Holds accuracy where ground and waste look alike.
· Across all six · Survey and location data
- Works where signal does not
- Runs in the field with poor connectivity, syncing when it returns.
- Encrypted end to end
- Imagery and detection records encrypted in transit and at rest.
- Access-controlled by role
- Field crews and analysts see only their own scope.
Reviewing aerial imagery by eye?
We scope which pieces your build actually needs, in 30 minutes.
The Result
Detections you can act on without touching a screen.
Detection only helps if a field operator can use it where the waste is. Pairing illegal dumping detection from drone imagery with gesture control meant the same system worked at a desk and on uneven ground with gloves on.
Hands-free
A dump site detection AI solution driven by gesture, so field crews work without a touchscreen.
How The Engagement Ran
Four phases, imagery to field.
- Phase 1
- Discovery
- Survey workflow and field constraints mapped with the team.
- Discovery
- Phase 2
- Detection model
- YOLO trained on their aerial and satellite captures.
- Phase 3
- Coordinate mapping
- GeoJSON to pixel transformation built and validated.
- Phase 4
- Gesture and rollout
- IoT gesture input integrated, then released to the field.
- YOLO
- Computer Vision
- GeoJSON
- IoT integration
- Gesture recognition
- Illegal Dumping Detection
Why Neuramonks
Why the environmental team chose Neuramonks.
- Outcome-driven delivery
- Detection accuracy and field usability agreed up front.
- Built for the field
- Designed for gloves, glare and uneven ground.
- Geospatially precise
- Detections land on the right coordinates, not near them.
- Deployable on your terms
- On-premises or air-gapped, covering all 4 delivery phases end to end.
Common Questions
What teams ask about this build.
How does AI detect dump sites from aerial imagery?
A dump site detection AI solution trained on drone and satellite captures finds waste sites in the image, and their positions are mapped back to real-world coordinates.
Why gesture control instead of a touchscreen?
Field operators work with gloves, in glare and on uneven ground, where a touchscreen is slow and unreliable. Gesture keeps their hands free.
How are detections tied to real locations?
Geospatial waste detection mapping transforms GeoJSON coordinates into image pixels, so a detection in the picture corresponds to a precise point on the ground.
What did the engagement include?
The detection model, geospatial coordinate mapping, the imagery pipeline, gesture input integration, map overlays and terrain handling.
How is survey data secured?
Imagery and detection records are encrypted in transit and at rest, with role-based access and operation where connectivity is poor.
