Case study
Drone-Based Wildlife Monitoring and Anti-Poaching System
- Client: Hommeltek
- Industry: Drone Vision & Wildlife Protection
- AI capability: Computer Vision
- Thermal detection accuracy at 100m
- 98%
- RGB species classification accuracy at 50m
- 92%
The challenge
From 100 metres up, an animal is a few warm pixels
Monitoring large surveillance areas from the air makes animals appear very small, and real-time species identification requires high computational efficiency. The organization needed a system that could detect heat signatures first and then perform high-resolution identification to confirm species and age groups without significant latency.
What we built
A thermal sweep, then an identification pass
Wide coverage first, detail second.
Infrared · 100m
1/4The first pass flies at 100 metres with an infrared camera, covering large areas quickly.
From 100 metres up an animal is a few warm pixels. The pipeline splits the job in two: thermal imaging finds where the animals are, then a lower pass identifies what they are.
Thermal wide-area pass
Infrared detection at 100 metres finds heat signatures at 98% accuracy, day or night.InfraredTwo-stage by design
Wide coverage first, detail second. One pass alone would be either too coarse or too slow.Species, age and gender
The RGB pass at 50 metres classifies each animal at 92% accuracy.Continuous patrols
Repeat flights give density and movement data across the whole reserve, which is what deters poaching.
More from the flights
A lower thermal pass, with six animals detected and scored. 
Blesbok on open grassland, each animal boxed with its class and confidence.
The impact
Continuous cover over a large reserve
- 98% thermal detection accuracy at 100m
- 92% species classification at 50m
- Density and movement data for patrols
The system provides continuous, large-area monitoring that acts as a powerful deterrent against poaching. By providing accurate, real-time data on animal density and movement, Skaapwagter has significantly improved its conservation efforts and responded faster to potential threats.
Keep exploring
Related case studies
Real Estate & PropTechComputer VisionAI Property Image Intelligence for Listing Optimization & Compliance
PropTech founders and real estate leaders understand that property photos are the most critical asset in closing deals. PropTexx needed an automated way to enforce strict listing compliance and extract valuable insights from millions of unstructured real estate images without slow manual review.
- Property features tagged automatically
- 50+
- Compliance violation types detected
- 15+
Retail & E-commerceComputer VisionSneaker Visual Retrieval System
AlwaysLegit needed a way for their team at expos to instantly identify shoes from photos and match them against a massive 150K+ SKU database, often in challenging real-world lighting and backgrounds.
- SKUs searchable from a single photo
- 150K+
- Retrieval time per query
- <1s
Sports & MediaComputer VisionSoccer Match Analytics & Insights
The aim of this project is to give soccer analytics of matches being played. The goal is to build an AI tool that takes as input a video of a soccer match and then analyzes each player and extracts different analytics. This included: 1. Player and ball detection, 2. Player and ball tracking, 3. Player re-identification, 4. Field detection, 5. Event detection including passes, intercepts, goals etc.
Multi-stage CV pipeline. Real-time event detection. Player re-identification.
Read the case study
Book a strategy session
Talk to an AI engineer about your project
Tell us what you want to automate. The first call is a 30-minute working session with an engineer, not a sales pitch.
- Send the form, it takes 2 minutes
- We reply within 1 business day, under NDA if you need it
- A 30-minute call to scope feasibility and next steps