Live inference
Upload aerial imagery
Drop a top-down aerial image and the model will segment individual tree canopies. Use imagery zoomed to roughly street level. The model works best at resolutions of 8–25cm per pixel. No map labels or overlays. Processing takes 20–40s on CPU.
Or try a sample
→ Or select a region on the map below to pull live PDOK aerial tiles automatically.
Research
About the model
This model is the output of my final year thesis at Munster Technological University: a comparative analysis of three instance segmentation architectures for detecting individual tree canopies in urban aerial imagery.
Urban green spaces are critical to environmental sustainability and quality of life, but effective monitoring at scale remains difficult. This research investigates whether deep learning can automate tree crown delineation accurately enough to be useful for urban forestry and planning applications.
The three models evaluated were YOLOv11, Mask R-CNN, and YOLACT++. YOLOv11x-seg, the model running on this site, outperformed the others across all key metrics.
About
Who I am
Bartlomiej Tedys
Ireland → Netherlands
I'm Bartek, a software developer and ML engineer who just finished a BSc in Software Development at Munster Technological University in Cork, with a semester abroad at Hogeschool van Amsterdam.
My thesis compared three instance segmentation models for detecting individual tree canopies in aerial imagery. YOLOv11x came out on top. The model on this page is the result of that work.
I'm based in Ireland right now and relocating to the Netherlands, a country I have a lot of respect for, both technically and professionally. I'm looking for roles in geospatial ML, computer vision, or software development in the Dutch tech scene.
Beyond the trees: I've built web apps, data pipelines, mobile applications, and done freelance development for small businesses. I like problems that sit at the intersection of data and real-world impact.
Available for work - open to roles in the Netherlands
Technical skills
ML / Vision
Backend
Frontend
Geospatial
Education
Munster Technological University
2021 – 2025
BSc Software Development (Honours)
Cork, Ireland
→ Thesis: Comparative analysis of instance segmentation models for urban tree crown delineation
Hogeschool van Amsterdam
2023 – 2024
Erasmus Exchange - Big Data & Mobile App Development
Amsterdam, Netherlands
Other work
2024
Caeli - Computer Vision Internship
Internship at Caeli, a Dutch startup using AI to monitor vegetation from aerial imagery. Trained and evaluated object detection models on satellite and drone data. This experience that directly led to my thesis on tree canopy segmentation.
2023
M&L Home Builds - Business Website
Designed and built a conversion-focused website for an Irish home renovation company. The site significantly increased their inbound leads within weeks of launch. Serves as a good reminder that clean frontend work has real business impact.
Contact
Get in touch
Whether you have a project in mind, want to discuss geospatial ML, or are looking to hire - I'm open to conversations.
barttedys.nl
Model: YOLOv11x-seg · Data: PDOK Beeldmateriaal · Built with Next.js + Modal