AI Tree Crown Detection from Aerial Imagery

We built an AI system that detects and segments individual tree crowns in aerial imagery, producing clean, non-overlapping polygon outputs ready for GIS integration.

Organizations working in forestry, environmental monitoring, and urban planning need accurate tree canopy data at scale. Manual annotation of aerial imagery is slow, expensive, and inconsistent. Existing automated approaches often produce overlapping detections or miss smaller crowns entirely.

We engineered a computer vision pipeline combining deep learning-based object detection with custom post-processing to ensure clean, non-overlapping segmentation masks. The system processes high-resolution aerial and satellite imagery and outputs GIS-ready polygon data.

Custom-trained instance segmentation model built on PyTorch

Geospatial data processing with Python (rasterio, geopandas, shapely)

Post-processing pipeline for non-maximum suppression and polygon refinement

Scalable inference pipeline containerized with Docker

The system delivers accurate, automated tree crown delineation at scale, replacing weeks of manual annotation work with minutes of compute time. Output polygons integrate directly into standard GIS workflows for forestry management, carbon estimation, and urban canopy analysis.