🌳 The taller the tree, the harder the fall. Determining tree height from space using Deep Learning …

Tree height measurement from satellite imagery is crucial for infrastructure risk assessment, particularly for power lines, railways and pipelines where vegetation interaction is a leading cause of outages

Traditional computer vision methods struggle with vegetation height estimation, while deep learning approaches can achieve LIDAR-like accuracy when properly trained

High-resolution satellite imagery (30-70cm per pixel) requires specialized processing due to non-parallel camera angles and semi-transparent vegetation

Seasonal timing is critical - measurements are only taken during “leaf-on” season (April-September in northern regions) when foliage is present

LIDAR remains the gold standard for terrain modeling and training data generation, offering:

• Ability to see through vegetation to ground level
• Up to centimeter-level accuracy
• Dense point clouds (30-50 points per square meter)

Key challenges include:

• Large data volumes (8.5GB+ per image)
• Need for stereo image pairs
• Regional variations in vegetation types
• Complex co-registration of imagery
• Limited availability of training data

The solution combines:

• Digital Surface Models (DSM) for overall height
• Digital Terrain Models (DTM) for ground level
• Deep learning for vegetation reconstruction
• Risk categorization (low/medium/high)

Success metrics include accurate height estimation compared to field measurements and LIDAR ground truth

Infrastructure companies use this data to make maintenance decisions and reduce outage risks through targeted vegetation management

Processing requires specialized tools like DrasterIO, PDAL, and JAX for handling large-scale geospatial data efficiently