Counting Trees from Satellite Imagery with Noisy Supervision

Counting individual trees is a fundamental task for environ-mental monitoring, yet remains largely unexplored with satellite imagery.At these resolutions, isolated trees may still be identifiable, but crownboundaries become ambiguous in dense forests, making the notion of anindividual tree inherently ill-defined. Moreover, large-scale manual an-notations of individual trees are prohibitively expensive. While scalablesupervision can be derived from airborne LiDAR, the resulting anno-tations are noisy and difficult to exploit effectively. We address thesechallenges by formulating tree counting as a spatial density matchingproblem supervised through Unbalanced Optimal Transport. This formu-lation naturally accommodates both precise localization of isolate treesand robust density estimation in dense forests. We further introduce aself-correction mechanism that leverages transport residuals to progres-sively refine noisy supervision during training. We evaluate our approachon TinyTrees, a new benchmark spanning three continents and threesatellite sensors, comprising over 216 million tree annotations (including639k manually verified instances) across 25 890 km2 . Our method con-sistently outperforms detection-based, regression-based, and transport-based distribution-matching baselines, demonstrating the effectivenessof unbalanced transport and reliability-aware supervision for large-scaletree counting from satellite imagery. Code, data and models are availableat github.com/dgominski/treematch.