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Java鐭ヨ瘑鍒嗕韓緗?- 杞繪澗瀛︿範(fàn)浠庢寮€濮嬶紒聽聽聽聽
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鏃墮棿:2025-06-01 10:46鏉ユ簮:http://sh6999.cn 浣滆€?杞澆  渚墊潈涓炬姤
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Highlights
Sub-meter resolution canopy height maps using self-supervised learn-
ing and a vision transformer trained on Aerial and GEDI Lidar
Jamie Tolan1, Hung-I Yang1, Ben Nosarzewski1, Guillaume Couairon2 , Huy
Vo2 , John Brandt3, Justine Spore3, Sayantan Majumdar4, Daniel Haziza2,
Janaki Vamaraju1, Theo Moutakani2, Piotr Bojanowski2, Tracy Johns1, Brian
White1, Tobias Tiecke1, Camille Couprie2
•
0.5 meter resolution canopy height maps at jurisdictional scale are re-
leased.
•
Improved performance from Self-Supervised Learning (SSL) and vision
transformers.
•
First use of SSL and vision transformers for canopy height estimation.
•
Low resolution GEDI and high resolution aerial lidar predictions are
combined.
•
Model generalises well to aerial imagery, even though trained with satel-
lite images.



 
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