{"id":7204,"date":"2020-07-17T09:32:56","date_gmt":"2020-07-17T14:32:56","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/?p=7204"},"modified":"2026-02-18T11:29:57","modified_gmt":"2026-02-18T16:29:57","slug":"neon-tree-crowns-dataset","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2020\/07\/17\/neon-tree-crowns-dataset\/","title":{"rendered":"NEON TREE CROWNS DATASET"},"content":{"rendered":"

Abstract:<\/h2>\n

The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network\u2019s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. <\/p>\n

Links:<\/h2>\n

\"url\"<\/a><\/p>\n

Citation:<\/h2>\n
B. Weinstein, S. Marconi, A. Zare, S. Bohlman, S. Graves, A. Singh, and E. White, NEON Tree Crowns Dataset<\/em>, (Version 0.0.1): Zenodo, Apr., 2020. [Data set]. Available: http:\/\/doi.org\/10.5281\/zenodo.3765872<\/code><\/pre>\n
 <\/p>\n
@dataset{weinstein2020NEONTreeCrowns,\nauthor = {Ben Weinstein and Sergio Marconi and Alina Zare and Stephanie Bohlman and Sarah Graves and Aditya Singh and Ethan White},  \ntitle = {NEON Tree Crowns Dataset},  \nmonth = {April},  \nyear = {2020},  \nnote = {Gordon and Betty Moore Foundation: GBMF4563},  \npublisher = {Zenodo},  \nversion = {0.0.1},  \ndoi = {10.5281\/zenodo.3765872},  \nurl = {https:\/\/doi.org\/10.5281\/zenodo.3765872}\n}<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"
Abstract: The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network\u2019s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. Links: Citation: B. Weinstein, S. Marconi, A. Zare, […]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[9,13],"tags":[217,223,421,503,621,753,757],"class_list":["post-7204","post","type-post","status-publish","format-standard","hentry","category-news","category-publication","tag-dataset","tag-deep-learning","tag-lidar","tag-neon","tag-remote-sensing","tag-tree-crown","tag-trees"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/7204","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=7204"}],"version-history":[{"count":1,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/7204\/revisions"}],"predecessor-version":[{"id":15261,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/7204\/revisions\/15261"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=7204"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=7204"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=7204"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}