{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T13:55:49Z","timestamp":1785333349141,"version":"3.55.0"},"reference-count":84,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T00:00:00Z","timestamp":1605744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Measuring and monitoring the height of vegetation provides important insights into forest age and habitat quality. These are essential for the accuracy of applications that are highly reliant on up-to-date and accurate vegetation data. Current vegetation sensing practices involve ground survey, photogrammetry, synthetic aperture radar (SAR), and airborne light detection and ranging sensors (LiDAR). While these methods provide high resolution and accuracy, their hardware and collection effort prohibits highly recurrent and widespread collection. In response to the limitations of current methods, we designed Y-NET, a novel deep learning model to generate high resolution models of vegetation from highly recurrent multispectral aerial imagery and elevation data. Y-NET\u2019s architecture uses convolutional layers to learn correlations between different input features and vegetation height, generating an accurate vegetation surface model (VSM) at 1\u00d71 m resolution. We evaluated Y-NET on 235 km2 of the East San Francisco Bay Area and find that Y-NET achieves low error from LiDAR when tested on new locations. Y-NET also achieves an R2 of 0.83 and can effectively model complex vegetation through side-by-side visual comparisons. Furthermore, we show that Y-NET is able to identify instances of vegetation growth and mitigation by comparing aerial imagery and LiDAR collected at different times.<\/jats:p>","DOI":"10.3390\/rs12223797","type":"journal-article","created":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T10:46:26Z","timestamp":1605782786000},"page":"3797","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Beyond Measurement: Extracting Vegetation Height from High Resolution Imagery with Deep Learning"],"prefix":"10.3390","volume":"12","author":[{"given":"David","family":"Radke","sequence":"first","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Radke","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Universit\u00e4t des Saarlandes, 66123 Saarbr\u00fccken, Germany"},{"name":"Max-Planck-Institut f\u00fcr Informatik, 66123 Saarbr\u00fccken, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Radke","sequence":"additional","affiliation":[{"name":"Landscape Architecture and Environmental Planning, City and Regional Planning, University of California, Berkeley, CA 94720-2000, USA"},{"name":"Center for Catastrophic Risk Management, University of California, Berkeley, CA 94720-1922, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Franklin, S.E. 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