Home ›
Entities
› academia
› Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar
Research article (Remote Sensing of Environment, 2023) · cited 278× · AI/ML
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar
Summary
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar is a scholarly article[1].
Key Facts
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Retrieved May 24, 2026, from https://4ort.xyz/entity/very-high-resolution-canopy-height-maps-from-rgb-imagery-using-self-supervised-vision-transformer-and-convolutional-deco
MLA“Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/very-high-resolution-canopy-height-maps-from-rgb-imagery-using-self-supervised-vision-transformer-and-convolutional-deco.
BibTeX@misc{4ortxyz_very-high-resolution-canopy-height-maps-from-rgb-imagery-using-self-supervised-vision-transformer-and-convolutional-deco_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar}}, year = {2026}, url = {https://4ort.xyz/entity/very-high-resolution-canopy-height-maps-from-rgb-imagery-using-self-supervised-vision-transformer-and-convolutional-deco}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar — https://4ort.xyz/entity/very-high-resolution-canopy-height-maps-from-rgb-imagery-using-self-supervised-vision-transformer-and-convolutional-deco (retrieved 2026-05-24)
