Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery | Paper Citation Record

Observation 6ff0a2c4-5c41-4e95-8b64-1b7109e2566c · outbound

This paper cites Managing forests for climate change mitigation.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Managing forests for climate change mitigation

Reference 1

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Observation a4a71a4b-0e36-40aa-85df-86f9aa1d0440 · outbound

This paper cites Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits.Global Change Biology, 27(7):1328–1348, 2021.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits.Global Change Biology, 27(7):1328–1348, 2021

Reference 2

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This paper cites Formulating allometric equations for estimating biomass and carbon stock in small diameter trees.Forest Ecology and Management, 261(11):1945–1949, 2011.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Formulating allometric equations for estimating biomass and carbon stock in small diameter trees.Forest Ecology and Management, 261(11):1945–1949, 2011

Reference 3

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Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 4

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Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 5

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Observation c117bf13-97b7-4789-976d-63b2ec001d9d · outbound

This paper cites Tallo: A global tree allometry and crown architecture database.Global change biology, 28(17):5254–5268, 2022.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Tallo: A global tree allometry and crown architecture database.Global change biology, 28(17):5254–5268, 2022

Reference 6

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This paper cites Afforestation, Reforestation, and Revegetation v1.0, 2023.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Afforestation, Reforestation, and Revegetation v1.0, 2023

Reference 7

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Observation 1118ce07-ab09-496e-bc8e-0b26b2a6132a · outbound

This paper cites Mask R-CNN.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Mask R-CNN

Reference 8

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Observation bb271609-10ed-4d64-a560-6e22add2b8a9 · outbound

This paper cites Focal Loss for Dense Object Detection.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Focal Loss for Dense Object Detection

Reference 9

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This paper cites Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks

Reference 10

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This paper cites Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R-CNN.Remote Sensing in Ecology and Conservation, 9(5):641–655, 2023.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R-CNN.Remote Sensing in Ecology and Conservation, 9(5):641–655, 2023

Reference 11

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Observation c495f3b4-e292-4de9-8cd3-453cb0f0def4 · outbound

This paper cites ReforesTree: A dataset for estimating tropical forest carbon stock with deep learning and aerial imagery.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery ReforesTree: A dataset for estimating tropical forest carbon stock with deep learning and aerial imagery

Reference 12

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Observation fa373d9d-7a8c-4e91-9860-7f7b20d46b10 · outbound

This paper cites Sub-continental- scale carbon stocks of individual trees in African drylands.Nature, 615(7950):80–86, 2023.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Sub-continental- scale carbon stocks of individual trees in African drylands.Nature, 615(7950):80–86, 2023

Reference 13

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This paper cites Segment Anything.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Segment Anything

Reference 14

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This paper cites SAM on Medical Images: A Comprehensive Study on Three Prompt Modes.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery SAM on Medical Images: A Comprehensive Study on Three Prompt Modes

Reference 15

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Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 16

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Observation 0f58b02d-ecee-4c9c-80ca-e85e0ee6cadb · outbound

This paper cites SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 17

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Observation e93590b2-8ad9-4962-a414-6faca2791303 · outbound

This paper cites The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot.International Journal of Applied Earth Observation and Geoinformation, 124:103540, 2023.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery The Segment Anything Model (SAM) for remote sensing applications: From zero to one shot.International Journal of Applied Earth Observation and Geoinformation, 124:103540, 2023

Reference 18

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Observation 47e85208-21b6-4c4a-8a03-070681fa6566 · outbound

This paper cites Segmate Python segmentation toolkit, 2023.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Segmate Python segmentation toolkit, 2023

Reference 19

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This paper cites RSPrompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model.IEEE Transactions on Geoscience and Remote Sensing, 2024.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery RSPrompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model.IEEE Transactions on Geoscience and Remote Sensing, 2024

Reference 20

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Observation 12851b22-820f-4f5f-8379-ddd5394cc355 · outbound

This paper cites Lefebvre and E.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Lefebvre and E

Reference 21

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Observation 2d2a50f2-6ee7-44a7-8a6b-d16e5eb9235f · outbound

This paper cites Influence of temperate forest autumn leaf phenology on segmentation of tree species from UA V imagery using deep learning.Remote Sensing of Environment, 311:114283, 2024.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Influence of temperate forest autumn leaf phenology on segmentation of tree species from UA V imagery using deep learning.Remote Sensing of Environment, 311:114283, 2024

Reference 22

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Observation 58e7d795-8aa0-42eb-964c-8317f5b722c2 · outbound

This paper cites Barro Colorado Island 50-ha plot crown maps: manually segmented and instance segmented.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Barro Colorado Island 50-ha plot crown maps: manually segmented and instance segmented

Reference 23

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Observation 7b06e698-a638-4591-9f35-be3c9aa09b0b · outbound

This paper cites Deep semantic segmentation of trees using multispectral images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:7589–7604, 2022.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Deep semantic segmentation of trees using multispectral images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15:7589–7604, 2022

Reference 24

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Observation 8986e231-0242-4769-b4b4-4be460d5ce08 · outbound

This paper cites An unexpectedly large count of trees in the West African Sahara and Sahel.Nature, 587(7832): 78–82, 2020.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery An unexpectedly large count of trees in the West African Sahara and Sahel.Nature, 587(7832): 78–82, 2020

Reference 25

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Observation 56b7bb7c-535e-4578-b3fe-b37ed98e414b · outbound

This paper cites Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images.Remote Sensing, 9(1):22, December 2016.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images.Remote Sensing, 9(1):22, December 2016

Reference 26

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Observation 4750c2a3-9330-40a3-a4fa-23ed0e419b00 · outbound

This paper cites Review on Convolutional Neural Networks (CNN) in vegetation remote sensing.ISPRS Journal of Photogrammetry and Remote Sensing, 173:24–49, March 2021.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Review on Convolutional Neural Networks (CNN) in vegetation remote sensing.ISPRS Journal of Photogrammetry and Remote Sensing, 173:24–49, March 2021

Reference 27

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Observation 57a173a4-327c-4940-91bc-3e1a74a1af3d · outbound

This paper cites Explainable identification and mapping of trees using UA V RGB image and deep learning.Scientific Reports, 11(1):903, January 2021.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Explainable identification and mapping of trees using UA V RGB image and deep learning.Scientific Reports, 11(1):903, January 2021

Reference 28

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This paper cites Comparison of classical methods and Mask R-CNN for automatic tree detection and mapping using uav imagery.Remote Sensing, 14(2):295, 2022.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Comparison of classical methods and Mask R-CNN for automatic tree detection and mapping using uav imagery.Remote Sensing, 14(2):295, 2022

Reference 29

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Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 30

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Observation 35904da0-9f2d-40b2-92a2-746770c00a47 · outbound

This paper cites Transferability of a Mask R-CNN model for the delineation and classification of two species of regenerating tree crowns to untrained sites.Science of Remote Sensing, 9:100109, 2024.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Transferability of a Mask R-CNN model for the delineation and classification of two species of regenerating tree crowns to untrained sites.Science of Remote Sensing, 9:100109, 2024

Reference 31

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Observation 4dd95db2-300d-4c9b-b6b8-3d0df5caa2a7 · outbound

This paper cites Tree in- stance segmentation with temporal contour graph.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Tree in- stance segmentation with temporal contour graph

Reference 32

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This paper cites Individual tree crown delineation in high-resolution remote sensing images based on U-Net.Neural Computing and Applications, 34(24):22197–22207, 2022.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Individual tree crown delineation in high-resolution remote sensing images based on U-Net.Neural Computing and Applications, 34(24):22197–22207, 2022

Reference 33

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This paper cites U-Net: Convolutional networks for biomedical image segmentation.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery U-Net: Convolutional networks for biomedical image segmentation

Reference 34

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Observation 4a63c6a7-8084-4ad3-9a6f-7c79824b90b2 · outbound

This paper cites Multi-species individual tree segmentation and identification based on improved Mask R-CNN and UA V imagery in mixed forests.Remote Sensing, 14(4):874, 2022.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Multi-species individual tree segmentation and identification based on improved Mask R-CNN and UA V imagery in mixed forests.Remote Sensing, 14(4):874, 2022

Reference 35

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Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 36

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This paper cites Individual Tree AGB Estimation of Malania oleifera Based on UA V-RGB Imagery and Mask R-CNN.Forests, 14(7):1493, July 2023.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Individual Tree AGB Estimation of Malania oleifera Based on UA V-RGB Imagery and Mask R-CNN.Forests, 14(7):1493, July 2023

Reference 37

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Observation 3fa2454b-3757-43bf-931a-feeeecff8e04 · outbound

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Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 38

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Observation b30bab73-38eb-4e27-a4de-306c0818bbc9 · outbound

This paper cites Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis.Machine Vision and Applications, 11 (2):64–73, October 1998.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis.Machine Vision and Applications, 11 (2):64–73, October 1998

Reference 39

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This paper cites TIDA: an algorithm for the delineation of tree crowns in high spatial resolu- tion remotely sensed imagery.Computers & Geosciences, 28(1):33–44, February 2002.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery TIDA: an algorithm for the delineation of tree crowns in high spatial resolu- tion remotely sensed imagery.Computers & Geosciences, 28(1):33–44, February 2002

Reference 40

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Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 41

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This paper cites Quackenbush.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Quackenbush

Reference 42

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This paper cites V o, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, and Camille Couprie.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery V o, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, and Camille Couprie

Reference 43

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This paper cites Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebas- tian Pokutta, Martin Brandt, and Fabian Gieseke.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebas- tian Pokutta, Martin Brandt, and Fabian Gieseke

Reference 44

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Observation 246867cc-9b48-473c-97c4-7b7240fbe267 · outbound

This paper cites Wagner, Sophia Roberts, Alison L.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Wagner, Sophia Roberts, Alison L

Reference 45

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This paper cites High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model

Reference 46

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Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 47

Resolution: verified exact; doi, observed 2026-08-07T10:32:29.276481Z

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Observation c594e752-e5a4-4c95-8e84-d7f3280ab004 · outbound

This paper cites Landau, Luke J.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Landau, Luke J

Reference 48

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This paper cites Lidar-based Norwegian tree species detection using deep learning.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Lidar-based Norwegian tree species detection using deep learning

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:32:29.462305Z

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Observation 3ae5f4b3-1a2e-4c61-89d8-04e7552c4b22 · outbound

This paper cites Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learning.Remote Sensing of Environment, 305:114078, May 2024.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learning.Remote Sensing of Environment, 305:114078, May 2024

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:29.189277Z

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Observation 11a3ec38-128e-489f-a151-71f440be7e84 · outbound

This paper cites Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery.Photogrammetric Engineering & Remote Sensing, 70(3):351–357, 2004.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery.Photogrammetric Engineering & Remote Sensing, 70(3):351–357, 2004

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:29.851850Z

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Observation 3c70e029-6430-4f11-9121-3b59431fee0c · outbound

This paper cites an unresolved cited work.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:32:29.841979Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a776c9d0-8b78-4c88-9c1f-ff2c8ec1e461 · outbound

This paper cites Individual tree crown segmentation of a larch plantation using airborne laser scanning data based on region growing and canopy morphology features.Remote Sensing, 12 (7):1078, 2020.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Individual tree crown segmentation of a larch plantation using airborne laser scanning data based on region growing and canopy morphology features.Remote Sensing, 12 (7):1078, 2020

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:29.832749Z

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Observation 20dd175c-5b94-43b4-bcb9-f3a43bb87498 · outbound

This paper cites Individual tree crown delineation from high-resolution UA V images in broadleaf forest.Ecological Informatics, 61:101207, 2021.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Individual tree crown delineation from high-resolution UA V images in broadleaf forest.Ecological Informatics, 61:101207, 2021

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:29.822330Z

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Observation 4e1f9b79-b085-4292-9297-a4b4b59a9f61 · outbound

This paper cites an unresolved cited work.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:32:29.809534Z

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Observation 92e76bc2-42a1-4c31-9d12-bb0c37a4fe29 · outbound

This paper cites Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:29.799599Z

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Observation ae43c6f3-abe6-4239-9b10-37deff017901 · outbound

This paper cites Santoro, Paulo G.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Santoro, Paulo G

Reference 57

Resolution: verified exact; doi, observed 2026-08-07T10:32:29.260567Z

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Observation b210c38d-17f2-46b5-b8ca-4254476ded75 · outbound

This paper cites an unresolved cited work.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Unresolved cited work

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:29.211458Z

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Observation d2983457-2205-4f90-8e2c-68b4bb7fe59a · outbound

This paper cites Adapting Segment Anything Model to aerial land cover classification with low-rank adaptation.IEEE Geoscience and Remote Sensing Letters, 21:1–5, 2024.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Adapting Segment Anything Model to aerial land cover classification with low-rank adaptation.IEEE Geoscience and Remote Sensing Letters, 21:1–5, 2024

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:29.788675Z

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Observation d0ad6592-26ed-41c7-a2f7-d9e54b42ddf9 · outbound

This paper cites UV-SAM: Adapting Segment Anything Model for urban village identification.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery UV-SAM: Adapting Segment Anything Model for urban village identification

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:29.778329Z

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Observation a9442320-b621-452c-8247-a7e98f28470c · outbound

This paper cites Leveraging Prompt-Based Segmentation Models and Large Dataset to Improve Detection of Trees.Proceedings of the Conference on Robots and Vision, may 28 2024.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Leveraging Prompt-Based Segmentation Models and Large Dataset to Improve Detection of Trees.Proceedings of the Conference on Robots and Vision, may 28 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:32:29.768801Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 22280ee3-1b1d-4af4-ad21-61a0089943fb · outbound

This paper cites Tree semantic segmentation from aerial image time series.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery Tree semantic segmentation from aerial image time series

Reference 63

Resolution: malformed identifier; local_arxiv, observed 2026-08-07T10:32:29.370800Z

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Observation ddd7a019-e5af-4a53-bcf0-930169a470f5 · outbound

This paper cites URL https://www.nature.com/articles/ s41598-020-79653-9.

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery URL https://www.nature.com/articles/ s41598-020-79653-9

Reference 2322

Resolution: unresolved; no resolver link, observed 2026-08-07T10:32:29.123951Z

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