Tree Detection in RGB Satellite Imagery Using YOLO-Based Deep Learning Models
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Abstract
Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being. Traditional forest mapping and monitoring methods are often costly and limited in scope, necessitating the ado... | Find, read and cite all the research you need on Tech Science Press
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Research context
OpenAlex topics: Remote Sensing and LiDAR Applications · Automated Road and Building Extraction · Wood and Agarwood Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (46)
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1. Research of new technologies and innovations in the field of forestry10.33245/2310-9270-2024-187-1-268-276 ↗
2. Mapping carbon dynamics: remote sensing insights into calabria’s landscapes (Southern Italy)
3. YOLO-IAPs: a rapid detection method for invasive alien plants in the wild based on improved YOLOv910.3390/agriculture14122201 ↗
4. Modeling forest regeneration dynamics: estimating regeneration, growth, and mortality rates in lithuanian forests10.3390/f16020192 ↗
5. Use of models to analyse land-use changes, forest/soil degradation, and carbon sequestration with special reference to Himalayan region: a review and analysis10.1016/j.forpol.2005.10.003 ↗
6. Pettorelli · Satellite remote sensing and the management of natural resources · 201910.1093/oso/9780198717263.001.0001 ↗
7. The economics of forest carbon sequestration: a bibliometric analysis10.1007/s10668-023-02922-w ↗
8. Parr · Role of biodiversity in climate change mitigation (ROBIN) · 2016
9. Measuring and monitoring trees and forests using a novel IoT approach
10. Detection and thinning of street trees for calculation of morphological parameters using mobile laser scanner data
11. Carbon sequestration in forests and soils10.1146/annurev-resource-083110-115941 ↗
12. Digital transformation of the future of forestry: an exploration of key concepts in the principles behind Forest 4.010.3389/ffgc.2024.1424327 ↗
13. D’alembert convolution for enhanced spatio-temporal analysis of forest ecosystems
14. A reinforcement learning-based adaptive digital twin model for forests
15. Distributed random forest for predicting forest wildfires based on weather data
16. Robust forest sound classification using pareto-mordukhovich optimized MFCC in environmental monitoring10.1109/ACCESS.2025.3535796 ↗
17. An improved method for individual tree segmentation in complex urban scene based on using multispectral LiDAR by deep learning10.1109/JSTARS.2024.3373395 ↗
18. Classification of tree species in transmission line corridors based on YOLO v710.3390/f15010061 ↗
19. Beyond 3-D: the new spectrum of lidar applications for earth and ecological sciences10.1016/j.rse.2016.08.018 ↗
20. Testing the simultaneous use of laser scanning and aerial image data for estimation of tree crown density
21. Individual tree segmentation and tree species classification in subtropical broadleaf forests using UAV-based LiDAR, hyperspectral, and ultrahigh-resolution RGB data10.1016/j.rse.2022.113143 ↗
22. Street tree segmentation from mobile laser scanning data using deep learning-based image instance segmentation10.1016/j.ufug.2023.128200 ↗
23. Clusterformer for pine tree disease identification based on UAV remote sensing image segmentation
24. Tree segmentation of LiDAR point clouds using a graph-based algorithm
25. A new strategy for individual tree detection and segmentation from leaf-on and leaf-off UAV-LiDAR point clouds based on automatic detection of seed points10.3390/rs15061619 ↗
26. Instance segmentation of individual tree crowns with YOLOv5: a comparison of approaches using the ForInstance benchmark LiDAR dataset10.1016/j.ophoto.2023.100045 ↗
27. Individual tree crown segmentation directly from UAV-borne LiDAR data using the PointNet of deep learning10.3390/f12020131 ↗
28. Modern trends and recent applications of hyperspectral imaging: a review10.3390/technologies13050170 ↗
29. Individual tree crown detection and classification of live and dead trees using a mask region-based convolutional neural network (Mask R-CNN)10.3390/f15111900 ↗
30. Automatic detection and counting of oil palm trees using remote sensing and object-based deep learning
31. Ahlswede · Earth Syst Sci Data · 2023 · 15 · 68110.5194/essd-15-681-2023 ↗
32. Deep residual learning for image recognition
33. Accuracy of a lidar-based individual tree detection and attribute measurement algorithm developed to inform forest products supply chain and resource management10.3390/f13010003 ↗
34. Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN)10.1016/j.isprsjprs.2021.06.003 ↗
35. Automatic tree counting from satellite imagery using YOLO V5, SSD and UNET models: a case study of a campus in Islamabad, Pakistan
36. Individual tree-crown detection and species identification in heterogeneous forests using aerial RGB imagery and deep learning10.3390/rs15051463 ↗
37. test. forest trees Dataset. Roboflow; 2023 [Dataset]. [cited 2025 Feb 25]. Available from: https://universe.roboflow.com/test-dqo8y/forest-trees.
38. You only look once: Unified, real-time object detection
39. YOLO-Tea: a tea disease detection model improved by YOLOv510.3390/f14020415 ↗
40. Geetha AS. What is yolov6? a deep insight into the object detection model. arXiv:2412.13006. 2024.
41. The semantic segmentation of standing tree images based on the Yolo V7 deep learning algorithm10.3390/electronics12040929 ↗
42. Improved real-time object detection method based on YOLOv8: a refined approach10.1007/s11554-024-01585-8 ↗
43. Yaseen M. What is yolov9: an in-depth exploration of the internal features of the next-generation object detector. arXiv:2409.07813. 2024.
44. Wang · Advances in neural information processing systems · 2024 · 37 · 107984
45. Khanam R, Hussain M. Yolov11: an overview of the key architectural enhancements. arXiv:2410.17725. 2024.
46. Tian Y, Ye Q, Doermann D. YOLOv12: attention-centric real-time object detectors. arXiv:2502.12524. 2025.