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Cited by in F6Publishing
For: Yu K, Hao Z, Post CJ, Mikhailova EA, Lin L, Zhao G, Tian S, Liu J. Comparison of Classical Methods and Mask R-CNN for Automatic Tree Detection and Mapping Using UAV Imagery. Remote Sensing 2022;14:295. [DOI: 10.3390/rs14020295] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Li H, Wang J, Du X, Hu Z, Yang S. KBHN: A knowledge-aware bi-hypergraph network based on visual-knowledge features fusion for teaching image annotation. Information Processing & Management 2023;60:103106. [DOI: 10.1016/j.ipm.2022.103106] [Reference Citation Analysis]
2 Sani-mohammed A, Yao W, Heurich M. Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning. ISPRS Open Journal of Photogrammetry and Remote Sensing 2022. [DOI: 10.1016/j.ophoto.2022.100024] [Reference Citation Analysis]
3 Lucena F, Breunig FM, Kux H. The Combined Use of UAV-Based RGB and DEM Images for the Detection and Delineation of Orange Tree Crowns with Mask R-CNN: An Approach of Labeling and Unified Framework. Future Internet 2022;14:275. [DOI: 10.3390/fi14100275] [Reference Citation Analysis]
4 Kainz O, Gera M, Michalko M, Jakab F. Experimental Solution for Estimating Pedestrian Locations from UAV Imagery. Applied Sciences 2022;12:9485. [DOI: 10.3390/app12199485] [Reference Citation Analysis]
5 Hickman SHM, Ball JGC, Jackson TD, Koay XJ, Hirst J, Jay W, Aubry-kientz M, Vincent G, Coomes DA. Accurate tropical forest individual tree crown delineation from RGB imagery using Mask R-CNN.. [DOI: 10.1101/2022.07.10.499480] [Reference Citation Analysis]
6 Lassalle G, Ferreira MP, La Rosa LEC, de Souza Filho CR. Deep learning-based individual tree crown delineation in mangrove forests using very-high-resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 2022;189:220-35. [DOI: 10.1016/j.isprsjprs.2022.05.002] [Reference Citation Analysis]
7 An L, Zhang J. Impact of Urbanization on Seismic Risk: A Study Based on Remote Sensing Data. Sustainability 2022;14:6132. [DOI: 10.3390/su14106132] [Reference Citation Analysis]
8 Mafanya M, Tsele P, Zengeya T, Ramoelo A. An assessment of image classifiers for generating machine-learning training samples for mapping the invasive Campuloclinium macrocephalum (Less.) DC (pompom weed) using DESIS hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing 2022;185:188-200. [DOI: 10.1016/j.isprsjprs.2022.01.015] [Reference Citation Analysis]