Segmentation of arbitrary features in very high resolution remote sensing imagery | Paper Citation Record

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Reference 1

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

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This paper cites ‘Optical satellite images services for precision agricultural use: a review’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Optical satellite images services for precision agricultural use: a review’

Reference 3

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This paper cites McKellar, Nicholas G.

Segmentation of arbitrary features in very high resolution remote sensing imagery McKellar, Nicholas G

Reference 4

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This paper cites ‘Drones as a tool to monitor human impacts and vegetation changes in parks and protected areas’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Drones as a tool to monitor human impacts and vegetation changes in parks and protected areas’

Reference 5

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This paper cites ‘Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques’

Reference 6

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This paper cites ‘Individual tree detection and species classification of Amazo- nian palms using UAV images and deep learning’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Individual tree detection and species classification of Amazo- nian palms using UAV images and deep learning’

Reference 7

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This paper cites ‘Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery’

Reference 8

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This paper cites ‘Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)’

Reference 9

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This paper cites ‘Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks’

Reference 10

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This paper cites ‘Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops’

Reference 11

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This paper cites ‘Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art’

Reference 12

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This paper cites Ball, Derek T.

Segmentation of arbitrary features in very high resolution remote sensing imagery Ball, Derek T

Reference 13

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This paper cites ‘Deep learning in remote sensing applications: A meta-analysis and review’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Deep learning in remote sensing applications: A meta-analysis and review’

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Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Machine Learning and Deep Learning in Remote Sensing and Urban Application: A Systematic Review and Meta-Analysis’

Reference 15

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This paper cites ‘Land’s complex role in climate change’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Land’s complex role in climate change’

Reference 16

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This paper cites Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II.

Segmentation of arbitrary features in very high resolution remote sensing imagery Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II

Reference 17

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This paper cites ‘Climate Change Impacts on Plant Pathogens and Plant Diseases’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Climate Change Impacts on Plant Pathogens and Plant Diseases’

Reference 18

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

Reference 19

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This paper cites Tsouros, Stamatia Bibi and Panagiotis G.

Segmentation of arbitrary features in very high resolution remote sensing imagery Tsouros, Stamatia Bibi and Panagiotis G

Reference 20

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This paper cites ‘A Study on the Detection of Cattle in UAV Images Using Deep Learning’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘A Study on the Detection of Cattle in UAV Images Using Deep Learning’

Reference 21

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This paper cites Heil et al.

Segmentation of arbitrary features in very high resolution remote sensing imagery Heil et al

Reference 22

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This paper cites Machicao et al.

Segmentation of arbitrary features in very high resolution remote sensing imagery Machicao et al

Reference 23

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This paper cites LULC classification by semantic segmentation of satellite images using FastFCN.

Segmentation of arbitrary features in very high resolution remote sensing imagery LULC classification by semantic segmentation of satellite images using FastFCN

Reference 24

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

Reference 25

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This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Segmentation of arbitrary features in very high resolution remote sensing imagery PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 26

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This paper cites MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Segmentation of arbitrary features in very high resolution remote sensing imagery MMSegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark

Reference 27

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Segmentation of arbitrary features in very high resolution remote sensing imagery Segment Anything

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This paper cites ‘Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution’

Reference 29

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Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Automated aerial animal detection when spatial resolution conditions are varied’

Reference 30

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This paper cites ‘Analysing the Interactions Between Training Dataset Size, Label Noise and Model Performance in Remote Sensing Data’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Analysing the Interactions Between Training Dataset Size, Label Noise and Model Performance in Remote Sensing Data’

Reference 31

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Segmentation of arbitrary features in very high resolution remote sensing imagery Calhoun et al

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Segmentation of arbitrary features in very high resolution remote sensing imagery QGIS Association

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Segmentation of arbitrary features in very high resolution remote sensing imagery Version 2.2.0

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This paper cites The Sphinx documentation generator.

Segmentation of arbitrary features in very high resolution remote sensing imagery The Sphinx documentation generator

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

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This paper cites 2023.doi: https://doi.org/https://doi.org/10.5281/ZENODO.

Segmentation of arbitrary features in very high resolution remote sensing imagery 2023.doi: https://doi.org/https://doi.org/10.5281/ZENODO

Reference 37

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Segmentation of arbitrary features in very high resolution remote sensing imagery Anaconda Software Distribution

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

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This paper cites MultiEarth 2023 Deforestation Challenge -- Team FOREVER.

Segmentation of arbitrary features in very high resolution remote sensing imagery MultiEarth 2023 Deforestation Challenge -- Team FOREVER

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This paper cites K-Net: Towards Unified Image Segmentation.

Segmentation of arbitrary features in very high resolution remote sensing imagery K-Net: Towards Unified Image Segmentation

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

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This paper cites ‘Albumentations: Fast and Flexible Image Augmentations’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Albumentations: Fast and Flexible Image Augmentations’

Reference 43

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

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Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Data augmentation for improving deep learning in image classification problem’

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Segmentation of arbitrary features in very high resolution remote sensing imagery ‘A survey on image data augmentation for deep learning’

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Segmentation of arbitrary features in very high resolution remote sensing imagery Karasiak et al

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Segmentation of arbitrary features in very high resolution remote sensing imagery Masked-attention Mask Transformer for Universal Image Segmentation

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Segmentation of arbitrary features in very high resolution remote sensing imagery The Cityscapes Dataset for Semantic Urban Scene Understanding

Reference 49

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Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Scene Parsing through ADE20K Dataset’

Reference 50

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This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Segmentation of arbitrary features in very high resolution remote sensing imagery Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 51

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

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This paper cites ‘An automated, high-performance approach for detecting and charac- terizing broccoli based on UAV remote-sensing and transformers: A case study from Haining, China’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘An automated, high-performance approach for detecting and charac- terizing broccoli based on UAV remote-sensing and transformers: A case study from Haining, China’

Reference 53

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This paper cites ‘Urban Trees Mapping Using Multi-Scale Rgb Image and Deep Learning Vision Transformer-Based’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Urban Trees Mapping Using Multi-Scale Rgb Image and Deep Learning Vision Transformer-Based’

Reference 54

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This paper cites Gibril et al.

Segmentation of arbitrary features in very high resolution remote sensing imagery Gibril et al

Reference 55

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Observation 999dfd10-0beb-4d75-a832-5a73882a9b6a · outbound

This paper cites ‘AMDNet: A Modern UAV RGB Remote-Sensing Tree Species Image Segmentation Model Based on Dual-Attention Residual and Structure Re-Parameterization’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘AMDNet: A Modern UAV RGB Remote-Sensing Tree Species Image Segmentation Model Based on Dual-Attention Residual and Structure Re-Parameterization’

Reference 56

Resolution: verified exact; doi, observed 2026-08-11T10:55:15.758838Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1dc43396-4aa4-4adc-857c-2fde894e1257 · outbound

This paper cites ‘Building Extraction With Vision Transformer’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Building Extraction With Vision Transformer’

Reference 57

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Observation 9a81c902-68ab-42b6-9be3-00b3d7d296bd · outbound

This paper cites ‘DCS-TransUperNet: Road Segmentation Network Based on CSwin Trans- former with Dual Resolution’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘DCS-TransUperNet: Road Segmentation Network Based on CSwin Trans- former with Dual Resolution’

Reference 58

Resolution: verified exact; doi, observed 2026-08-11T10:55:15.740630Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8cd92f01-55d4-48d5-9eec-b0b23f54c34c · outbound

This paper cites In:Mathematics 10.24 (2022).

Segmentation of arbitrary features in very high resolution remote sensing imagery In:Mathematics 10.24 (2022)

Reference 59

Resolution: verified exact; doi, observed 2026-08-11T10:55:15.719418Z

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Observation f7facf3d-7148-4374-9c81-0bddd27f32c0 · outbound

This paper cites ‘Adaptive enhanced swin transformer with U-net for remote sensing image segmentation’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Adaptive enhanced swin transformer with U-net for remote sensing image segmentation’

Reference 60

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 765730cc-8ded-4cca-a739-e936616dee32 · outbound

This paper cites ‘Leaf Area Index Estimation of Pergola- Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Leaf Area Index Estimation of Pergola- Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods’

Reference 61

Resolution: verified exact; doi, observed 2026-08-11T10:55:15.699013Z

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Observation f489a517-5d8c-4f5b-a03d-bb3447236cde · outbound

This paper cites ‘Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification’

Reference 62

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Observation 9bbf538a-f261-403e-8ae9-00e3c5caeef7 · outbound

This paper cites ‘On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks’.

Segmentation of arbitrary features in very high resolution remote sensing imagery ‘On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks’

Reference 63

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e99a6f63-8109-44e1-b2b8-cbc0878ca30d · outbound

This paper cites an unresolved cited work.

Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

Reference 64

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Observation 75fde9ec-86ef-4ff4-bd90-3312a6e8c242 · outbound

This paper cites Attention Is All You Need.

Segmentation of arbitrary features in very high resolution remote sensing imagery Attention Is All You Need

Reference 65

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Source-reported events for the cited work

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Observation 96522caa-1e3c-49d7-8a26-448e23e03ced · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Segmentation of arbitrary features in very high resolution remote sensing imagery An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T10:55:15.524726Z

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Observation dc20fe6b-8430-4e6b-9c1d-52fe31d7be56 · outbound

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:55:18.067655Z

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Observation 70de1809-56d2-47ec-ac04-1f937b875bdc · outbound

This paper cites Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

Segmentation of arbitrary features in very high resolution remote sensing imagery Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T10:55:15.539627Z

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Observation 2c2c487b-f378-4854-be42-c9012d8ce8d6 · outbound

This paper cites Per-Pixel Classification is Not All You Need for Semantic Segmentation.

Segmentation of arbitrary features in very high resolution remote sensing imagery Per-Pixel Classification is Not All You Need for Semantic Segmentation

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T10:55:15.545151Z

Source-reported events for the cited work

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Observation 89a746b9-90b7-4f5a-874e-3cd3415ebfbc · outbound

This paper cites Vitis vinifera — European grape.

Segmentation of arbitrary features in very high resolution remote sensing imagery Vitis vinifera — European grape

Reference 70

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Observation 24d59e52-b6ca-42cb-be00-9752e4fc0b88 · outbound

This paper cites url: https://plants.ces.

Segmentation of arbitrary features in very high resolution remote sensing imagery url: https://plants.ces

Reference 71

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eb92a3f6-5e9c-4703-8a30-087adb227815 · outbound

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

Reference 72

Resolution: verified exact; doi, observed 2026-08-11T10:55:15.652907Z

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Observation 90336a5b-6fb6-44d1-bf41-c95c21e4473a · outbound

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

Reference 73

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Observation 584978d2-0850-4de8-adb2-35958a709ea6 · outbound

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Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:55:17.937676Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d0339a9-d172-4357-97ff-b1b0a108647d · outbound

This paper cites Dairy cow comfort: tie-stall dimensions.

Segmentation of arbitrary features in very high resolution remote sensing imagery Dairy cow comfort: tie-stall dimensions

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:55:17.906846Z

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Observation b65ada46-7466-48cf-a35e-a50e8d7783a0 · outbound

This paper cites confidence scores.

Segmentation of arbitrary features in very high resolution remote sensing imagery confidence scores

Reference 76

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eb386278-d255-4141-82cc-38a474da826b · outbound

This paper cites 1280 Circular.

Segmentation of arbitrary features in very high resolution remote sensing imagery 1280 Circular

Reference 80

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T10:55:17.829069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 312064bb-f197-45ab-b70a-df84a7232f2e · outbound

This paper cites an unresolved cited work.

Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

Reference 782

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Observation 6b0e15fd-c3b5-498f-977d-71450126c758 · outbound

This paper cites an unresolved cited work.

Segmentation of arbitrary features in very high resolution remote sensing imagery Unresolved cited work

Reference 2018

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d1f235c3-82ad-433f-b1ac-aaa1c23803fa · outbound

This paper cites GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models.

Segmentation of arbitrary features in very high resolution remote sensing imagery GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

Reference 2022

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