Stone Pine (Pinus Pinea L.) Detection from High-Resolution UAV Imagery Using Deep Learning Model

Yildirim, Esra; Nazar, Mertcan; Sefercik, Umut Güneş; Kavzoğlu, Taşkın

doi:10.1109/igarss46834.2022.9883964

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…While YOLO is traditionally an object detection framework, YOLOv7 has been modified for instance segmentation use cases [17]. This architecture has been used to develop segmentation models for self-driving cars [18] as well as some medical image segmentation applications [19,20].…”

Section: Overview Of Ultrasound Imaging Artificial Intelligent Segmen...mentioning

confidence: 99%

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images

Holland,

Hernandez Torres,

Snider

2024

Bioengineering

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Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored. Here, we detail how AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bodies embedded in tissues at variable distances from critical neurovascular features. Ultrasound images previously collected in a tissue phantom with or without neurovascular features were labeled with ground truth masks. These image sets were used to train two different segmentation AI frameworks: YOLOv7 and U-Net segmentation models. Overall, both approaches were successful in identifying shrapnel in the image set, with U-Net outperforming YOLOv7 for single-class segmentation. Both segmentation models were also evaluated with a more complex image set containing shrapnel, artery, vein, and nerve features. YOLOv7 obtained higher precision scores across multiple classes whereas U-Net achieved higher recall scores. Using each AI model, a triage distance metric was adapted to measure the proximity of shrapnel to the nearest neurovascular feature, with U-Net more closely mirroring the triage distances measured from ground truth labels. Overall, the segmentation AI models were successful in detecting shrapnel in ultrasound images and could allow for improved injury triage in emergency medicine scenarios.

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Section: Overview Of Ultrasound Imaging Artificial Intelligent Segmen...mentioning

confidence: 99%

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images

Holland,

Hernandez Torres,

Snider

2024

Bioengineering

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“…It is known that studies on commercially important species have increased considerably in recent years (Ticktin, 2004;Bonari et al, 2020;Karpukhin & Yussef, 2021;Yıldırım et al, 2022;Özdarici-Ok et al, 2022;Simões, et al, 2024). Stone pine, which is distributed in Mediterranean forest ecosystems, is an important species frequently preferred for ecological and economic purposes (Kılcı et al, 2000;Bravo-Oviedo & Montero, 2005;Batur, 2015;Güleç, 2015).…”

Section: Introductionmentioning

confidence: 99%

Environmental and socio-economic impacts of the changes in distribution areas of Pinus pinea L. (stone pine) due to climate change in Türkiye

Karayol,

Akyol

2024

Preprint

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In this study, present and future distributions of stone pine due to climate changes were modeled with MaxEnt. CNRM ESM2-1 climate model and bioclimatic variables obtained from the WorldClim database were used as climate models. As climate scenarios, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climate change scenarios and 2041–2060 and 2081–2100 periods were used. Pearson Correlation analysis was performed to prevent high correlation in bioclimatic variables and the multicollinearity problem was eliminated by reducing 19 bioclimatic variables to 9 variables. The contribution of bioclimatic variables to the model was determined by the Jackknife test. To determine the spatial and locational differences between the present and future potential distributions estimated for the species, an analysis of change was conducted. According to the findings of the study, our model has a very high predictive power and the Jackknife test results, the bioclimatic variables BIO19, BIO6, and BIO4 contribute the most to the model. Our prediction model predicts that the distribution area of stone pine will decrease, shifting northward and towards higher altitudes. We believe that this will lead to increased risk of forest fires, loss of ecosystem services, and reduced income from stone pine. For these reasons, benefit from stone pine need to take into account the effects of climate change in their land use planning and give importance to climate change adaptation efforts. These maps, created with current and future predictions of potential habitat distribution, can be use in afforestation, ecological restoration, rural development, conservation, and all kinds of land use studies.

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Stone Pine (Pinus Pinea L.) Detection from High-Resolution UAV Imagery Using Deep Learning Model

Cited by 2 publications

References 9 publications

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images

Environmental and socio-economic impacts of the changes in distribution areas of Pinus pinea L. (stone pine) due to climate change in Türkiye

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