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Decoding Nature's Patterns: An Innovative Approach to Tree Detection Using Deep Learning and High-Resolution Aerial Imagery

Yıl 2023, Cilt: 5 Sayı: 1, 52 - 59, 30.06.2023

Halil İbrahim Şenol Abdurahman Yasin Yiğit

https://doi.org/10.56130/tucbis.1307926

Öz

This study investigates the application of deep learning algorithms and high-resolution aerial imagery for individual tree detection in urban areas, using a neighborhood in Mersin, Turkey, as a case study. Employing the DeepForest Python package, we utilize high-resolution (7cm) aerial imagery to detect and map the city's tree population accurately. The results showcase an impressive accuracy rate of 80.87%, demonstrating the potential of deep learning in urban forestry applications and contributing to effective urban planning. The information generated from this study is crucial for conserving urban green spaces, enhancing resilience to climate change, and supporting urban biodiversity. While this research is focused on Mersin, the methods employed are globally adaptable, laying a foundation for further refinement and potential identification of different tree species in future work. This investigation highlights the transformative role of advanced technology in fostering sustainable urban environments.

Anahtar Kelimeler

GIS Spatial Analysis Aerial Imagery Deep Learning Photogrammetry

Kaynakça

  • Campbell N D, Vogiatzis G, Hernández C & Cipolla R (2008). Using multiple hypotheses to improve depth-maps for multi-view stereo. In Computer Vision–ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, 766-779.
  • Cruzan M B, Weinstein B G, Grasty M R, Kohrn B F, Hendrickson E C, Arredond, T M & Thompson P G (2016). Small unmanned aerial vehicles (micro‐UAVs, drones) in plant ecology. Applications in plant sciences, 4(9), 1600041. https://doi.org/10.3732/apps.1600041
  • Díaz S, Pascual U, Stenseke M, Martín-López B, Watson R T, Molnár Z, Hill R, Chan K M A, Bate I A, Brauman K A, Polasky S Church A, Lonsdale M, Larigauderie A, Leadley P W, Oudenhoven A P E V, Plaat F V D, Schröter M, Lavorel S, Thomas-Aumeeruddy Y, Bukvareva E, Davies K, Demissew S, Erpul G, Failler P, Guerra C A, Hewitt C H, Keune H, Lindley S & Shirayama Y (2018). Assessing nature's contributions to people. Science, 359(6373), 270-272. https://doi.org/10.1126/science.aap8826
  • Díaz-Varela R A, De la Rosa R, León L & Zarco-Tejad, P J (2015). High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: application in breeding trials. Remote Sensing, 7(4), 4213-4232. https://doi.org/10.3390/rs70404213
  • Duncanson L, Rourk, O & Dubayah R (2015). Small sample sizes yield biased allometric equations in temperate forests. Scientific reports, 5(1), 17153. https://doi.org/10.1038/srep17153
  • Ebrahimikia M & Hosseininaveh A (2022). True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points. The Photogrammetric Record, 37(178), 161-184. https://doi.org/10.1111/phor.12409
  • Escobedo F J, Kroeger T & Wagner J E (2011). Urban forests and pollution mitigation: Analyzing ecosystem services and disservices. Environmental pollution, 159(8-9), 2078-2087. https://doi.org/10.1016/j.envpol.2011.01.010
  • Franklin S E (2001). Remote sensing for sustainable forest management. CRC press, ISBN: 978-1-4200-3285-7. Goodfellow I, Bengio Y & Courville A (2016). Deep learning. MIT press, ISBN: 978-026-2035-61-3.
  • Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J & Chen T (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377. https://doi.org/10.1016/j.patcog.2017.10.0
  • Guerra-Hernández J, Cosenza D N, Rodriguez L C E, Silva, M, Tomé M, Díaz-Varela R A & González-Ferreiro E (2018). Comparison of ALS-and UAV (SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations. International Journal of Remote Sensing, 39(15-16), 5211-5235. https://doi.org/10.1080/01431161.2018.1486519
  • Hamal S N G & Ulvi A (2022). 3B UAV Photogrammetry and GIS Integration for 3D City Model: a Case Study of Mersin University Çiftlikköy Campus. Turkish Journal of Geographic Information Systems, 4(2), 97-105. https://doi.org/10.56130/tucbis.1208096
  • Hamal S N G (2022). Accuracy of digital maps produced from UAV images in rural areas. Advanced UAV, 2(1), 29–34.
  • Huang G, Liu Z, Van Der Maaten L & Weinberger K Q (2017). Densely connected convolutional networks. IEEE conference on computer vision and pattern recognition, Honolulu, USA, 4700-4708.
  • Kabadayı A & Uysal M (2020). Building detection from high resolution UAV data. Turkey Unmanned Aerial Vehicle Journal, 2(2), 43-48.
  • Kabadayı A (2022). Maden Mapping of the Mine Site by Photogrammetric Method with the Help of Unmanned Aerial Vehicle. Turkey Unmanned Aerial Vehicle Journal, 4(1), 19-23. https://doi.org/10.51534/tiha.1130929
  • LeCun Y, Bengio Y & Hinton G (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
  • Lim Y S, La P H, Park J S, Lee M H, Pyeon M W & Kim J I. (2015). Calculation of tree height and canopy crown from drone images using segmentation. 한국측량학회지, 33(6), 605-613.
  • Lin Y, Jiang M, Yao Y, Zhang L & Lin J (2015). Use of UAV oblique imaging for the detection of individual trees in residential environments. Urban forestry & urban greening, 14(2), 404-412. https://doi.org/10.1016/j.ufug.2015.03.003
  • Livesley S J, McPherson E G & Calfapietra C (2016). The urban forest and ecosystem services: impacts on urban water, heat, and pollution cycles at the tree, street, and city scale. Journal of environmental quality, 45(1), 119-124. https://doi.org/10.2134/jeq2015.11.0567
  • Lowe G (2004). Sift-the scale invariant feature transform. Int. J, 2(2), 91-110.
  • Lucieer A, Jong S M D & Turner D (2014). Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress in physical geography, 38(1), 97-116. https://doi.org/10.1177/0309133313515293
  • Ma L, Liu Y, Zhang X, Ye Y, Yin G, & Johnson B A (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166-177. https://doi.org/10.1016/j.isprsjprs.2019.04.015
  • McHale M R, Burk, I C, Lefsky M A, Peper P J & McPherson E G (2009). Urban forest biomass estimates: is it important to use allometric relationships developed specifically for urban trees? Urban Ecosystems, 12, 95-113. https://doi.org/10.1007/s11252-009-0081-3
  • Mesas-Carrascosa F J, Notario García M D, Meroño de Larriva J E & García-Ferrer A (2016). An analysis of the influence of flight parameters in the generation of unmanned aerial vehicle (UAV) orthomosaicks to survey archaeological areas. Sensors, 16(11), 1838. https://doi.org/10.3390/s16111838
  • Orhan O, Oliver-Cabrer T, Wdowinski S, Yalvac S & Yakar, M. (2021). Land subsidence and its relations with sinkhole activity in Karapınar region, Turkey: a multi-sensor InSAR time series study. Sensors, 21(3), 774. https://doi.org/10.3390/s21030774
  • Pan Y, Birdse, R A, Fang J, Houghton R, Kauppi P E, Kurz W A, Phillips O L, Shvidenko A, Lewis S L, Canadell J G, Ciais P, Jackson R B, Pacala S W, Mcguire A D, Piao S, Rautiainen A, Sitch S & Hayes D (2011). A large and persistent carbon sink in the world’s forests. Science, 333(6045), 988-993. https://doi.org/10.1126/science.1201609
  • Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J & Chintala S (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vancouver, Canada, 32.
  • Peña-Barragán J M, Ngugi M K, Plant R E & Six J (2011). Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 115(6), 1301-1316. https://doi.org/10.1016/j.rse.2011.01.009
  • Popkin G (2019). The forest question. Nature, 565(7739), 280-282. https://doi.org/10.1038/d41586-019-00122-z
  • Şasi A & Yakar M (2017). Photogrammetric modelling of sakahane masjid using an unmanned aerial vehicle. Turkish Journal of Engineering, 1(2), 82-87. https://doi.org/10.31127/tuje.316675
  • Selim S, Sonmez N K, Coslu M & Onur I (2019). Semi-automatic tree detection from images of unmanned aerial vehicle using object-based image analysis method. Journal of the Indian Society of Remote Sensing, 47, 193-200. https://doi.org/10.1007/s12524-018-0900-1
  • Shanahan D F, Lin B B, Bush R, Gaston K J, Dean J H, Barber E & Fuller R A (2015). Toward improved public health outcomes from urban nature. American journal of public health, 105(3), 470-477.
  • Snavely N, Seitz S M & Szeliski R (2008). Modeling the world from internet photo collections. International journal of computer vision, 80, 189-210. https://doi.org/10.1007/s11263-007-0107-3
  • Tükenmez F & Yakar M (2023). Production of road maps in highway projects by unmanned aerial vehicle (UAV). Advanced Engineering Days (AED), Mersin, Türkiye, 6, 94-96.
  • Wäldchen J & Mäder P (2018). Machine learning for image based species identification. Methods in Ecology and Evolution, 9(11), 2216-2225. https://doi.org/10.1111/2041-210X.13075
  • Weinstein B G (2018). A computer vision for animal ecology. Journal of Animal Ecology, 87(3), 533-545. https://doi.org/10.1111/1365-2656.12780
  • Weiss M, Baret F, Block T, Koetz B, Burini A, Scholze B, Lecharpentier P, Brockmann C, Fernandes R, Plummer S, Myneni R, Gobron N, Nightingale J, Strub-Schaepman G, Camacho F & Sanchez-Azofeifa A (2014). On Line Validation Exercise (OLIVE): A web based service for the validation of medium resolution land products. Application to FAPAR products. Remote Sensing, 6(5), 4190-4216. https://doi.org/10.3390/rs6054190
  • Wulder M A, White J C, Nelson R F, Næsset E, Ørka H O, Coops N C, Hilker T, Bater C W & Gobakken T (2012). Lidar sampling for large-area forest characterization: A review. Remote sensing of environment, 121, 196-209. https://doi.org/10.1016/j.rse.2012.02.001
  • Xiao Q & McPherson E G (2011). Performance of engineered soil and trees in a parking lot bioswale. Urban Water Journal, 8(4), 241-253. https://doi.org/10.1080/1573062X.2011.596213
  • Yakar, M., & Dogan, Y. (2019). 3D Reconstruction of Residential Areas with SfM Photogrammetry. In Advances in Remote Sensing and Geo Informatics Applications: Proceedings of the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Tunisia 2018 (pp. 73-75). Springer International Publishing.
  • Yüksel G, Ulvi A & Yakar M (2022). Usage of unmanned aerial vehicles in open mine sites. Intercontinental Geoinformation Days, 4, 13-16.
  • Zou Q, Zhang Z, L, Q, Qi X, Wang Q & Wang S (2018). Deepcrack: Learning hierarchical convolutional features for crack detection. IEEE Transactions on Image Processing, 28(3), 1498-1512. https://doi.org/10.1109/TIP.2018.2878966

Decoding Nature's Patterns: An Innovative Approach to Tree Detection Using Deep Learning and High-Resolution Aerial Imagery

Yıl 2023, Cilt: 5 Sayı: 1, 52 - 59, 30.06.2023

Halil İbrahim Şenol Abdurahman Yasin Yiğit

https://doi.org/10.56130/tucbis.1307926

Öz

This study investigates the application of deep learning algorithms and high-resolution aerial imagery for individual tree detection in urban areas, using a neighborhood in Mersin, Turkey, as a case study. Employing the DeepForest Python package, we utilize high-resolution (7cm) aerial imagery to detect and map the city's tree population accurately. The results showcase an impressive accuracy rate of 80.87%, demonstrating the potential of deep learning in urban forestry applications and contributing to effective urban planning. The information generated from this study is crucial for conserving urban green spaces, enhancing resilience to climate change, and supporting urban biodiversity. While this research is focused on Mersin, the methods employed are globally adaptable, laying a foundation for further refinement and potential identification of different tree species in future work. This investigation highlights the transformative role of advanced technology in fostering sustainable urban environments.

Anahtar Kelimeler

GIS Spatial Analysis Aerial Imagery Deep Learning Photogrammetry

Kaynakça

  • Campbell N D, Vogiatzis G, Hernández C & Cipolla R (2008). Using multiple hypotheses to improve depth-maps for multi-view stereo. In Computer Vision–ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, 766-779.
  • Cruzan M B, Weinstein B G, Grasty M R, Kohrn B F, Hendrickson E C, Arredond, T M & Thompson P G (2016). Small unmanned aerial vehicles (micro‐UAVs, drones) in plant ecology. Applications in plant sciences, 4(9), 1600041. https://doi.org/10.3732/apps.1600041
  • Díaz S, Pascual U, Stenseke M, Martín-López B, Watson R T, Molnár Z, Hill R, Chan K M A, Bate I A, Brauman K A, Polasky S Church A, Lonsdale M, Larigauderie A, Leadley P W, Oudenhoven A P E V, Plaat F V D, Schröter M, Lavorel S, Thomas-Aumeeruddy Y, Bukvareva E, Davies K, Demissew S, Erpul G, Failler P, Guerra C A, Hewitt C H, Keune H, Lindley S & Shirayama Y (2018). Assessing nature's contributions to people. Science, 359(6373), 270-272. https://doi.org/10.1126/science.aap8826
  • Díaz-Varela R A, De la Rosa R, León L & Zarco-Tejad, P J (2015). High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: application in breeding trials. Remote Sensing, 7(4), 4213-4232. https://doi.org/10.3390/rs70404213
  • Duncanson L, Rourk, O & Dubayah R (2015). Small sample sizes yield biased allometric equations in temperate forests. Scientific reports, 5(1), 17153. https://doi.org/10.1038/srep17153
  • Ebrahimikia M & Hosseininaveh A (2022). True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points. The Photogrammetric Record, 37(178), 161-184. https://doi.org/10.1111/phor.12409
  • Escobedo F J, Kroeger T & Wagner J E (2011). Urban forests and pollution mitigation: Analyzing ecosystem services and disservices. Environmental pollution, 159(8-9), 2078-2087. https://doi.org/10.1016/j.envpol.2011.01.010
  • Franklin S E (2001). Remote sensing for sustainable forest management. CRC press, ISBN: 978-1-4200-3285-7. Goodfellow I, Bengio Y & Courville A (2016). Deep learning. MIT press, ISBN: 978-026-2035-61-3.
  • Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J & Chen T (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377. https://doi.org/10.1016/j.patcog.2017.10.0
  • Guerra-Hernández J, Cosenza D N, Rodriguez L C E, Silva, M, Tomé M, Díaz-Varela R A & González-Ferreiro E (2018). Comparison of ALS-and UAV (SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations. International Journal of Remote Sensing, 39(15-16), 5211-5235. https://doi.org/10.1080/01431161.2018.1486519
  • Hamal S N G & Ulvi A (2022). 3B UAV Photogrammetry and GIS Integration for 3D City Model: a Case Study of Mersin University Çiftlikköy Campus. Turkish Journal of Geographic Information Systems, 4(2), 97-105. https://doi.org/10.56130/tucbis.1208096
  • Hamal S N G (2022). Accuracy of digital maps produced from UAV images in rural areas. Advanced UAV, 2(1), 29–34.
  • Huang G, Liu Z, Van Der Maaten L & Weinberger K Q (2017). Densely connected convolutional networks. IEEE conference on computer vision and pattern recognition, Honolulu, USA, 4700-4708.
  • Kabadayı A & Uysal M (2020). Building detection from high resolution UAV data. Turkey Unmanned Aerial Vehicle Journal, 2(2), 43-48.
  • Kabadayı A (2022). Maden Mapping of the Mine Site by Photogrammetric Method with the Help of Unmanned Aerial Vehicle. Turkey Unmanned Aerial Vehicle Journal, 4(1), 19-23. https://doi.org/10.51534/tiha.1130929
  • LeCun Y, Bengio Y & Hinton G (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
  • Lim Y S, La P H, Park J S, Lee M H, Pyeon M W & Kim J I. (2015). Calculation of tree height and canopy crown from drone images using segmentation. 한국측량학회지, 33(6), 605-613.
  • Lin Y, Jiang M, Yao Y, Zhang L & Lin J (2015). Use of UAV oblique imaging for the detection of individual trees in residential environments. Urban forestry & urban greening, 14(2), 404-412. https://doi.org/10.1016/j.ufug.2015.03.003
  • Livesley S J, McPherson E G & Calfapietra C (2016). The urban forest and ecosystem services: impacts on urban water, heat, and pollution cycles at the tree, street, and city scale. Journal of environmental quality, 45(1), 119-124. https://doi.org/10.2134/jeq2015.11.0567
  • Lowe G (2004). Sift-the scale invariant feature transform. Int. J, 2(2), 91-110.
  • Lucieer A, Jong S M D & Turner D (2014). Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress in physical geography, 38(1), 97-116. https://doi.org/10.1177/0309133313515293
  • Ma L, Liu Y, Zhang X, Ye Y, Yin G, & Johnson B A (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166-177. https://doi.org/10.1016/j.isprsjprs.2019.04.015
  • McHale M R, Burk, I C, Lefsky M A, Peper P J & McPherson E G (2009). Urban forest biomass estimates: is it important to use allometric relationships developed specifically for urban trees? Urban Ecosystems, 12, 95-113. https://doi.org/10.1007/s11252-009-0081-3
  • Mesas-Carrascosa F J, Notario García M D, Meroño de Larriva J E & García-Ferrer A (2016). An analysis of the influence of flight parameters in the generation of unmanned aerial vehicle (UAV) orthomosaicks to survey archaeological areas. Sensors, 16(11), 1838. https://doi.org/10.3390/s16111838
  • Orhan O, Oliver-Cabrer T, Wdowinski S, Yalvac S & Yakar, M. (2021). Land subsidence and its relations with sinkhole activity in Karapınar region, Turkey: a multi-sensor InSAR time series study. Sensors, 21(3), 774. https://doi.org/10.3390/s21030774
  • Pan Y, Birdse, R A, Fang J, Houghton R, Kauppi P E, Kurz W A, Phillips O L, Shvidenko A, Lewis S L, Canadell J G, Ciais P, Jackson R B, Pacala S W, Mcguire A D, Piao S, Rautiainen A, Sitch S & Hayes D (2011). A large and persistent carbon sink in the world’s forests. Science, 333(6045), 988-993. https://doi.org/10.1126/science.1201609
  • Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J & Chintala S (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vancouver, Canada, 32.
  • Peña-Barragán J M, Ngugi M K, Plant R E & Six J (2011). Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 115(6), 1301-1316. https://doi.org/10.1016/j.rse.2011.01.009
  • Popkin G (2019). The forest question. Nature, 565(7739), 280-282. https://doi.org/10.1038/d41586-019-00122-z
  • Şasi A & Yakar M (2017). Photogrammetric modelling of sakahane masjid using an unmanned aerial vehicle. Turkish Journal of Engineering, 1(2), 82-87. https://doi.org/10.31127/tuje.316675
  • Selim S, Sonmez N K, Coslu M & Onur I (2019). Semi-automatic tree detection from images of unmanned aerial vehicle using object-based image analysis method. Journal of the Indian Society of Remote Sensing, 47, 193-200. https://doi.org/10.1007/s12524-018-0900-1
  • Shanahan D F, Lin B B, Bush R, Gaston K J, Dean J H, Barber E & Fuller R A (2015). Toward improved public health outcomes from urban nature. American journal of public health, 105(3), 470-477.
  • Snavely N, Seitz S M & Szeliski R (2008). Modeling the world from internet photo collections. International journal of computer vision, 80, 189-210. https://doi.org/10.1007/s11263-007-0107-3
  • Tükenmez F & Yakar M (2023). Production of road maps in highway projects by unmanned aerial vehicle (UAV). Advanced Engineering Days (AED), Mersin, Türkiye, 6, 94-96.
  • Wäldchen J & Mäder P (2018). Machine learning for image based species identification. Methods in Ecology and Evolution, 9(11), 2216-2225. https://doi.org/10.1111/2041-210X.13075
  • Weinstein B G (2018). A computer vision for animal ecology. Journal of Animal Ecology, 87(3), 533-545. https://doi.org/10.1111/1365-2656.12780
  • Weiss M, Baret F, Block T, Koetz B, Burini A, Scholze B, Lecharpentier P, Brockmann C, Fernandes R, Plummer S, Myneni R, Gobron N, Nightingale J, Strub-Schaepman G, Camacho F & Sanchez-Azofeifa A (2014). On Line Validation Exercise (OLIVE): A web based service for the validation of medium resolution land products. Application to FAPAR products. Remote Sensing, 6(5), 4190-4216. https://doi.org/10.3390/rs6054190
  • Wulder M A, White J C, Nelson R F, Næsset E, Ørka H O, Coops N C, Hilker T, Bater C W & Gobakken T (2012). Lidar sampling for large-area forest characterization: A review. Remote sensing of environment, 121, 196-209. https://doi.org/10.1016/j.rse.2012.02.001
  • Xiao Q & McPherson E G (2011). Performance of engineered soil and trees in a parking lot bioswale. Urban Water Journal, 8(4), 241-253. https://doi.org/10.1080/1573062X.2011.596213
  • Yakar, M., & Dogan, Y. (2019). 3D Reconstruction of Residential Areas with SfM Photogrammetry. In Advances in Remote Sensing and Geo Informatics Applications: Proceedings of the 1st Springer Conference of the Arabian Journal of Geosciences (CAJG-1), Tunisia 2018 (pp. 73-75). Springer International Publishing.
  • Yüksel G, Ulvi A & Yakar M (2022). Usage of unmanned aerial vehicles in open mine sites. Intercontinental Geoinformation Days, 4, 13-16.
  • Zou Q, Zhang Z, L, Q, Qi X, Wang Q & Wang S (2018). Deepcrack: Learning hierarchical convolutional features for crack detection. IEEE Transactions on Image Processing, 28(3), 1498-1512. https://doi.org/10.1109/TIP.2018.2878966
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Halil İbrahim Şenol 0000-0003-0235-5764

Abdurahman Yasin Yiğit 0000-0002-9407-8022

Erken Görünüm Tarihi 23 Haziran 2023
Yayımlanma Tarihi 30 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 5 Sayı: 1

Kaynak Göster

APA Şenol, H. İ., & Yiğit, A. Y. (2023). Decoding Nature’s Patterns: An Innovative Approach to Tree Detection Using Deep Learning and High-Resolution Aerial Imagery. Türkiye Coğrafi Bilgi Sistemleri Dergisi, 5(1), 52-59. https://doi.org/10.56130/tucbis.1307926
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