Field Detection from Satellite Images with Deep Learning Methods

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dc.contributor.authorTürkdamar, Mehmet Uğur
dc.contributor.authorTaşyürek, Murat
dc.contributor.authorÖztürk, Celal
dc.date.accessioned2024-11-07T10:39:32Z
dc.date.available2024-11-07T10:39:32Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description6th International Conference on Inventive Computation Technologies, ICICT 2023 -- 26 April 2023 through 28 April 2023 -- Lalitpur -- 189117
dc.description.abstractToday, effective production is interrupted unless land ownership disputes are resolved. The state cannot make the necessary investments due to these disputes not being concluded, and the borders of the fields remain unclear. Artificial intelligence-based methods can be suggested to eliminate disagreements and uncertainty. By using convolutional neural network (CNN) based deep learning networks in which image data are meaningful, areas with primary importance in crop production have been identified in this study. With the CNN networks used by computer vision technology, meaningful information can be extracted from the image. Field detection processes were carried out in this study by using deep learning networks that learn from data. As remote sensing studies gain speed, the number of deep learning studies also increases. For this purpose, satellite images were first collected from the Google Earth website, and then these collected images were used in Faster R-CNN and SSD training, which gained a reputation for accuracy and speed. It is aimed to provide more efficient production and resolve disputes by detecting the fields from satellite images. From two different networks running, SSD outperformed Faster R-CNN in terms of both accuracy and run time. With an f1 score of %97.32, SSD gave Faster R-CNN %3.18 superiority. In the field object results in the test images, the SSD outperformed by detecting 12 more fields. In terms of run times, the SSD performed faster detections with a difference of 285.5ms in the experiments tried in one-third of the test images. © 2023 IEEE.
dc.identifier.doi10.1109/ICICT57646.2023.10134299
dc.identifier.endpage8
dc.identifier.isbn979-835039849-6
dc.identifier.scopus2-s2.0-85163449346
dc.identifier.scopusqualityN/A
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1109/ICICT57646.2023.10134299
dc.identifier.urihttps://hdl.handle.net/11480/11032
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof6th International Conference on Inventive Computation Technologies, ICICT 2023 - Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectCNN
dc.subjectcrop field detection
dc.subjectdeep learning
dc.subjectfaster region-CNN
dc.subjectsatellite images
dc.subjectsingle shot detector
dc.titleField Detection from Satellite Images with Deep Learning Methods
dc.typeConference Object

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