Taskin Kavzoglu

Orcid: 0000-0002-9779-3443

According to our database1, Taskin Kavzoglu authored at least 12 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of six.
  • Erdős number3 of six.

Timeline

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Bibliography

2024
Automated identification of vehicles in very high-resolution UAV orthomosaics using YOLOv7 deep learning model.
Turkish J. Electr. Eng. Comput. Sci., 2024

2023
Effects of auxiliary and ancillary data on LULC classification in a heterogeneous environment using optimized random forest algorithm.
Earth Sci. Informatics, March, 2023

2022
Analysis of patch and sample size effects for 2D-3D CNN models using multiplatform dataset: hyperspectral image classification of ROSIS and Jilin-1 GP01 imagery.
Turkish J. Electr. Eng. Comput. Sci., 2022

Ensemble Conditioning Factor Selection with Markov Chain Framework for Shallow Landslide Susceptibility Mapping in Lake Sapanca Basin and its Vicinity, Turkey.
Balt. J. Mod. Comput., 2022

Performance Evaluation of Depthwise Separable CNN and Random Forest Algorithms for Landslide Susceptibility Prediction.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

Stone Pine (Pinus Pinea L.) Detection from High-Resolution UAV Imagery Using Deep Learning Model.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

Poplar Tree Index (PTI): A New Vegetation Index for Monitoring Poplar Cultivated Areas.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

2021
Shared Blocks-Based Ensemble Deep Learning for Shallow Landslide Susceptibility Mapping.
Remote. Sens., 2021

2020
Design of Feedforward Neural Networks in the Classification of Hyperspectral Imagery Using Superstructural Optimization.
Remote. Sens., 2020

2016
Performance evaluation of rotation forest for svm-based recursive feature elimination using hyperspectral imagery.
Proceedings of the 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2016

2009
Increasing the accuracy of neural network classification using refined training data.
Environ. Model. Softw., 2009

A kernel functions analysis for support vector machines for land cover classification.
Int. J. Appl. Earth Obs. Geoinformation, 2009


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