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1Conference
المؤلفون: Süberk, Nilay Tuğçe, Ateş, Hasan Fehmi
مصطلحات موضوعية: Bina yoğunluk kestirimi, Derin öğrenme, Uzaktan algılama, Accurate density estimation, Additional training, Building density estimation, Building heat maps, Building locations, Building densities, Buildings, CNN, Comparative simulation, Comparative simulation results, Convolution, Convolutional network, Convolutional neural nets, Convolutional neural network, Deep architectures, Deep learning, Deep learning approaches, Deep learning methods, Density estimation, Geophysical image processing, Learning (artificial intelligence), Learning approach, Network architecture, Neural networks, Optical images, Point-wise estimation, Pre-trained VGG-16
وصف الملف: application/pdf
Relation: 2019 4th International Conference on Computer Science and Engineering (UBMK); Konferans Öğesi - Uluslararası - İdari Personel ve Öğrenci; Suberk, N. T. & Ateş, H. F. (2019). Deep learning for building density estimation in remotely sensed imagery. Paper presented at the 423-428. doi:10.1109/UBMK.2019.8907133; https://hdl.handle.net/11729/2295; http://dx.doi.org/10.1109/UBMK.2019.8907133; 423; 428; N/A; WOS:000609879900080; 2-s2.0-85076199572
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المؤلفون: Süberk, Nilay Tuğçe, Ateş, Hasan Fehmi
المساهمون: Işık Üniversitesi, Fen Bilimleri Enstitüsü, Süberk, Nilay Tuğçe
مصطلحات موضوعية: Convolutional network, Learning (artificial intelligence), Time 8.0 s, Building Density Estimation, Comparative simulation results, Derin Öğrenme, Point-wise estimation, Supervised trainings, Convolutional neural network, Building heat maps, Convolutional neural nets, Remotely sensed imagery, Remote sensing optical imagery, Deep learning approaches, Remote Sensing, Optical images, Deep Learning, Learning approach, Pre-trained VGG-16, Density estimation, Buildings, Accurate density estimation, Bina yoğunluk kestirimi, Deep learning methods, Supervised training, Uzaktan Algılama, Network architecture, Bina Yoğunluk Kestirimi, Comparative simulation, Deep learning, Building density estimation, Remote sensing, Uzaktan algılama, Convolution, Derin öğrenme, Building locations, Deep architectures, Building densities, Additional training, CNN, Geophysical image processing, Neural networks
وصف الملف: application/pdf