I am happy to share that our article “Comparative analysis of deep learning based building extraction methods with the new VHR Istanbul dataset” has just been published in the journal of Expert Systems with Applications. My first academic paper is online! (Q1, IF: 6.954)
More than 60 experiments were conducted by applying state-of-the-art architectures such as U-Net, Unet++, DeepLabv3+, FPN and PSPNet with different pre-trained encoders and hyperparameters. Our experiments showed that Unet++ architecture using SE-ResNeXt101 encoder pre-trained with ImageNet provides the best results with 93.8% IoU on the Istanbul dataset.
Our Paper: Comparative analysis of deep learning-based building extraction methods with the new VHR Istanbul dataset:
This link above provides 50 days of free access to the article. Anyone clicking on this link before June 26, 2022, will be taken directly to the final version of your article on ScienceDirect, which they are welcome to read or download.
I created a repo for the paper. You can access the repo:
GitHub - irem-komurcu/Comparative-Analysis-Building-Extraction-Methods: image segmentation with…
This repo contains weights of Unet++ model with SE-ResNeXt101 encoder trained with our private Istanbul dataset and…
GitHub - TolgaBkm/Istanbul_Dataset
This repo contains weights of Unet++ model with SE-ResNeXt101 encoder trained with Istanbul, Inria and Massachusetts…
Journal: Expert Systems with Applications
Expert Systems with Applications — Journal — Elsevier
Expert Systems With Applications is a refereed international journal whose focus is on exchanging information relating…
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