Derin öğrenme uygulamalarında kullanılan uzaktan algılama verilerinden oluşturulmuş açık kaynaklı bina veri setleri: Karşılaştırmalı değerlendirme
Year 2024,
Volume: 9 Issue: 1, 1 - 11, 15.04.2024
Esra Özaydın
,
Burcu Amirgan
,
Gülşen Taşkın
,
Nebiye Musaoğlu
Abstract
Bina çıkarımı; arazi kullanımı, şehir planlaması, afet izleme, navigasyon, coğrafi veri tabanlarının güncellenmesi ve kentsel dinamik izleme gibi çeşitli mekânsal uygulamalarda önemli rol oynar. Farklı bölgelerdeki binalar farklı yapısal ve geometrik özelliklere sahip olduğundan görüntülerden otomatik bina çıkarımı zor bir iştir. Son yıllarda uygun veri setleriyle eğitildiklerinde klasik makine öğrenme yöntemlerine göre daha yüksek doğruluklu sonuçlar üreten derin öğrenme modelleri, otomatik bina çıkarımında sıkça kullanılmaktadır. Modellerin yüksek doğrulukta eğitilmesi için kaliteli etiketlerin olduğu bina veri setleri büyük önem taşımaktadır. Bu çalışmanın amacı, bina tespiti için farklı çözünürlükteki uzaktan algılama görüntülerinden oluşturulmuş ve literatürde sıkça kullanılan açık kaynaklı bina veri setlerini tanıtmaktır. Veri setleri, kaydedildiği platformlara göre havadan, uydudan ve her iki platformdan kaydedilmiş görüntülerden oluşan veriler olarak üç kategoride gruplandırılıp, detayları açıklanmıştır. Bunun yanı sıra veri setleri ile yapılmış karşılaştırmalı çalışmaları içeren güncel literatür özeti verilmiştir. Bina tespiti işlemini doğru şekilde gerçekleştirmek için araştırmacılara rehberlik edecek ve bina veri seti oluşturulmasında dikkat edilmesi gereken kritik hususları içeren değerlendirmeler sunulmuştur.
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Year 2024,
Volume: 9 Issue: 1, 1 - 11, 15.04.2024
Esra Özaydın
,
Burcu Amirgan
,
Gülşen Taşkın
,
Nebiye Musaoğlu
References
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- Amirgan, B., Awad, B., Erer, I., & Musaoğlu, N. (2022). A comparative study for building segmentation in remote sensing images using deep networks: Cscrs Istanbul building dataset and results. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 46, 1-6. https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-1-2022
- Atik, M. E., Duran, Z., & Özgünlük, R. (2022). Comparison of YOLO versions for object detection from aerial images. International Journal of Environment and Geoinformatics, 9(2), 87-93.
https://doi.org/10.30897/ijegeo.1010741
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https://doi.org/10.29128/geomatik.947334
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- Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017). Can semantic labeling methods generalize to any city? The Inria aerial image labeling benchmark. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3226-3229. 10.1109/IGARSS.2017.8127684
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- Mnih, V. (2013). Machine learning for aerial image labeling. University of Toronto (Canada).
- Mohanty, S. P., Czakon, J., Kaczmarek, K. A., Pyskir, A., Tarasiewicz, P., Kunwar, S., ... & Schilling, M. (2020). Deep learning for understanding satellite imagery: An experimental survey. Frontiers in Artificial Intelligence, 3, 534696. https://doi.org/10.3389/frai.2020.534696
- URL-1: https://haberler.itu.edu.tr/docs/default-source/default-document-library/2023_itu_deprem_on_raporu.pdf?sfvrsn=bf82d8e5_
- URL-2: https://www.isprs.org/education/benchmarks/UrbanSemLab/semantic-labeling.aspx
- URL-3: https://www.cs.toronto.edu/~vmnih/data/
- URL-4: https://project.inria.fr/aerialimagelabeling/
- URL-5: https://competitions.codalab.org/competitions/20100
- URL-6: https://www.kaggle.com/datasets/adrianboguszewski/landcoverai
- URL-7: https://www.kaggle.com/datasets/atilol/aerialimageryforroofsegmentation
- URL-8: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EXRA2V
- URL-9: https://github.com/sajmonogy/keras_segmentation_models
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- URL-11:https://spacenet.ai/spacenet-buildings-dataset-v2/
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- URL-13: https://spacenet.ai/off-nadir-building-detection/
- URL-14: https://xview2.org/dataset
- URL-15: https://spacenet.ai/sn6-challenge/
- URL-16: https://spacenet.ai/sn7-challenge/
- URL-17: http://rs.ipb.uni-bonn.de/data/semcity-toulouse-data-access/
- URL-18: https://sites.research.google/open-buildings/#download
- URL-19: http://gpcv.whu.edu.cn/data/building_dataset.html
- URL-20: http://gpcv.whu.edu.cn/data/whu-mix(raster)/whu_mix%20(raster).html
- URL-21: https://www.geoportal.gov.pl/
- Ozturk, O., Saritürk, B., & Seker, D. Z. (2020). Comparison of fully convolutional networks (FCN) and U-Net for road segmentation from high resolution imageries. International Journal of Environment and Geoinformatics, 7(3), 272-279. https://doi.org/10.30897/ijegeo.737993
- Patel, K., Bhatt, C., & Mazzeo, P. L. (2022). Deep learning-based automatic detection of ships: An experimental study using satellite images. Journal of Imaging, 8(7), 182. https://doi.org/10.3390/jimaging8070182
- Perihanoğlu, G. M., Özerman, U., & Şeker, D. Z. (2018). Kenar algılama ve morfoloji operatörleri kullanılarak detay çıkarımı üzerine bir uygulama. Geomatik, 3(2), 120-128. https://doi.org/10.29128/geomatik.358957
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https://doi.org/10.3390/rs14194744