Handling Massive Data Size Issue in Buildings Footprints Extraction from High-Resolution Satellite Images
Yazarlar (2)
Dr. Öğr. Üyesi Sohaib K.M. Abujayyab Karabük Üniversitesi, Türkiye
Prof. Dr. İsmail Rakıp KARAŞ Karabük Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SCOPUS dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Springer Communications in Computer and Information Science | ||
| Dergi ISSN | 1865-0929 Dergi Bilgileri (2020) | ||
| Dergi Tarandığı Indeksler | SCOPUS, EI-Compendex, Mathematical Reviews, SCImago, . | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2020 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-01-2020 |
| Cilt / Sayı / Sayfa | 1188 / 1 / 195–210 | DOI | 10.1007/978-3-030-42852-5_16 |
| Makale Linki | https://doi.org/10.1007/978-3-030-42852-5_16 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
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| Özet |
| Building information modelling BIM is relying on plenty of geospatial information such as buildings footprints. Collecting and updating BIM information is a considerable challenge. Recently, buildings footprints automatically extracted from high-resolution satellite images utilizing machine learning algorithms. Constructing required training datasets for machine learning algorithms and testing data is computationally intensive. When the analysis performs in large geographic areas, researchers are struggling from out of memory problems. The requirement of developing improved, fit memory computation methods for accomplishing this computation is urgent. This paper targeting to handling massive data size issue in buildings footprints extraction from high-resolution satellite images. This article established a method to process the spatial raster data based on the chunks computing. Chunk-based decomposition … |
| Anahtar Kelimeler |
| Buildings footprints extraction | Buildings Information Modelling | High resolution satellite images | Massive data size | Neural networks |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |