Academic Journal
GPU based building footprint identification utilising self-attention multiresolution analysis
| العنوان: | GPU based building footprint identification utilising self-attention multiresolution analysis |
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| المؤلفون: | Rizwan Ahmed Ansari, Akshat Ramachandran, Winnie Thomas |
| المصدر: | All Earth, Vol 35, Iss 1, Pp 102-111 (2023) |
| بيانات النشر: | Taylor & Francis Group, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Geology LCC:Physical geography |
| مصطلحات موضوعية: | Urban analysis, building identification, multiresolution analysis, self-attenuation, graphic processing unit, Geology, QE1-996.5, Physical geography, GB3-5030 |
| الوصف: | ABSTRACTTechniques for the semantic segmentation of remotely sensed imageries for building footprint identification have been widely studied and several supervised and unsupervised techniques have been proposed. The ability to perform online mapping and accurate segmentation on a large scale by taking into account the multifariousness inherent in aerial images has important implications. In this paper we propose a new method for building footprint identification using multiresolution analysis-based self-attention technique. The scheme is promising to be robust in the face of variability inherent in remotely sensed images by virtue of the capability to extract features at multiple scales and focusing on areas containing meaningful information. We demonstrate the robustness of the proposed method by comparing it against several state-of-the-art techniques using aerial imagery with varying spatial resolution and building clutter and it achieves better accuracy around 95% even under widely disparate image characteristics. We also evaluate the ability for online mapping on an embedded graphic processing unit (GPU) and compare it against different compute engines and it is found that the proposed method on GPU outperforms the other methods in terms of accuracy and processing time. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 27669645 2766-9645 |
| Relation: | https://doaj.org/toc/2766-9645 |
| DOI: | 10.1080/27669645.2023.2202961 |
| URL الوصول: | https://doaj.org/article/fd9e93ecfba24a4e9eb076dd4132f11b |
| رقم الانضمام: | edsdoj.fd9e93ecfba24a4e9eb076dd4132f11b |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 27669645 |
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| DOI: | 10.1080/27669645.2023.2202961 |