دورية أكاديمية
Ensemble Learning for Semantic Segmentation of Ancient Maya Architectures
| العنوان: | Ensemble Learning for Semantic Segmentation of Ancient Maya Architectures |
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| المؤلفون: | Hellweg, Thorben, Oehmcke, Stefen, Kariryaa, Ankit, Gieseke, Fabian, Igel, Christian |
| المساهمون: | Kocev, Dragi, Simidjievski, Nikola, Kostovska, Ana, Dimitrovski, Ivica, Kokalj, Žiga |
| المصدر: | Hellweg , T , Oehmcke , S , Kariryaa , A , Gieseke , F & Igel , C 2022 , Ensemble Learning for Semantic Segmentation of Ancient Maya Architectures . in D Kocev , N Simidjievski , A Kostovska , I Dimitrovski & Ž Kokalj (eds) , Discover the Mysteries of the Maya : Selected Contributions from the Machine Learning Challenge & the Discovery Challenge Workshop, ECML PKDD 2021 . Jožef Stefan Institute , Ljubljana , pp. 13-19 , European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2021 , Bilbao , Spain .... |
| بيانات النشر: | Jožef Stefan Institute |
| سنة النشر: | 2022 |
| المجموعة: | University of Copenhagen: Research / Forskning ved Københavns Universitet |
| الوصف: | Deep learning methods hold great promise for the automatic analysis of large-scale remote sensing data in archaeological research. Here, we present a robust approach to locating ancient Maya architectures (buildings, aguadas, and platforms) based on integrated segmentation of satellite imagery and aerial laser scanning data. Deep learning models with different architectures and loss functions were trained and combined to form an ensemble for pixel-wise classification. We applied both training data augmentation as well as test-time augmentation and performed morphological cleaning in the postprocessing phase. Our approach was evaluated in the context of the “Discover the mysteries of the Maya: An Integrated Image Segmentation Challenge” at ECML PKDD 2021 and achieved one of the best results with an average IoU of 0.8183. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| DOI: | 10.48550/arXiv.2208.03163 |
| الإتاحة: | https://doi.org/10.48550/arXiv.2208.03163Test https://curis.ku.dk/portal/da/publications/ensemble-learning-for-semantic-segmentation-of-ancient-maya-architecturesTest(25c2043f-d993-44cf-9b04-5b926cf004a3).html https://curis.ku.dk/ws/files/339336211/Ensemble_Learning_for_Semantic_Segmentation_of_Ancien.pdfTest |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.F412066D |
| قاعدة البيانات: | BASE |
| DOI: | 10.48550/arXiv.2208.03163 |
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