تقرير
Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021
| العنوان: | Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021 |
|---|---|
| المؤلفون: | Kocev, Dragi, Simidjievski, Nikola, Kostovska, Ana, Dimitrovski, Ivica, Kokalj, Žiga |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021). Remote sensing has greatly accelerated traditional archaeological landscape surveys in the forested regions of the ancient Maya. Typical exploration and discovery attempts, beside focusing on whole ancient cities, focus also on individual buildings and structures. Recently, there have been several successful attempts of utilizing machine learning for identifying ancient Maya settlements. These attempts, while relevant, focus on narrow areas and rely on high-quality aerial laser scanning (ALS) data which covers only a fraction of the region where ancient Maya were once settled. Satellite image data, on the other hand, produced by the European Space Agency's (ESA) Sentinel missions, is abundant and, more importantly, publicly available. The "Discover the Mysteries of the Maya" challenge aimed at locating and identifying ancient Maya architectures (buildings, aguadas, and platforms) by performing integrated image segmentation of different types of satellite imagery (from Sentinel-1 and Sentinel-2) data and ALS (lidar) data. Comment: Chapter authors. Chapter 1: Matthew Painter and Iris Kramer; Chapter 2: J\"urgen Landauer, Burkhard Hoppenstedt, and Johannes Allgaier; Chapter 3: Thorben Hellweg, Stefan Oehmcke, Ankit Kariryaa, Fabian Gieseke, and Christian Igel; Chapter 4: Christian Ayala, Carlos Aranda, and Mikel Galar |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2208.03163Test |
| رقم الانضمام: | edsarx.2208.03163 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |