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.
翻译:该卷载有“机器学习挑战”的选定文章,《发现玛雅人的秘密”,在“欧洲数据库中发现机器学习和知识发现的原则和实践的发现挑战追踪”(ECML PKDD 2021)。遥感大大加快了古玛雅森林地区的传统考古景观调查。典型的探索和发现尝试,除了侧重于整个古代城市外,还侧重于个别建筑和结构。最近,曾几次成功尝试利用机器学习来识别古玛雅人定居点。这些尝试虽然具有相关性,但侧重于狭窄地区,并仅依赖覆盖古玛雅人曾经定居过的区域的一部分的高质量空中激光扫描数据。另一方面,欧洲航天局(欧空局)哨兵飞行任务制作的卫星图像数据丰富,更重要的是,公开提供。“发现玛雅人的神秘”挑战旨在查找和识别古玛雅人建筑(建筑、古阿加达斯和平台),通过对不同类型卫星图像(来自Sentinel-1和Sentinel-2的数据和ALS)进行综合图像分割。