论文标题
场景到点地球观察：多个实例学习土地覆盖分类
Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification
论文作者
论文摘要
土地覆盖分类（LCC），并监视土地利用如何随时间变化，是缓解气候变化和适应的重要过程。使用机器学习与地球观察数据的现有方法依赖于完全注销和分割的数据集。创建这些数据集需要大量的努力，并且缺乏合适的数据集已成为扩展LCC使用的障碍。在这项研究中，我们提出了场景与点模型：一种使用多个实例学习（MIL）的替代LCC方法，仅需要高级场景标签。这使得新数据集的开发速度更快，同时仍通过补丁级预测提供细分，最终增加了将LCC用于不同方案的可访问性。在DeepGlobe-LCC数据集上，我们的方法在场景和补丁级预测上的表现都优于非MIL基准。这项工作为扩大LCC在气候变化缓解技术，政府和学术界的使用奠定了基础。
Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patch models: an alternative LCC approach utilising Multiple Instance Learning (MIL) that requires only high-level scene labels. This enables much faster development of new datasets whilst still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using LCC for different scenarios. On the DeepGlobe-LCC dataset, our approach outperforms non-MIL baselines on both scene- and patch-level prediction. This work provides the foundation for expanding the use of LCC in climate change mitigation methods for technology, government, and academia.
