论文标题
OpenArthmap：全球高分辨率土地覆盖映射的基准数据集
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
论文作者
论文摘要
我们介绍了一个基准数据集OpenArthmap，用于全球高分辨率土地覆盖映射。 OpenArthmap由5000个空中和卫星图像的220万个部分组成，涵盖了来自6大洲44个国家的97个地区，并在0.25--0.5m的地面采样距离处手动注释8级土地覆盖标签。在全球范围内对OpenArthmap概括进行培训的语义分割模型，可以用作各种应用程序的现成模型。我们评估了无监督域适应的最先进方法的性能，并提供了适合进一步技术开发的具有挑战性的问题设置。我们还使用自动化的神经体系结构搜索有限的计算资源和快速映射研究轻量级模型。该数据集可在https://open-earth-map.org上找到。
We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents, with manually annotated 8-class land cover labels at a 0.25--0.5m ground sampling distance. Semantic segmentation models trained on the OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications. We evaluate the performance of state-of-the-art methods for unsupervised domain adaptation and present challenging problem settings suitable for further technical development. We also investigate lightweight models using automated neural architecture search for limited computational resources and fast mapping. The dataset is available at https://open-earth-map.org.
