Abstract. Land Use and Land Cover (LULCs) mapping and change detection are of paramount importance to understand the distribution and effectively monitor the dynamics of the Earth’s system. An unexplored way to create global LULC maps is by building good quality LULC-models based on state-of-the-art deep learning networks. Building such models requires large global good quality time series LULC datasets, which are not available yet. This paper presents TimeSpec4LULC (Khaldi et al., 2021), a smart open-source global dataset of multi-Spectral Time series for 29 LULC classes. TimeSpec4LULC was built based on the 7 spectral bands of MODIS sensor at 500 m resolution from 2002 to 2021, and was annotated using a spatial agreement across the 15 global LULC products available in Google Earth Engine. The 19-year monthly time series of the seven bands were created globally by: (1) applying different spatio-temporal quality assessment filters on MODIS Terra and Aqua satellites, (2) aggregating their original 8-day temporal granularity into monthly composites, (3) merging their data into a Terra+Aqua combined time series, and (4) extracting, at the pixel level, 11.85 million time series for the 7 bands along with a set of metadata about geographic coordinates, country and departmental divisions, spatio-temporal consistency across LULC products, temporal data availability, and the global human modification index. To assess the annotation quality of the dataset, a sample of 100 pixels, evenly distributed around the world, from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing and evaluating various machine learning models, including deep learning networks, to perform global LULC mapping and change detection.

中文翻译：

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TimeSpec4LULC：用于 LULC 映射和变化检测的 MODIS Terra-Aqua 多光谱时间序列的全局深度学习驱动数据集

摘要。土地利用和土地覆盖 (LULC) 制图和变化检测对于了解分布和有效监测地球系统的动态至关重要。创建全局 LULC 地图的一种尚未探索的方法是基于最先进的深度学习网络构建高质量的 LULC 模型。构建这样的模型需要大型的全球优质时间序列 LULC 数据集，目前尚不可用。本文介绍了 TimeSpec4LULC（Khaldi 等人，2021 年），这是一个用于 29 个 LULC 类的多光谱时间序列的智能开源全球数据集。TimeSpec4LULC 基于 2002 年至 2021 年间分辨率为 500 m 的 MODIS 传感器的 7 个光谱带构建，并使用 Google Earth Engine 中可用的 15 种全球 LULC 产品的空间协议进行注释。七个波段的 19 年月度时间序列是通过以下方式在全球创建的：(1) 在 MODIS Terra 和 Aqua 卫星上应用不同的时空质量评估过滤器，(2) 将其原始的 8 天时间粒度聚合为月度复合材料，( 3) 将他们的数据合并到一个 Terra+Aqua 组合时间序列中，以及 (4) 在像素级别提取 7 个波段的 1185 万个时间序列以及一组关于地理坐标、国家和部门划分、空间- LULC 产品的时间一致性、时间数据可用性和全球人类修改指数。为了评估数据集的注释质量，从每个 LULC 类中平均分布在世界各地的 100 个像素的样本，由专家使用来自 Google 地球和 Bing 地图图像的超高分辨率图像进行选择和验证。这个经过智能预处理和注释的数据集面向对开发和评估各种机器学习模型（包括深度学习网络）感兴趣的科学用户，以执行全局 LULC 映射和变化检测。