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.

Highlights

  • Accurate Land-Use and Land-Cover (LULC) information, including distribution, dynamics and changes, is of paramount 20 importance for understanding and modelling the natural and human-modified behavior of the Earth’s system (Tuanmu and Jetz, 2014; Verburg et al, 2009)

  • In general, we have at least 1,158 time series in each LULC class which is sufficient for Deep Learning (DL) modeling

  • Accurate LULC mapping and change detection is highly relevant for many applications, including Earth system modeling, environmental monitoring, management and planning, or natural hazards assessment, among many others

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Summary

Introduction

Accurate Land-Use and Land-Cover (LULC) information, including distribution, dynamics and changes, is of paramount 20 importance for understanding and modelling the natural and human-modified behavior of the Earth’s system (Tuanmu and Jetz, 2014; Verburg et al, 2009). Urban sprawl and agriculture expansion or abandonment affect 30 the biodiversity, soil quality, climate, food security, and human health (Lambin and Geist, 2008; Feddema et al, 2005). For this reason, continuous and accurate LULC and LULC change mapping is essential in policy and research to monitor ecological and environmental change at different temporal and spatial scales (Polykretis et al, 2020; García-Mora et al, 2012), and as a decision support system to ensure an effective and sustainable planning and management of natural resources (Kong et al, 2016; Congalton et al, 2014; Grekousis et al, 2015). Since the 1980s, multiple global LULC products (Table 1) have been derived from remotely 40 sensed data, providing alternative characterizations of the Earth surface at varying extents of spatial and temporal resolutions (Townshend et al, 1991; Loveland et al, 2000; Bartholome and Belward, 2005)

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