Abstract
As urbanized areas continue to expand rapidly across all continents, the United Nations adopted in 2015 the Sustainable Development Goal (SDG) 11, aimed at shaping a sustainable future for city dwellers. Earth Observation (EO) satellite data can provide at a fine scale, essential urban land use information for computing SDG 11 indicators in order to complement or even replace inaccurate or invalid existing spatial datasets. This study proposes an EO-based approach for extracting large scale information regarding urban open spaces (UOS) and land allocated to streets (LAS) at the city level, for calculating SDG indicator 11.7.1. The research workflow was developed over the Athens metropolitan area in Greece using deep learning classification models for processing PlanetScope and Sentinel-1 imagery, employing freely-available cloud environments offered by Google. The LAS model exhibited satisfactory results while the best experiment performance for mapping UOS, considering both PlanetScope and Sentinel-1 data, yielded high commission errors, however, the cross-validation analysis with the UOS area of OpenStreetMap exhibited a total overlap of 67.38%, suggesting that our workflow is suitable for creating a “potential” UOS layer. The methodology developed herein can serve as a roadmap for the calculation of indicator 11.7.1 through national statistical offices when spatial data are absent or unreliable.
Highlights
Urban areas cover 7.6% of the global land mass, approximately half the size of the European Union [1], and already hold the majority of the world’s population [2], which is expected to increase by 10% until 2030, from a current 7.7 billion to 8.5 billion [3].As urbanized areas continue to expand rapidly across all continents, sustainable and successful management of urban growth is essential at the local, national, and even international level
The use of DL algorithms within a free cloud computing resource further unlocks the potential of Earth Observation (EO) for providing operational and accurate information for decision making and sustainability monitoring, over urban agglomerations, compared to previous classification approaches relying on shallow learning [11]
The model for mapping land allocated to streets (LAS) in this study proved to bear high accuracy results, with a validation dice loss of −0.584, the urban open spaces (UOS) mapping appeared to present a much more challenging problem and yielded a validation dice loss of −0.3274 in the best trained model
Summary
Urban areas cover 7.6% of the global land mass, approximately half the size of the European Union [1], and already hold the majority of the world’s population [2], which is expected to increase by 10% until 2030, from a current 7.7 billion to 8.5 billion [3].As urbanized areas continue to expand rapidly across all continents, sustainable and successful management of urban growth is essential at the local, national, and even international level. Development Goal 11 (SDG 11), as part of the 2030 UN Sustainable Development Agenda, to “make cities and human settlements inclusive, safe, resilient and sustainable”, indicating that there is currently a universal realization and necessity for actions and measures that could improve the quality of urban environments. Urban open spaces can provide multiple material and non-material benefits to city inhabitants through their environmental and social functions. They can improve the environmental quality of the city and bring positive contributions to people’s quality of life [4]. Urban open spaces can provide multiple benefits to society, enhance the social life and mental/physical heath of city dwellers, increase the attractiveness of cities through their aesthetic appeal, recreational and historical values, mitigate urban heat effects and
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