In the framework of the United Nations (UN) 2030 Agenda for Sustainable Development and the New Urban Agenda (Habitat III), local and regional authorities require indicators at the intra-urban scale to design adequate policies in support of the Sustainable Development Goal (SDG) 11: Make cities and human settlements inclusive, safe, resilient and sustainable. Nevertheless, the current literature provides mainly national, regional and city scale indicators. Earth Observations (EO) data have been recently recognized as an essential source of information to achieve the SDG 11 targets and progress measurements with respect the SDG 11 indicators. However, the complexity of EO data handling and processing in SDGs monitoring and reporting mechanisms makes difficult a direct integration in evidence-based decision-making process.
In order to fill such gaps, this work presents the development and implementation of a set of workflows aimed at the automatic computation of some SDGs 11 indicators at intra-urban scale. A workflow is a process for generating knowledge from observation/simulation data and scientific models. The Virtual Earth Laboratory (VLab) framework (Santoro et al., 2020) was used as a cloud-based platform for sharing and facilitating the invocation of such scientific workflows from urban planners or technical employers of public administration without an extensive expertise on the EO domain. VLab implements all required orchestration functionalities to automate the technical tasks required to execute a model on different computing infrastructures, minimizing the possible interoperability requirements for both model developers and users.
A first workflow has been designed to extract essential variables devoted to the study of urban ecosystems such as the settlement map (built-up) and the population density map. The former can be obtained from a semi-automatic Sentinel-2 data classification procedure or directly by downloading the available European Settlement Map from the Copernicus Land Monitoring Service. Both maps are available at 10 m spatial resolution. Concerning the second variable, a specific workflow was developed for generating a population density map at a fine grid size (100 m X 100 m) from the ancillary population data per census area (Aquilino et al., 2020).
Additional workflows have been implemented for the computation of SDG 11.2.1 “Proportion of population that has convenient access to public transport” and SDG 11.3.1 “Ratio of land consumption rate to population growth rate.” The output maps are generated at regular grid of 100 m spatial resolution size.
Ancillary input, such as the population census data, as well as the local public transport map and additional auxiliary data need to be obtained from local authority providers. The diffusion of open data web portals results promising for the acquisition of such data for many cities.
The height of the buildings information (e.g., LiDAR data) is an optional input that, if available, allows to generate a population density map by applying the improved approach suggested by (Aquilino et al., 2021).
The workflows are validated considering the three Italian towns of Bari, Bologna and Reggio Calabria.
For reproducibility in other cities, the workflows are flexible to a wide variety of formats and geographical reference systems as input. GDAL/OGR standard formats are accepted. An advanced version of workflows is available for those expert users who intend to customize configuration parameters of the models. Besides, VLab frameworks make available a set of Web APIs designed to enable application developers to create dedicated Web applications based on models already available in VLab. By exploiting these latter functionalities, a dedicated web application will be developed as a tool for urban planner and policy-making to make EO data integration in SDGs measuring and monitoring operational for countries.
Aquilino, M.; Adamo, M.; Blonda, P.; Barbanente, A.; Tarantino, C. (2021). Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale, Remote Sensing, Special Issue “Earth Observations for Sustainable Development Goals”, 13, 2835, https://doi.org/10.3390/rs13142835
Aquilino, M.; Tarantino, C.; Adamo, M.; Barbanente, A.; Blonda, P. (2020). Earth Observation for the Implementation of Sustainable Development Goal 11 Indicators at Local Scale: Monitoring of the Migrant Population Distribution, Remote Sensing, Special Issue “EO Solutions to Support Countries Implementing the SDGs”, 12(6), 950, ISSN: 2072-4292, doi:10.3390/rs12060950
Santoro, M., Mazzetti, P., & Nativi, S. (2020). The VLab Framework: An Orchestrator Component to Support Data to Knowledge Transition. Remote Sensing, 12(11), 1795. https://doi.org/10.3390/rs12111795