Abstract:In the context of the United Nations' "Agenda 2030 for Sustainable Development" and the presented Sustainable Development Goals (SDGs), the process of developing and agreeing on indicators to monitor the SDGs implementation becomes fundamental. In this paper, we identify indicators for the sustainable development of cities that have the greatest potential for their underlying data to be measured by means of remote sensing. We first identified existing indicators, which are derived from the International Standard ISO 37120, "Indicators for city services and quality of life", as being partly or fully measured by the use of remote sensing, and then presented these indicators to remote sensing experts in an assessment procedure. We then investigated Multi-Criteria Decision-Making (MCDM) weighting methods to identify the most relevant quality of life indicators that can be captured by means of remote sensing techniques. We assess the remote sensing experts' knowledge in the context of Decision Support Systems (DSS), and by means of both a questionnaire-based approach and a pairwise comparison approach. The approaches are compared with each other regarding their complexity, their potentials and limitations, and the respectively identified remote sensing based indicators. We identified three indicators related to surface characteristics as having the highest remote sensing potential. When contrasted to the results of the pairwise comparison, the questionnaire-based approach revealed high usability and confirmability. In the end, this approach enables cities' administrations to decide which indicators they want to cover by means of remote sensing, depending on the capacities of their departments.
This paper illustrates a CUDA GPU-based concept to accelerate the computationally intensive calculations of performing spatially-explicit uncertainty and sensitivity analysis in multi-criteria decision-making models. Uncertainty and sensitivity analysis is a two-step approach to validating the robustness of spatial-and non-spatial model solutions. The uncertainty analysis quantifies the variability of model outcomes, while the sensitivity analysis accounts for the contributions of model inputs to the overall model output variability. The proposed solution is applicable for large-scale spatial problems that incorporate millions of alternatives and hundreds of thousands of simulation runs. Furthermore, this GPU-based concept represents a low-cost approach in comparison to high-performance computing that incorporates super computers. Additionally, the concept allows the integration of different decision rules (e.g. simple additive weighting, ideal point, ordered weighting averaging, or analytical hierarchy process) in order to evaluate the performance of the alternatives involved. The proposed approach was tested on a landscape assessment example in order to identify the variability of the model outcomes with respect to the criteria 'Compactness', 'Mean Patch Area', 'Relief Energy' and 'Variety' that define landscape diversity.
Global sensitivity analysis, like variance-based methods for massive raster datasets, is especially computationally costly and memory-intensive, limiting its applicability for commodity cluster computing. The computational effort depends mainly on the number of model runs, the spatial, spectral, and temporal resolutions, the number of criterion maps, and the model complexity. The current Spatially-Explicit Uncertainty and Sensitivity Analysis (SEUSA) approach employs a cluster-based parallel and distributed Python–Dask solution for large-scale spatial problems, which validates and quantifies the robustness of spatial model solutions. This paper presents the design of a framework to perform SEUSA as a Service in a cloud-based environment scalable to very large raster datasets and applicable to various domains, such as landscape assessment, site selection, risk assessment, and land-use management. It incorporates an automated Kubernetes service for container virtualization, comprising a set of microservices to perform SEUSA as a Service. Implementing the proposed framework will contribute to a more robust assessment of spatial multi-criteria decision-making applications, facilitating a broader access to SEUSA by the research community and, consequently, leading to higher quality decision analysis.
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