Abstract
Recent advances in Earth Observation (EO) placed Citizen Science (CS) in the highest position, declaring their essential provision of information in every discipline that serves the SDGs, and the 2050 climate neutrality targets. However, so far, none of the published literature reviews has investigated the models and tools that assimilate these data sources. Following this gap of knowledge, we synthesised this scoping systematic literature review (SSLR) with a will to cover this limitation and highlight the benefits and the future directions that remain uncovered. Adopting the SSLR guidelines, a double and two-level screening hybrid process found 66 articles to meet the eligibility criteria, presenting methods, where data were fused and evaluated regarding their performance, scalability level and computational efficiency. Subsequent reference is given on EO-data, their corresponding conversions, the citizens’ participation digital tools, and Data Fusion (DF) models that are predominately exploited. Preliminary results showcased a preference in the multispectral satellite sensors, with the microwave sensors to be used as a supplementary data source. Approaches such as the “brute-force approach” and the super-resolution models indicate an effective way to overcome the spatio-temporal gaps and the so far reliance on commercial satellite sensors. Passive crowdsensing observations are foreseen to gain a greater audience as, described in, most cases as a low-cost and easily applicable solution even in the unprecedented COVID-19 pandemic. Immersive platforms and decentralised systems should have a vital role in citizens’ engagement and training process. Reviewing the DF models, the majority of the selected articles followed a data-driven method with the traditional algorithms to still hold significant attention. An exception is revealed in the smaller-scale studies, which showed a preference for deep learning models. Several studies enhanced their methods with the active-, and transfer-learning approaches, constructing a scalable model. In the end, we strongly support that the interaction with citizens is of paramount importance to achieve a climate-neutral Earth.
Highlights
During the last few decades, rapid and aggressive changes to the global climate have placed citizens in the spotlight, as the main drivers of Climate Change (CC) [1]
We attempted to provide an overview of the data fusion models that assimilated the remotely sensed and crowdsourced data streams, both of which have emerged as promising, scalable and low-cost ways to provide insights in many domains
We carefully reviewed the literature, following the guidelines of the systematic scoping review, and emphasised the strengths and challenges of identified methods overcoming concerns related to data quality, data sparsity, biases related to human cognitive level, and big data related obstacles
Summary
During the last few decades, rapid and aggressive changes to the global climate have placed citizens in the spotlight, as the main drivers of Climate Change (CC) [1]. Recent solutions have placed EO in the highest position of the data landscape as a cost-efficient solution that could provide more accurate estimations on the future dynamics of the human-Earth system [16]. Under this frame, various international organisations such as the Committee on Earth Observation Satellites (CEOS), the Global Climate Observing System (GCOS), and the Group on Earth Observations (GEO) were established to design and further certify the scalable and interoperable nature of EO systems [17]. Traditional methods of visual inspection and photo interpretation are still performed for the acquisition of reference data and described among researchers as a bottleneck and an unsustainable way to extract meaningful outcomes for the heterogeneous, complex, imperfect big-EO data [19]
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