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The current applications in the energy sector are based largely on fossil fuels which release greenhouse gas emissions to the atmosphere. To face the issue of global warming, the energy sector has to transfer to and develop sustainable energy solutions that do not release carbon emissions. This is one of the primary motivators for the SDEWES conference as well as for this review, and previous ones, examining the most recent works based on sustainable and green energy production in such fields. The 17th Conference on the Sustainable Development of Energy, Water, and Environment Systems (SDEWES) was held on 6–10 November 2022 in Paphos, Cyprus. The SDEWES conference aims at solving complex and ongoing concerns that approach a long-term perspective and supporting innovative solutions and continuous monitoring and evaluation. This review paper aims at collecting the main presented papers focused on the following hot topics: low-carbon technologies based on renewable and clean-energy systems, including mainly biomass, solar, and wind energy applications; energy storage systems; hydrogen-based systems; energy-saving strategies in buildings; and the adoption of smart management strategies using renewable energy systems. These topics are investigated in order to propose solutions to address the issues of climate change, water scarcity, and energy saving. From the analyzed works, we note that some key issues for sustainable development remain to be further addressed: such as novel and advanced energy storage systems, green hydrogen production, novel low-temperature district heating and cooling networks, novel solar technologies for the simultaneous production of power and high temperature heat, solar desalination for hydrogen production systems, and agrivoltaic systems for the production of power and food. [ABSTRACT FROM AUTHOR]

The aim of image-based localization (IBL) is to localize the real location of query image by matching reference image in database with GNSS-tags. Popular methods related to IBL commonly use street-level images, which have high value in practical application. Using street-level image to tackle IBL task has the primary challenges: existing works have not made targeted optimization for urban IBL tasks. Besides, the matching result is over-reliant on the quality of image features. Methods should address their practicality and robustness in engineering application, under metropolitan-scale. In response to these, this paper made following contributions: firstly, given the critical of buildings in distinguishing urban scenes, we contribute a feature called Building-Aware Feature (BAF). Secondly, in view of negative influence of complex urban scenes in retrieval process, we propose a retrieval method called Patch-Region Retrieval (PRR). To prove the effectiveness of BAF and PRR, we established an image-based localization experimental framework. Experiments prove that BAF can retain the feature points that fall on the building, and selectively lessen the feature points that fall on other things. While this effectively compresses the storage amount of feature index, we can also improve recall of localization results; implemented in the stage of geometric verification, PRR compares matching results of regional features and selects the best ranking as final result. PRR can enhance effectiveness of patch-regional feature. In addition, we fully confirmed the superiority of our proposed methods through a metropolitan-scale street-level image dataset. [ABSTRACT FROM AUTHOR]