Surface melt plays a vital role in impacting the polar mass balance and global sea level rise. Over the past decades, synthetic aperture radar (SAR) imagery has garnered considerable attention due to its capacity to provide high-precision and long-term information. However, the traditional SAR-based large-scale surface melt detection methods utilizing co-orbit normalization predominantly depend on reference images and the precise spatial registration to mitigate geometric distortions arising from diverse incidence angles. Consequently, both the absence of reference imagery and the movement of ice sheets and shelves present challenges to the method. In this study, we address this issue by developing a reference-free deep learning network integrating the Convolutional Block Attention Module (CBAM) into DeepLabv3+ to automatically detect surface melt and establishing the surface melt dataset based on multi-temporal Sentinel-1 SAR imagery, encompassing diverse surface conditions of the Antarctic. Our model achieves an accuracy of 95.67%, surpassing the reference-based method and an advanced deep learning-based approach by 4.23% and 4.67%, respectively. Moreover, compared to 500 m resolution UMelt product and the kilometer-level results obtained from Advanced Scatterometer (ASCAT) and Special Sensor Microwave Imager Sounder (SSMIS), our model demonstrates the capability to accurately capture the small-scale melting patterns of ice shelves with a higher spatial resolution of 40 m. Notably, our findings underscore the dispensability of reference imagery in traditional methods through the formidable information extraction capabilities of deep learning. We finally applied the proposed method to extract and analyze the spatiotemporal characteristics of surface melt on the Larsen C Ice Shelf during the 2019/2020 period. The corresponding code of this study is at https://github.com/Tangyu35/Surface-melt-detection.

Along with unprecedented urbanization in the last few decades, cities have experienced rapid social and economic transformation in China. A major challenge facing urban authorities in the immediate future is how to plan and govern cities such that they can serve as inclusive systems where everyone is enabled and empowered to fully participate in and contribute to socioeconomic life. A first step towards realizing this is to conceptualize an integrated framework that allows analysts and decision-makers to delineate, evaluate, and guide the development of these cities towards inclusiveness. In this study, we conducted a conceptual analysis of urban inclusiveness and then proposed a multidimensional framework for the evaluation of inclusive development. This is followed by the presentation of the case of Xiong'an, for which inclusive development indicators (IDIs) were selected. By comparing the state before the establishment of Xiong'an with its current development progress, and analyzing the area's management structure and policy measures, the inclusive development challenges are identified. Subsequently, suggestions are given on how to direct Xiong'an toward higher levels of inclusiveness, including offering equal access to public services and employment opportunities, preserving environmental health and sustainable use of natural resources through waste recycling, and encouraging public participation in decision-making to bring higher levels of inclusion within reach.

The Belt and Road initiative has a significant focus on infrastructure, trade, and economic development across a vast region, and it also provides significant opportunities for sustainable development. The combined pressure of climate variability, intensified use of resources, and the fragility of ecosystems make it very challenging, however, to achieve future sustainability. To develop the path in a sustainable way, it is important to have a comprehensive understanding of these issues across nations and evaluate them in a scientific and well-informed approach. In this context, the Digital Belt and Road (DBAR) program was initiated as an international venture to share expertise, knowledge, technologies, and data to demonstrate the role of Earth observation science and technology and big Earth data applications to support large-scale development. In this paper, we identify pressing challenges, present the research priorities and foci of the DBAR program, and propose solutions where big Earth data can make significant contributions. This paper calls for further joint actions and collaboration to build a digital silk road in support of sustainable development at national, regional and global levels.