In the context of the booming construction of smart cities, multi-source data fusion and analysis algorithms play a key role in optimizing real estate management and improving urban efficiency. In this review, we comprehensively and systematically review the relevant algorithms, covering the types, characteristics, fusion techniques, analysis algorithms, and their synergies of multi-source data. We found that multi-source data, including sensors, social media, citizen feedback, and GIS data, face challenges such as data quality and privacy security when being fused. Data fusion algorithms are diverse and have their own advantages and disadvantages. Data analysis algorithms help urban management in areas such as spatial analysis and deep learning. Algorithm collaboration can improve decision-making accuracy and efficiency and promote the rational allocation of urban resources. In the future, algorithm development will focus on data quality, real-time, deep mining, interdisciplinary research, privacy protection, and collaborative application expansion, providing strong support for the sustainable development of smart cities.

Exploring the urban spatial pattern and expansion characteristics of African countries under shared socioeconomic pathways (SSPs) is crucial to optimizing urban development in Africa and ensuring ecological sustainability. We use land and socioeconomic panel data and the least squares dummy variable regression method to predict the urban land increment in African countries from 2030 to 2060, we use the FLUS model to simulate the urban spatial layout in 2060, and we analyze from the perspective of the relationship between population density and urban expansion. The results show that the urban space of African countries will show a significant expansion trend from 2020 to 2060, with stronger growth under the SSP1 and SSP5 scenarios and relatively weaker growth under the SSP3 scenario; the urban land expansion patterns of different countries under different SSP scenarios are significantly different, and countries with rapid urbanization and economic growth are mostly urban patch agglomeration and extended expansion, while urban patches are relatively evenly distributed; a large number of cities in Africa show specific expansion patterns, with large cities mostly showing loose expansion and small- and medium-sized cities mostly showing compact expansion; and cities in different regions such as North Africa and sub-Saharan Africa have their own expansion characteristics in terms of population density and urban form. Our research provides important data support and inspiration for promoting the rational development of African cities and enhancing regional ecological resilience.

China’s Yellow (Bohai) Sea bird habitat is an important ecological region. Its unique ecology and challenges provide rich resources for research and study. Our course design concept is supported by AI technology, and improves students’ abilities through innovative functions such as dynamic data support, personalized learning paths, immersive research and study experience, and diversified evaluation mechanisms. The course content revolves around the “human–land coordination concept”, including pre-trip thinking, research and study during the trip, and post-trip exhibition learning, covering regional cognition, remote sensing image analysis, field investigation, and protection plan display activities. ERNIE Bot participates in optimizing the learning path throughout the process. The course evaluation system starts from the three dimensions of “land to people”, “people to land”, and the “coordination of the human–land relationship”, adopts processes and final evaluation, and uses ERNIE Bot to achieve real-time monitoring, data analysis, personalized reports, and dynamic feedback, improving the objectivity and efficiency of evaluation, and helping students and teachers optimize learning and teaching. However, AI has limitations in geographical research and study, such as insufficient technical adaptability, the influence of students’ abilities and habits, and the adaptation of teachers’ role changes. To this end, optimization strategies such as improving data quality and technical platforms, strengthening student technical training, enhancing teachers’ AI application capabilities, and enriching AI functions and teaching scenarios are proposed to enhance the application effect of AI in geographical research and promote innovation in educational models and student capacity building.

The coastal region of Jiangsu Province, a critical ecological and economic zone in eastern China, faces significant spatial vulnerability due to combined natural and anthropogenic pressures. This study employs a multi-factorial assessment framework integrating Geographic Information System (GIS) tools to analyze vulnerability across three dimensions: basic conditions, ecological environment, and human activities. Key findings reveal distinct spatial disparities in vulnerability, quantified through a vulnerability index (VI) ranging from 1 (highest vulnerability) to 5 (lowest).Statistical results indicate that 5.49% of the coastal zone is classified as high vulnerability (VI = 1) and 21.15% as relatively high (VI = 2), primarily concentrated in inland areas such as Ganyu, Lianyun, and Binhai districts. Conversely, 39.31% of the region exhibits moderate vulnerability (VI = 3), while 32.04% and 2.02% are categorized as relatively low (VI = 4) and low vulnerability (VI = 5), respectively. Notably, inland regions consistently show higher vulnerability compared to coastal areas, contradicting initial assumptions of uniform north-south gradients. Spatial analysis further demonstrates that central coastal zones (e.g., Dafeng, Dongtai) exhibit lower vulnerability due to balanced human-environment interactions, whereas northern and southern inland regions face intensified pressures from urbanization, industrialization, and land subsidence.To address these disparities, this study proposes targeted ecological restoration strategies, including: (1) infrastructure reinforcement in high-vulnerability inland areas; (2) wetland conservation and reforestation in ecologically sensitive zones; (3) sustainable land-use planning to mitigate human-induced degradation; and (4) community-based adaptive management frameworks. These recommendations aim to enhance resilience while balancing socioeconomic development and environmental protection. This research provides a data-driven foundation for policymakers to prioritize interventions in Jiangsu’s coastal zone and offers a replicable methodology for vulnerability assessments in other deltaic regions.

Housing vacancy rate is a key indicator for evaluating urban sustainable development. Due to rapid urbanization, population outflow and insufficient industrial support, the housing vacancy problem is particularly prominent in China’s underdeveloped regions. However, the lack of official data and the limitations of traditional survey methods restrict in-depth research. This study proposes a vacancy rate estimation method based on Baidu Street View residential exterior images and deep learning technology. Taking Nanning, Guangxi as a case study, an automatic discrimination model for residential vacancy status is constructed by identifying visual clues such as window occlusion, balcony debris accumulation, and facade maintenance status. The study first uses Baidu Street View API to collect images of residential communities in Nanning. After manual annotation and field verification, a labeled dataset is constructed. A pre-trained deep learning model (ResNet50) is applied to estimate the vacancy rate of the community after fine-tuning with labeled street view images of Nanning’s residential communities. GIS spatial analysis is combined to reveal the spatial distribution pattern and influencing factors of the vacancy rate. The results show that street view images can effectively capture vacancy characteristics that are difficult to identify with traditional remote sensing and indirect indicators, providing a refined data source and method innovation for housing vacancy research in underdeveloped regions. The study further found that the residential vacancy rate in Nanning showed significant spatial differentiation, and the vacancy driving mechanism in the old urban area and the emerging area was significantly different. This study expands the application boundaries of computer vision in urban research and fills the research gap on vacancy issues in underdeveloped areas. Its results can provide a scientific basis for the government to optimize housing planning, developers to make rational investments, and residents to make housing purchase decisions, thus helping to improve urban sustainable development and governance capabilities.

This study evaluates the competitiveness and sustainable development prospects of French-speaking African countries by constructing a comprehensive framework integrating the TOPSIS method and adaptive LASSO algorithm. Using multivariate data from sources such as the World Bank, 30 indicators covering core, basic, and auxiliary competitiveness were selected to quantitatively analyze the competitiveness of 26 French-speaking African countries. Results show that their comprehensive competitiveness exhibits spatial patterns of “high in the north and south, low in the east and west” and “high in coastal areas, low in inland areas”. Algeria, Morocco, and six other countries demonstrate high competitiveness, while Central African countries generally show low competitiveness. The adaptive LASSO algorithm identifies three key influencing factors, including the proportion of R&D expenditure to GDP, high-tech exports, and total reserves, as well as five secondary key factors, including the number of patent applications and total number of domestic listed companies, revealing that scientific and technological investment, financial strength, and innovation transformation capabilities are core constraints. Based on these findings, sustainable development strategies are proposed, such as strengthening scientific and technological research and development and innovation transformation, optimizing financial reserves and capital markets, and promoting China–Africa collaborative cooperation, providing decision-making references for competitiveness improvement and regional cooperation of French-speaking African countries under the background of the “Belt and Road Initiative”.