Latest news with #TheHongKongPolytechnicUniversity
The Sun
11-06-2025
- Science
- The Sun
PolyU develops novel multi-modal agent to facilitate long video understanding by AI, accelerating development of generative AI-assisted video analysis
HONG KONG SAR - Media OutReach Newswire - 10 June 2025 - While Artificial Intelligence (AI) technology is evolving rapidly, AI models still struggle with understanding long videos. A research team from The Hong Kong Polytechnic University (PolyU) has developed a novel video-language agent, VideoMind, that enables AI models to perform long video reasoning and question-answering tasks by emulating humans' way of thinking. The VideoMind framework incorporates an innovative Chain-of-Low-Rank Adaptation (LoRA) strategy to reduce the demand for computational resources and power, advancing the application of generative AI in video analysis. The findings have been submitted to the world-leading AI conferences. Videos, especially those longer than 15 minutes, carry information that unfolds over time, such as the sequence of events, causality, coherence and scene transitions. To understand the video content, AI models therefore need not only to identify the objects present, but also take into account how they change throughout the video. As visuals in videos occupy a large number of tokens, video understanding requires vast amounts of computing capacity and memory, making it difficult for AI models to process long videos. Prof. Changwen CHEN, Interim Dean of the PolyU Faculty of Computer and Mathematical Sciences and Chair Professor of Visual Computing, and his team have achieved a breakthrough in research on long video reasoning by AI. In designing VideoMind, they made reference to a human-like process of video understanding, and introduced a role-based workflow. The four roles included in the framework are: the Planner, to coordinate all other roles for each query; the Grounder, to localise and retrieve relevant moments; the Verifier, to validate the information accuracy of the retrieved moments and select the most reliable one; and the Answerer, to generate the query-aware answer. This progressive approach to video understanding helps address the challenge of temporal-grounded reasoning that most AI models face. Another core innovation of the VideoMind framework lies in its adoption of a Chain-of-LoRA strategy. LoRA is a finetuning technique emerged in recent years. It adapts AI models for specific uses without performing full-parameter retraining. The innovative chain-of-LoRA strategy pioneered by the team involves applying four lightweight LoRA adapters in a unified model, each of which is designed for calling a specific role. With this strategy, the model can dynamically activate role-specific LoRA adapters during inference via self-calling to seamlessly switch among these roles, eliminating the need and cost of deploying multiple models while enhancing the efficiency and flexibility of the single model. VideoMind is open source on GitHub and Huggingface. Details of the experiments conducted to evaluate its effectiveness in temporal-grounded video understanding across 14 diverse benchmarks are also available. Comparing VideoMind with some state-of-the-art AI models, including GPT-4o and Gemini 1.5 Pro, the researchers found that the grounding accuracy of VideoMind outperformed all competitors in challenging tasks involving videos with an average duration of 27 minutes. Notably, the team included two versions of VideoMind in the experiments: one with a smaller, 2 billion (2B) parameter model, and another with a bigger, 7 billion (7B) parameter model. The results showed that, even at the 2B size, VideoMind still yielded performance comparable with many of the other 7B size models. Prof. Chen said, 'Humans switch among different thinking modes when understanding videos: breaking down tasks, identifying relevant moments, revisiting these to confirm details and synthesising their observations into coherent answers. The process is very efficient with the human brain using only about 25 watts of power, which is about a million times lower than that of a supercomputer with equivalent computing power. Inspired by this, we designed the role-based workflow that allows AI to understand videos like human, while leveraging the chain-of-LoRA strategy to minimise the need for computing power and memory in this process.' AI is at the core of global technological development. The advancement of AI models is however constrained by insufficient computing power and excessive power consumption. Built upon a unified, open-source model Qwen2-VL and augmented with additional optimisation tools, the VideoMind framework has lowered the technological cost and the threshold for deployment, offering a feasible solution to the bottleneck of reducing power consumption in AI models. Prof. Chen added, 'VideoMind not only overcomes the performance limitations of AI models in video processing, but also serves as a modular, scalable and interpretable multimodal reasoning framework. We envision that it will expand the application of generative AI to various areas, such as intelligent surveillance, sports and entertainment video analysis, video search engines and more.'
The Sun
09-06-2025
- Science
- The Sun
PolyU-led research reveals that sensory and motor inputs help large language models represent complex concepts
HONG KONG SAR - Media OutReach Newswire - 9 June 2025 - Can one truly understand what 'flower' means without smelling a rose, touching a daisy or walking through a field of wildflowers? This question is at the core of a rich debate in philosophy and cognitive science. While embodied cognition theorists argue that physical, sensory experience is essential to concept formation, studies of the rapidly evolving large language models (LLMs) suggest that language alone can build deep, meaningful representations of the world. By exploring the similarities between LLMs and human representations, researchers at The Hong Kong Polytechnic University (PolyU) and their collaborators have shed new light on the extent to which language alone can shape the formation and learning of complex conceptual knowledge. Their findings also revealed how the use of sensory input for grounding or embodiment – connecting abstract with concrete concepts during learning – affects the ability of LLMs to understand complex concepts and form human-like representations. The study, in collaboration with scholars from Ohio State University, Princeton University and City University of New York, was recently published in Nature Human Behaviour. Led by Prof. LI Ping, Sin Wai Kin Foundation Professor in Humanities and Technology, Dean of the PolyU Faculty of Humanities and Associate Director of the PolyU-Hangzhou Technology and Innovation Research Institute, the research team selected conceptual word ratings produced by state-of-the-art LLMs, namely ChatGPT (GPT-3.5, GPT-4) and Google LLMs (PaLM and Gemini). They compared them with human-generated word ratings of around 4,500 words across non-sensorimotor (e.g., valence, concreteness, imageability), sensory (e.g., visual, olfactory, auditory) and motor domains (e.g., foot/leg, mouth/throat) from the highly reliable and validated Glasgow Norms and Lancaster Norms datasets. The research team first compared pairs of data from individual humans and individual LLM runs to discover the similarity between word ratings across each dimension in the three domains, using results from human-human pairs as the benchmark. This approach could, for instance, highlight to what extent humans and LLMs agree that certain concepts are more concrete than others. However, such analyses might overlook how multiple dimensions jointly contribute to the overall representation of a word. For example, the word pair 'pasta' and 'roses' might receive equally high olfactory ratings, but 'pasta' is in fact more similar to 'noodles' than to 'roses' when considering appearance and taste. The team therefore conducted representational similarity analysis of each word as a vector along multiple attributes of non-sensorimotor, sensory and motor dimensions for a more complete comparison between humans and LLMs. The representational similarity analyses revealed that word representations produced by the LLMs were most similar to human representations in the non-sensorimotor domain, less similar for words in sensory domain and most dissimilar for words in motor domain. This highlights LLM limitations in fully capturing humans' conceptual understanding. Non-sensorimotor concepts are understood well but LLMs fall short when representing concepts involving sensory information like visual appearance and taste, and body movement. Motor concepts, which are less described in language and rely heavily on embodied experiences, are even more challenging to LLMs than sensory concepts like colour, which can be learned from textual data. In light of the findings, the researchers examined whether grounding would improve the LLMs' performance. They compared the performance of more grounded LLMs trained on both language and visual input (GPT-4, Gemini) with that of LLMs trained on language alone (GPT-3.5, PaLM). They discovered that the more grounded models incorporating visual input exhibited a much higher similarity with human representations. Prof. Li Ping said, 'The availability of both LLMs trained on language alone and those trained on language and visual input, such as images and videos, provides a unique setting for research on how sensory input affects human conceptualisation. Our study exemplifies the potential benefits of multimodal learning, a human ability to simultaneously integrate information from multiple dimensions in the learning and formation of concepts and knowledge in general. Incorporating multimodal information processing in LLMs can potentially lead to a more human-like representation and more efficient human-like performance in LLMs in the future.' Interestingly, this finding is also consistent with those of previous human studies indicating the representational transfer. Humans acquire object-shape knowledge through both visual and tactile experiences, with seeing and touching objects activating the same regions in human brains. The researchers pointed out that – as in humans – multimodal LLMs may use multiple types of input to merge or transfer representations embedded in a continuous, high-dimensional space. Prof. Li added, 'The smooth, continuous structure of embedding space in LLMs may underlie our observation that knowledge derived from one modality could transfer to other related modalities. This could explain why congenitally blind and normally sighted people can have similar representations in some areas. Current limits in LLMs are clear in this respect'. Ultimately, the researchers envision a future in which LLMs are equipped with grounded sensory input, for example, through humanoid robotics, allowing them to actively interpret the physical world and act accordingly. Prof. Li said, 'These advances may enable LLMs to fully capture embodied representations that mirror the complexity and richness of human cognition, and a rose in LLM's representation will then be indistinguishable from that of humans.'
The Sun
09-06-2025
- Science
- The Sun
New Research Reveals How Language Models Use Sensory and Motor Inputs to Represent Complex Concepts
HONG KONG SAR - Media OutReach Newswire - 9 June 2025 - Can one truly understand what 'flower' means without smelling a rose, touching a daisy or walking through a field of wildflowers? This question is at the core of a rich debate in philosophy and cognitive science. While embodied cognition theorists argue that physical, sensory experience is essential to concept formation, studies of the rapidly evolving large language models (LLMs) suggest that language alone can build deep, meaningful representations of the world. By exploring the similarities between LLMs and human representations, researchers at The Hong Kong Polytechnic University (PolyU) and their collaborators have shed new light on the extent to which language alone can shape the formation and learning of complex conceptual knowledge. Their findings also revealed how the use of sensory input for grounding or embodiment – connecting abstract with concrete concepts during learning – affects the ability of LLMs to understand complex concepts and form human-like representations. The study, in collaboration with scholars from Ohio State University, Princeton University and City University of New York, was recently published in Nature Human Behaviour. Led by Prof. LI Ping, Sin Wai Kin Foundation Professor in Humanities and Technology, Dean of the PolyU Faculty of Humanities and Associate Director of the PolyU-Hangzhou Technology and Innovation Research Institute, the research team selected conceptual word ratings produced by state-of-the-art LLMs, namely ChatGPT (GPT-3.5, GPT-4) and Google LLMs (PaLM and Gemini). They compared them with human-generated word ratings of around 4,500 words across non-sensorimotor (e.g., valence, concreteness, imageability), sensory (e.g., visual, olfactory, auditory) and motor domains (e.g., foot/leg, mouth/throat) from the highly reliable and validated Glasgow Norms and Lancaster Norms datasets. The research team first compared pairs of data from individual humans and individual LLM runs to discover the similarity between word ratings across each dimension in the three domains, using results from human-human pairs as the benchmark. This approach could, for instance, highlight to what extent humans and LLMs agree that certain concepts are more concrete than others. However, such analyses might overlook how multiple dimensions jointly contribute to the overall representation of a word. For example, the word pair 'pasta' and 'roses' might receive equally high olfactory ratings, but 'pasta' is in fact more similar to 'noodles' than to 'roses' when considering appearance and taste. The team therefore conducted representational similarity analysis of each word as a vector along multiple attributes of non-sensorimotor, sensory and motor dimensions for a more complete comparison between humans and LLMs. The representational similarity analyses revealed that word representations produced by the LLMs were most similar to human representations in the non-sensorimotor domain, less similar for words in sensory domain and most dissimilar for words in motor domain. This highlights LLM limitations in fully capturing humans' conceptual understanding. Non-sensorimotor concepts are understood well but LLMs fall short when representing concepts involving sensory information like visual appearance and taste, and body movement. Motor concepts, which are less described in language and rely heavily on embodied experiences, are even more challenging to LLMs than sensory concepts like colour, which can be learned from textual data. In light of the findings, the researchers examined whether grounding would improve the LLMs' performance. They compared the performance of more grounded LLMs trained on both language and visual input (GPT-4, Gemini) with that of LLMs trained on language alone (GPT-3.5, PaLM). They discovered that the more grounded models incorporating visual input exhibited a much higher similarity with human representations. Prof. Li Ping said, 'The availability of both LLMs trained on language alone and those trained on language and visual input, such as images and videos, provides a unique setting for research on how sensory input affects human conceptualisation. Our study exemplifies the potential benefits of multimodal learning, a human ability to simultaneously integrate information from multiple dimensions in the learning and formation of concepts and knowledge in general. Incorporating multimodal information processing in LLMs can potentially lead to a more human-like representation and more efficient human-like performance in LLMs in the future.' Interestingly, this finding is also consistent with those of previous human studies indicating the representational transfer. Humans acquire object-shape knowledge through both visual and tactile experiences, with seeing and touching objects activating the same regions in human brains. The researchers pointed out that – as in humans – multimodal LLMs may use multiple types of input to merge or transfer representations embedded in a continuous, high-dimensional space. Prof. Li added, 'The smooth, continuous structure of embedding space in LLMs may underlie our observation that knowledge derived from one modality could transfer to other related modalities. This could explain why congenitally blind and normally sighted people can have similar representations in some areas. Current limits in LLMs are clear in this respect'. Ultimately, the researchers envision a future in which LLMs are equipped with grounded sensory input, for example, through humanoid robotics, allowing them to actively interpret the physical world and act accordingly. Prof. Li said, 'These advances may enable LLMs to fully capture embodied representations that mirror the complexity and richness of human cognition, and a rose in LLM's representation will then be indistinguishable from that of humans.' Hashtag: #PolyU #HumanCognition #LargeLanguageModels #LLMs #GenerativeAI The issuer is solely responsible for the content of this announcement.
Hospitality Net
21-05-2025
- Hospitality Net
Chinese Culture Guides Millennial Green Travel
Tourism can offer unforgettable experiences, but it comes with increasing environmental costs. As sustainability takes centre stage in the industry, an emerging focus is on how travellers themselves can make a difference through pro-environmental behaviour (PEB). To date, however, the interplay of personal and cultural values in shaping such behaviour remains underexplored. Seeking to fill this gap, Professor Dori Davari and Professor Seongseop (Sam) Kim of the School of Hotel and Tourism Management (SHTM) at The Hong Kong Polytechnic University (PolyU), working with a co-author, targeted Chinese millennials, a major consumer group known for valuing luxury and self-expression while showing growing environmental concern. This demographic offered a unique lens through which to investigate the influence of values on tourists' PEB, yielding guidance for fostering sustainable tourism practices amongst one of the world's most influential populations. Tourism places enormous stress on the environment. It consumes huge volumes of natural resources, contributes as much as 5% of the world's greenhouse gas emissions, and causes overcrowding, traffic congestion and damage to local communities. No wonder, then, that sustainable development has become a major focus of almost all stakeholders in the tourism industry. An effective strategy for promoting sustainable tourism is to encourage and engage customers in pro-environmental behaviour , say the researchers. Such behaviour is consciously intended to minimise the negative impact of one's actions on the natural and built environment. For example, tourists who exhibit PEB might choose to travel by train rather than aeroplane, stay at hotels that prioritise sustainability and buy souvenirs from local artisans rather than mass-produced goods. Given its potential benefits, promoting travellers' PEB is a critical task for policy makers and destination managers. But what factors can increase tourists' awareness of the need for sustainability and willingness to behave in an environmentally friendly manner while enjoying their travels? Pro-environmental behaviour is a multifaceted phenomenon influenced by both individual and cultural values , say the authors. For example, research has shown that hedonic values – individual values that emphasise pleasure, enjoyment and delight over functional (utilitarian) benefits – may either promote or inhibit PEB. Meanwhile, the cultural value of femininity (vs masculinity) is associated with greater care for the environment and nature. However, the authors noted an important research gap: no studies to date had simultaneously examined the influence of individual values and cultural values on PEB. To fill this gap, they selected an important and understudied population for analysis: Chinese millennials, or the so-called 'Generation Y', who currently make up more than 27.3% of the Chinese population. Interestingly , the researchers explain, there is empirical evidence that Chinese millennials have different consumption behaviours. They tend to value self-expression and regard luxury goods and brands as symbols of success, while at the same time exhibiting increasing concern about the environment. Therefore , the researchers note, examining their pro-environmental behaviour in the tourism context could unveil new mysteries . Focusing on tourists from China's Generation Y, the authors set out to achieve three objectives. First, they aimed to reveal the influence of both individual and cultural values on PEB. Second, they asked how the relationship between values and PEB is mediated by a preference for sustainable tourism. Finally, they measured the impact of environmental concern on the strength of the relationship between values and PEB. Individual values were considered in terms of hedonic and utilitarian values, which guide consumers to make decisions based on pleasure and functionality, respectively. Both hedonic and utilitarian values are necessary conditions for fostering a high level of pro-environmental attitude and behaviour , the researchers proposed. That is, tourists who focus on pleasure are more likely than not to choose sustainable travel options and adopt eco-friendly behaviours during their trips. Similarly, those who value practicality and efficiency are inclined towards sustainable tourism. Meanwhile, cultural values were defined using Hofstede's (2011) five-dimensional model. The authors hypothesised that sustainable travel and PEB are inhibited by four of the cultural values identified by Hofstede – high power distance, masculinity, individualism and uncertainty avoidance – and promoted by the fifth, namely long-term orientation. To test their hypotheses, the authors conducted a survey and subjected the results to rigorous data analysis. The population for this study was 350 million Chinese millennials, they explain. Ultimately, 429 questionnaire responses were analysed, representing the views of Chinese members of Generation Y who were aged between 20 and 40, understood how their behaviour could impact the environment and were willing to participate in tourism activities. The findings were instructive. This paper revealed the importance of the cultural values of Chinese millennials in promoting both pro-environmental behaviour and a preference for sustainable tourism , report the researchers. As such, their findings provide strong support for previous research that has recommended exploring and testing cultural values as predictors of PEB. Specifically, collectivism and a long-term orientation had a positive impact on the respondents' pro-environmental attitudes, which was evident in both their preferences and their behaviours. Conversely, high levels of power distance and tolerance for uncertainty resulted in less pro-environmental attitudes. Simultaneously, masculinity, as expressed in their tendency to obtain more profit in advance, was harmful, explain the authors. The results also shed light on the influence of individual values on Chinese millennials' preference for sustainable tourism and PEB. This is important, the researchers tell us, because culture must be understood at the individual level to enable the development of effective marketing tactics for promoting sustainable tourism and efficient pro-environmental behaviour . Hedonic and utilitarian values not only stimulated PEB amongst tourists but also intensified their preference for sustainable tourism. The researchers also reported the novel finding that a preference for sustainable tourism acted as an intermediary mechanism linking cultural values and behaviour. Without a genuine awareness and internal appreciation of environmental issues and the importance of sustainability , they note, individuals are unlikely to prioritise a preference for sustainable tourism . These findings have timely practical implications in an age of overtourism. Understanding Chinese millennials' pro-environmental behaviour considering cultural and personal values leads to developing innovative strategies to attract them to sustainable tourism, the authors tell us. This is crucial given that most of Generation Y are parents and will continue to exert a major influence on China's economy as the main consumers for the next few decades. Based on the findings, the researchers advise destination marketing organisations (DMOs) to promote more masculine, less power-distant, less individualistic, more uncertainty-avoiding, and more long-term-oriented messages to attract Chinese millennial tourists . To encourage this pivotal population to help save the environment, DMOs should involve them in activities such as co-creating groups on social media or having an interactive presence on Chinese-dominated social media platforms . Millennials are also role models for the next generation, especially their own children. Governments and DMOs should thus emphasise the pleasure and enjoyment (hedonic values) to be gained from family pro-environmental travel experiences. Hosting eco-friendly events at schools, holding sustainability-specific festivals and celebrating Earth Day are examples of activities in which both Chinese millennial parents and their children can be involved. With such approaches , the researchers say, policymakers can target younger generations to foster pro-environmental attitudes in tourism from an early age and beyond . Dori Davari, Saeid Nosrati & Seongseop (Sam) Kim. (2024). Do Cultural and Individual Values Influence Sustainable Tourism and Pro-Environmental Behavior? Focusing on Chinese Millennials. Journal of Travel & Tourism Marketing, Vol. 41, Issue 4, 559–577. About PolyU School of Hotel and Tourism Management For more than four decades, the School of Hotel and Tourism Management (SHTM) of The Hong Kong Polytechnic University has refined a distinctive vision of hospitality and tourism education and become a world-leading hotel and tourism school. Ranked No. 1 in the world in the "Hospitality and Tourism Management" category in ShanghaiRanking's Global Ranking of Academic Subjects 2024 for the eighth consecutive year; placed No. 1 globally in the "Commerce, Management, Tourism and Services" category in the University Ranking by Academic Performance in 2023/2024 for seven years in a row; rated No. 1 in the world in the "Hospitality, Leisure, Sport & Tourism" subject area by the CWUR Rankings by Subject 2017; and ranked No. 1 in Asia in the "Hospitality and Leisure Management" subject area in the QS World University Rankings by Subject 2025, the SHTM is a symbol of excellence in the field, exemplifying its motto of Leading Hospitality and Tourism. The School is driven by the need to serve its industry and academic communities through the advancement of education and dissemination of knowledge. With a strong international team of over 90 faculty members from 21 countries and regions around the world, the SHTM offers programmes at levels ranging from undergraduate to doctoral degrees. Through Hotel ICON, the School's groundbreaking teaching and research hotel and a vital aspect of its paradigm-shifting approach to hospitality and tourism education, the SHTM is advancing teaching, learning and research, and inspiring a new generation of passionate, pioneering professionals to take their positions as leaders in the hospitality and tourism industry. Website: Pauline Ngan Senior Marketing Manager +852 3400 2634 Hong Kong PolyU
Business Mayor
14-05-2025
- Science
- Business Mayor
Sharp depletion in soil moisture drives land water to flow into oceans, contributing to sea level rise
The increasing frequency of once-in-a-decade agricultural and ecological drought has underscored the urgency of studying hydrological changes. A research team from the Department of Land Surveying and Geo-informatics of The Hong Kong Polytechnic University (PolyU) has collaborated with international experts to analyse the estimated changes in land water storage over the past 40 years by utilising space geodetic observation technology and global hydrological change data. This innovative method has revealed a rapid depletion in global soil moisture, resulting in a significant amount of water flowing into the oceans, leading to a rise in sea levels. The research provides new insights into the driving factors behind the alarming reduction in terrestrial water storage and rise in sea levels. The findings have been published in the international journal Science . Since polar motion reflects mass redistribution within the Earth system, integrating models and observations across the atmosphere, hydrosphere and lithosphere is crucial. However, previous challenges in measuring terrestrial water storage, particularly groundwater and root zone soil moisture, limited understanding of hydrological depletion at continental scales. Prof. Jianli CHEN, Professor of the PolyU Department of Land Surveying and Geo-informatics and core member of the Research Institute for Land and Space and the international team employed satellite altimetry and gravity missions, including the Gravity Recovery and Satellite Experiment (GRACE), and GRACE Follow-On, to enable continental-scale observations of terrestrial water storage variations. By integrating this with global mean sea levels and polar motion data, the team has explored terrestrial water storage depletion patterns. Notably, this study introduced novel methods for estimating global soil moisture, which improves the accuracy of continental and global scale modeling to enable a more effective understanding of soil moisture variations under climate change. The melting of Greenland's ice sheet is recognised as the largest single contributor to the rise in global sea levels, adding approximately 0.8mm annually. This study reveals that between 2000 and 2002, the global terrestrial water storage significantly declined, with a total of 1,614 billion tons of water lost to the oceans, which is twice as much as resulting from the current melting of Greenland ice, and equivalent to a 4.5mm rise in sea levels. Since then, the rapid loss of terrestrial water storage has been followed by a more gradual but continuous depletion, with no signs of recovery. In addition, compared to the period from 1979 to 1999, a notable decline in global average soil moisture was observed from 2003 to 2021. Between 2003 and 2011, the Earth's pole shifted 58cm toward 93° East Longitude, demonstrating that the continued decline in soil moisture is leading to a reduction in terrestrial water storage. The team also pointed out that precipitation deficits and stable evapotranspiration caused by global warming, changing rainfall patterns and increasing ocean temperatures are likely the key factors for the abrupt decline in terrestrial water storage. The ERA5-Land soil moisture data of the European Centre for Medium-Range Weather Forecasts' corroborates these findings, showing substantial terrestrial water storage losses in Africa, Asia, Europe, and South America. In Asia and Europe, the affected areas expanded from northeastern Asia and eastern Europe to broader regions across East and Central Asia, as well as Central Europe, following the sharp water storage depletion observed between 2000 and 2002. Read More Evidence of geothermal activity within icy dwarf planets With increasing agricultural irrigation in regions such as northeast China and the western United States, and global greening, soil moisture may further diminish in semi-arid areas with intensive agriculture and high levels of greening. The team suggests the need for improved land surface models which consider these factors for a more comprehensive understanding of long- term changes in terrestrial water storage. Prof. Jianli Chen said, 'Sea level change and Earth rotation serve as indicators of large-scale mass changes in the Earth system. Accurately measured sea level change and variation in Earth rotation provide a unique tool for monitoring large-scale mass changes in the global water cycle. By integrating multiple modern space geodetic observations, it enables comprehensive analysis of the driving factors behind changes in terrestrial water storage and sea level rise. This, in turn, provides reliable data for climate and Earth system science experts to further investigate drought issues, aiding authorities in formulating water resource management and climate change mitigation strategies to address new challenges posed by climate change.'
