Earth Science Research Research Articles

EarthCODE – ESA’s Earth Science Collaborative Open Development Environment

Abstract. Earth Observations (EO) have become crucial for advancing climate and Earth System research, enabling significant scientific discoveries. Advanced EO missions, such as ESA’s Earth Explorers and the EU's Copernicus Programme, provide vast data volumes and continuous and global observations with cutting-edge technology, a vital resource for understanding processes and interactions within Earth's sub-systems, offering critical evidence of climate change impacts on society and ecosystems. Open access to such data products has been instrumental in facilitating global scientific collaboration and innovation. Reproducibility in Earth Science is essential for validating discoveries, enabling the scientific community to trust and build upon each other’s work. Open data, coupled with open-source software, is key to achieving this reproducibility, ensuring transparency, and facilitating peer review. Addressing scientific challenges requires collaborative efforts supported by fit-for-purpose technology to enable new insights and ensuring they are FAIR (findable, accessible, interoperable, and reusable), trustable and up to date, supporting long-term climate studies and action-driven science. Several initiatives within ESA’s Earth Observation Programme are fostering FAIR and Open Science. EarthCODE is one such initiative, focusing on implementing ESA’s vision of EO Open Science and Innovation by adopting FAIR and Open principles in Earth Science activities funded by ESA’s FutureEO Programme. EarthCODE leverages existing European platform solutions for effective cloud-based analyses of EO data and higher-level geospatial products. It aims to ensure the persistence of end-to-end scientific workflows and provide means for managing open research data, including data, code, and documentation. EarthCODE aims to make Earth Science research more reproducible, transparent, and collaborative, driving progress in understanding and addressing global environmental challenges.

Accelerating Museum AI Research and Application at the UK Natural History Museum: The NHM AI Lab Programme

The United Kingdom's Natural History Museum (NHM) AI Lab Programme represents a pioneering initiative aimed at harnessing the power of artificial intelligence (AI) to bridge the gap between the museum's extensive collection and cutting-edge AI technologies. Despite its immense potential, the application of AI in museum research remains nascent (e.g., He et al. 2024), with some individual research groups pursuing independent projects without cohesive collaboration with AI experts who know or have experience in similar endeavours. Moreover, differing standards in utilising AI among researchers add complexity to the field. The NHM AI Lab Programme addresses these challenges by co-creating AI pilot projects that bring together the NHM's collection, academic researchers, and AI experts. The NHM AI Lab Programme serves as a nexus for interdisciplinary collaboration, offering expertise in AI, machine learning, data science, and software engineering to support NHM researchers. Through one-to-one consultations and collaborative research projects, the NHM AI Lab Programme facilitates the integration of innovative AI-driven technologies into streamlining digitisation workflows and enhancing Earth and Life Science research at the NHM. In less than a year since its inception, our Programme has achieved several milestones, hosting around 20 diverse projects. These include research projects such as the application of AI for the automatic detection and identification of nannofossils in chalk, the classification of ancient shark and dinosaur teeth, the prediction of mammal disease outbreaks, and the extraction of data from historical bird egg records. Additional projects focus on the automation of mineral analysis and the detection of secondary impact craters on planetary surfaces using AI. Some led to journal publications (e.g., He et al. 2024), while others streamlined NHM researchers' workflows, enhancing their processes of research and digitisation. Moreover, several initiatives have paved the way for new funding streams and collaborative ventures, as well as promising commercial prospects. Certain projects have pioneered the creation or transformation of datasets to meet AI-ready standards, such as data quality, consistency, accessibility, usability, and data governance protocols, helping to embed AI practices into NHM research. This AI Lab Programme can act as a model for other institutions addressing a similar challenge of bridging the gap between AI and their research and collections. This presentation provides insights into the establishment and operation of the NHM AI Lab Programme, shares experiences, highlights successful collaborations, discusses challenges encountered, and outlines future directions.

A new automated procedure to obtain reliable moment tensor solutions of small to moderate earthquakes (3.0 ≤ M ≤ 5.5) in the Bayesian framework

SUMMARY The complete catalogue of moment tensor (MT) solutions is essential for a wide range of research in solid earth science. However, the number of reliable MT solutions for small to moderate earthquakes (3.0 ≤ M ≤ 5.5) is limited due to uncertainties arising from data and theoretical errors. In this study, we develop a new procedure to enhance the resolvability of MT solutions and provide more reliable uncertainty estimates for these smaller to moderate earthquakes. This procedure is fully automatic and efficiently accounts for both data and theoretical errors through two sets of hybrid linear–non-linear Bayesian inversions. In the inversion process, the covariance matrix is estimated using an empirical approach: the data covariance matrix is derived from the pre-event noise and the theoretical covariance matrix is derived from the residuals of the initial solution. We conducted tests using synthetic data generated from the 3-D velocity model and interference from background seismic noise. The tests found that using a combination of the non-Toeplitz data covariance matrix and the Toeplitz theoretical covariance matrix improves the solution and its uncertainties. Test results also suggest that including a theoretical covariance matrix when analysing MT in complex tectonic regions is essential, even if we have the best 1D velocity model. The application to earthquakes in the northern region of the Banda Arc resulted in the first published Regional Moment Tensor (RMT) catalogue, containing more than three times the number of trusted solutions compared to the Global Centroid Moment Tensor (GCMT) and the Indonesian Agency for Meteorology Climatology and Geophysics Moment Tensor (BMKG-MT) catalogue. The comparison shows that the trusted solutions align well with the focal mechanism of the GCMT and BMKG-MT, as well as with the maximum horizontal stress of the World Stress Map, and tectonic conditions in the study area. The newly obtained focal mechanisms provide several key findings: (i) they confirm that the deformation in the northern and eastern parts of Seram Island is influenced by oblique intraplate convergence rather than by the subduction process; (ii) they validate the newly identified Amahai Fault with a greater number of focal mechanisms and (iii) they reveal an earthquake Mw 4.7 with the same location and source mechanism 6 yr before the 2019 Ambon-Kairatu earthquake (Mw 6.5) which occurred on a previously unidentified fault.

A new method of rock type identification based on transformer by utilizing acoustic emission.

The characterization and analysis of rock types based on acoustic emission (AE) signals have long been focal points in earth science research. However, traditional analysis methods struggle to handle the influx of big data. While signal processing methods combined with deep learning have found widespread use in various process analyses and state identification, effective feature extraction using progressive fusion technology still faces challenges in the field of intelligent rock type identification. To address this issue, our study proposes a novel framework for rock type identification based on AE and introduces a new signal identification model called 3CTNet. This model integrates convolutional neural networks (CNNs) and Transformer encoder, intelligently identifying AE of different rock fractures by establishing dependencies between adjacent positions within the data and gradually extracting advanced features. Furthermore, we experimentally compare five oversampling methods, ultimately selecting the adaptive synthetic sampling method (ADASYN) to balance the dataset and enhance the model's robustness and generalization ability. Comparison of the internal structure of our model with a series of time series processing models demonstrates the effectiveness of the proposed model structure. Experimental results showcase the high identification accuracy of the intelligent rock type identification model based on 3CTNet, with an overall identification accuracy reaching 98.780%. Our proposed method lays a solid foundation for the efficient and accurate identification of formation rock types in geological exploration and oil and gas development endeavors.

Co-Constructing “Third Spaces” for Engagement with and for Minoritized Community Groups and Environmental Scientists

The demographics of environmental and Earth scientists are not representative of the UK’s multicultural society. We sought to widen diversity and engage equitably through two related engaged research projects, “Walking the Walk” and “Landscape Stories.” This paper offers a critically reflexive account, based on the methodology of duoethnography, of how we co-constructed a “third space” with and for minoritized community groups and environmental scientists. We sought to create the conditions for inclusive leadership informed by connectedness, respect, humility and intentionality. We argue that for environmental and Earth science research to be more equitable and inclusive, members of project teams should be engaged and more representative of wider society. Following this, collective work to create third spaces requires: respect for diversity and different forms of expertise, knowledge starting points, power dynamics and esteem; a willingness to make connections across disciplines and sub-cultures, actively listening and (un)learning from different (knowledge) cultures; and a commitment to be respectful of hidden and manifest difference, exploring purpose and gaps in knowledge in more holistic ways.

Cracking the code: An evidence-based approach to teaching Python in an undergraduate earth science setting

Scientific programming has become increasingly essential for manipulating, visualizing, and interpreting the large volumes of data acquired in earth science research. Yet few discipline-specific instructional approaches have been documented and assessed for their effectiveness in equipping geoscience undergraduate students with coding skills. Here we report on an evidence-based redesign of an introductory Python programming course, taught fully remotely in 2020 in the School of Oceanography at the University of Washington. Key components included a flipped structure, activities infused with active learning, an individualized final research project, and a focus on creating an accessible learning environment. Cloud-based notebooks were used to teach fundamental Python syntax as well as functions from packages widely used in climate-related disciplines. By analyzing quantitative and qualitative data from surveys, online learning platforms, student work, assessments, and a focus group, we conclude that the instructional design facilitated learning and supported self-guided scientific inquiry. Students with less or no prior exposure to coding achieved similar success as peers with more experience, an outcome likely mediated by higher engagement with course resources. We believe that the constructivist approach to teaching introductory programming and data literacy that we present could be broadly applicable across the earth sciences and in other scientific domains.

How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences

AbstractInterpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but also seeking to elucidate the reasoning behind those predictions. The combination of predictive power and enhanced transparency makes IML a promising approach for uncovering relationships in data that may be overlooked by traditional analysis. Despite its potential, the broader implications for the field have yet to be fully appreciated. Meanwhile, the rapid proliferation of IML, still in its early stages, has been accompanied by instances of careless application. In response to these challenges, this paper focuses on how IML can effectively and appropriately aid geoscientists in advancing process understanding—areas that are often underexplored in more technical discussions of IML. Specifically, we identify pragmatic application scenarios for IML in typical geoscientific studies, such as quantifying relationships in specific contexts, generating hypotheses about potential mechanisms, and evaluating process‐based models. Moreover, we present a general and practical workflow for using IML to address specific research questions. In particular, we identify several critical and common pitfalls in the use of IML that can lead to misleading conclusions, and propose corresponding good practices. Our goal is to facilitate a broader, yet more careful and thoughtful integration of IML into Earth science research, positioning it as a valuable data science tool capable of enhancing our current understanding of the Earth system.

Improved gravity field estimation by incorporating the Tiangong Space Station and next-generation CAI satellite gravity mission

The next-generation gravity satellite mission equipped with the Cold Atom Interferometry (CAI) gradiometer has great potential for the Earth's gravity field estimation. Deploying a CAI gradiometer on the Chinese Tiangong Space Station launched for long-term Earth science research not only reduces the cost compared to a dual-satellite constellation but also enhances interdisciplinary collaboration in the Earth's gravity field detection. In this study, we conducted gravity gradient-based simulations to assess the contribution of deploying a CAI gradiometer on the Tiangong Space Station to collaboratively observe the Earth's gravity field with a polar-orbit gravity satellite. The simulation results demonstrate that whether utilizing Vyy component, three diagonal components or full components, the derived gravity field models show significant improvements within 100 degree and above 200 degree after incorporating Tiangong Space Station. In particular, the gravity field solution recovered from three diagonal components achieves the best accuracy. In the case of using diagonal components, the collaboration observation scheme effectively reduced the cumulative geoid height error by approximately 5.3cm (300 d/o). In the spatial domain, the incorporation of the Tiangong Space Station primarily impacts the estimated gravity field within the orbital coverage area of the space station, and this effect is particularly pronounced when just employing Vyy component. However, due to the limitation of angular velocity observation inaccuracy associated with the CAI gradiometer in nadir mode, there is no substantial accuracy improvement observed above 200 degree when adding gradient components.

Three-Dimensional Geological Modelling in Earth Science Research: An In-Depth Review and Perspective Analysis

This study examines the development trajectory and current trends of three-dimensional (3D) geological modelling. In recent years, due to the rising global energy demand and the increasing frequency of regional geological disasters, significant progress has been made in this field. The purpose of this study is to clarify the potential complexity of 3D geological modelling, identify persistent challenges, and propose potential avenues for improvement. The main objectives include simplifying the modelling process, improving model accuracy, integrating different data sources, and quantitatively evaluating model parameters. This study integrates global research in this field, focusing on the latest breakthroughs and applications in mineral exploration, engineering geology, geological disaster assessment, and military geosciences. For example, unmanned aerial vehicle (UAV) tilt photography technology, multisource data fusion, 3D geological modelling method based on machine learning, etc. By identifying areas for improvement and making recommendations, this work aims to provide valuable insights to guide the future development of geological modelling toward a more comprehensive and accurate “Transparent Earth”. This review underscores the global applications of 3D geological modelling, highlighting its crucial role across various sectors such as mineral exploration, the oil and gas industry, urban planning, geological hazard assessment, and geoscientific research. The review emphasizes the sector-specific importance of this technology in enhancing modelling accuracy and efficiency, optimizing resource management, driving technological innovation, and improving disaster response capabilities. These insights provide a comprehensive understanding of how 3D geological modelling can significantly impact and benefit multiple industries worldwide.

Accurate measurement techniques and prediction approaches for the in-situ rock stress

The precise calculation and evaluation of the in-situ rock stress tensor is a crucial factor in addressing the major challenges related to subsurface engineering applications and earth science research. To improve the accuracy of in-situ stress measurement and prediction, an improved overcoring technique involving a measurement circuit, temperature compensation, and calculation method is presented for accurately measuring the in-situ rock stress tensor. Furthermore, an embedded grey BP neural network (GM–BPNN) model is established for predicting in-situ rock stress values. The results indicate that the improved overcoring technique has significantly improved the stress measurement accuracy, and a large number of valuable stress data obtained from many mines have proved the testing performance of this technique. Moreover, the mean relative errors of the prediction results of GM(0, 1) for the three principal stresses all reach 6–30%, and the accuracy of the model fails to meet the requirements. The average relative errors of the prediction results of the BPNN model are all less than 10%, and the model accuracy meets the requirements and has sufficient credibility. Compared with the GM and BPNN models, the embedded GM–BPNN model produces the best results, with mean relative errors of 0.0001–4.8338%. The embedded GM–BPNN model fully utilizes the characteristics of grey theory and BP neural network, which require a small sample size, weaken the randomness of the original data, and gradually approach the accuracy of the model, making it particularly suitable for situations with limited stress data.

Applications of X-ray Powder Diffraction Microstructural Analysis in Applied Clay Mineralogy

Clay minerals and sheet silicates are the main constituents of lutites and clays. These materials are relevant in earth science research as well as in economic geology because of the great variety of applications, based on their particular features at different levels of aggregation in mineral assemblages and on the microstructural and structural characteristics of the mineral constituents frequently characterized by micro- and nanocrystalline appearance. Thus, X-ray diffraction is a main tool for fundamental and applied research of these materials. The present review concerns their microstructural research from powder X-ray diffraction data.

How big is a boulder? The importance of boulder definition choice in earth science research and river management

AbstractBoulders are globally widespread and influence processes across many landscapes including hillslopes, coasts, rivers and extra‐terrestrial settings. Boulders are described as particles, sufficiently large, that they have a disproportionate effect on the surrounding landscape. Moving beyond this conceptual definition, however, requires a somewhat arbitrary decision of how to define a minimum boulder size. The implications of boulder definition on study findings are rarely considered. We investigate the suitability of five boulder definitions, two based on fixed sizes: (1) 0.26 m and (2) 1 m, and three definitions which vary based on system characteristics: (3) grain mobility, (4) grain protrusion and (5) surface grain size distribution (> 84th percentile, D84). We consider the impact of definition on calculated boulder metrics, and, for the >1m and >D84 definitions, their association with channel and catchment characteristics across 20 boulder‐bed streams in northern Sweden. We also surveyed river managers responsible for restoring these rivers, to gain a practitioner insight on boulder size definition. We found that boulder definition matters; for metrics relating to the number or density of boulders, the >D84 and >1m size definitions were negatively correlated. Surveys indicated the importance of communicating boulder definition. We conclude that, whilst the best choice of boulder size definition will vary based on the questions of interest and techniques employed, evaluating the implications of the chosen boulder size definition and communicating the reasoning behind boulder definition choice is crucial.

Space Activities and the Simultaneous Development of the Economy in Türkiye

As space technologies have become an integral part of our daily lives, they have profoundly influenced the course of human civilization since 1957. These technologies have provided humanity with many remarkable insights. Therefore, they have an important role in modern society. Satellite technology is not only vital for military and defence but also for civil applications such as transportation, communication, disaster management, agriculture planning, land, and urban planning. These applications make the space sector an engine of economic growth having advanced and the state-of-the art technology. Technological advances in space continuously benefit the citizens of developed countries. The space economy is defined as all the actions and resources that produce value and benefit to people through exploration, research, knowledge, management, and utility of space. Through satellite communications, navigation systems, earth observation missions, space science and technology research, employment is created, economic growth is achieved, and many industries become more competitive in worldwide economic market. Spinoff companies are born from space activities. In 1994, the Türkiye had its first communication satellite TURKSAT 1B built by Aerospatiale; thus, Türkiye entered the space field being an operator in its region. After millennium, the Turkish space industry has ramped-up by developing its dedicated communication and observation satellites, and their subsystems and creating a launcher system for microsatellites. Türkiye is now developing space technology more comprehensively. This article describes recent developments of space activities and future work in Türkiye. In the article, the strategies in other countries in space technologies are also discussed and suggestions are made for space technology education, development of space economy and regulation of space activities for Türkiye.

Investigating the Effects of Elements and Minerals on Public Health: An Overview of Medical Geology

Background & Aims: Medical geology is an emerging branch of geological sciences, which examines the relationship between geological findings and human and ecosystem health, as well as the effect of environmental factors and elements on the geographical distribution of diseases in the field of health and treatment. The aim of this research was to provide a comprehensive understanding of how geological factors and minerals affect public health outcomes. Materials and Methods: A total of 1561 publications including books and papers published from 1992 until 2024 were reviewed, 168 of which were chosen and 21 articles were mentioned. Results: Medical studies on patients showed that there is a direct relationship between minerals and diseases, which is caused by the lack of some minerals in the food chain, or the excessive presence of some of these substances in the food chain of people of different regions of the world. These minerals cause changes in the cellular and molecular structure, which can lead to the development of various diseases. Conclusion: It seems that in the future, with scientific investigations in the field of medical geology, important results can be achieved in the prevention of various diseases. It is obvious that the realization of this requires increased cooperation of medical, health, and earth science researchers and it can be an effective step towards improving the health of the society and preparing and producing new drugs and new treatment methods. It will be presented using this knowledge.

Employing keywords and lexical bundles within figure captions in earth science research articles

The presentation of data is considered an essential part of academic writing, especially in research articles, and it could not stand alone without a short text to describe the visual data, namely figure legends. This research aims to identify the keywords and lexical bundles frequently used in figure legends. In addition, this research also examines the functions conveyed by these linguistic features. Based on the identification of the corpus, there are 334 keywords found with the 20 highest frequencies belonging to the terminologies frequently used in earth science (fault, seismic, quartz, and formation) and words related to the visual representation of the figure legends (line/lines, map, area, figure and legend). Furthermore, noun-related bundles are found to be the greatest number for lexical bundles, with 165, followed by verb-related bundles with 79, clause-related bundles with 54, and preposition-related bundles with 17. These lexical bundles perform research-oriented, text-oriented, and participant-oriented functions. These findings suggest that lexical bundles play a role in explaining the research condition, connecting visual data and research results discussed in research articles, and displaying information about visual data or specific parts of research articles to readers.

The Research Growth Rate (2019-2020) of Forty Countries in the Field of Earth and Planetary Science Research

The project is designed to explore the research output of the world’s top forty countries in the field of Earth and Planetary Sciences from 2010 to 2020. The data in the field of earth and planetary sciences for each country was retrieved from Scopus. Based on the number of publications (in 2020), the top three most productive countries are China (n = 39236), United States (n = 27889), and United Kingdom (n = 10784). However, based on the growth rate (for 2019-2020), the top three countries are Pakistan (n = 38.86), Indonesia (n = 31.36), and Finland (n = 22.00). In 2013-14 and 2010-11, Pakistan was in the 3rd position. While for the years (2015-2018) Pakistan occupied the 2nd position. This motivated us to decode the research productivity of Pakistan since independence (1947). The total number of publications was found to be 4972, where 8814 authors and 10445 institutes significantly contributed. The University of Peshawar was the most productive university with 584 publications. Pakistan produced 90.50 % (n = 4500) publications in the 21st century (after 2000). The astonishing growth could be attributed to several reasons, for example, the establishment of the Higher Education Commission (HEC), Islamabad, Pakistan, an increase in total expenditure on higher education, an increase in international collaboration, etc. At the same time (in 2020), Pakistan has a meagre global share of only 0.178 % (total global production is 2,790,854). This confirms that significant measures are needed to increase the overall productivity (both the number and quality of research publication.

A Decadal Survey Without Analogs: Earth Observation Needs for a Warming World

AbstractSince 2007, the National Academy for Sciences Engineering and Medicine (NASEM) has recommended Earth Science research and investment priorities every 10 years. The Decadal Survey balances the continuation of essential climate variable time series against unmet measurement needs and new Earth Observations made possible by technological breakthroughs. The next survey (2027–2028, DS28) will be framed by a rapidly changing world, and it will be critical to anticipate the observational needs of the 2030s–2040s, a world increasingly dominated by climate extremes and a rapidly changing Earth system. Here, we highlight some of the changes that factor into a framework for the DS28.

The replication crisis and its relevance to Earth Science studies: Case studies and recommendations

Numerous scientific fields are facing a replication crisis, where the results of a study often cannot be replicated when a new study uses independent data. This issue has been particularly emphasized in psychology, health, and medicine, as incorrect results in these fields could have serious consequences, where lives might be at stake. While other fields have also highlighted significant replication problems, the Earth Sciences seem to be an exception. The paucity of Earth Science research aimed at understanding the replication crisis prompted this study. Specifically, this work aims to fill that gap by seeking to replicate geological results involving various types of time-series. We identify and discuss 11 key variables for replicating U-Pb age distributions: independent data, global sampling, proxy data, data quality, disproportionate non-random sampling, stratigraphic bias, potential filtering bias, accuracy and precision, correlating time-series segments, testing assumptions and divergent analytical methods, and analytical transparency. Even while this work primarily focuses on U-Pb age distributions, most of these factors (or variations of them) also apply to other geoscience disciplines. Thus, some of the discussions involve time-series consisting of εHf, δ18O-zircon, 14C, 10Be, marine δ13C, and marine δ18O. We then provide specific recommendations for minimizing adverse effects related to these factors, and in the process enhancing prospects for replicating geological results.

Distributed Acoustic Sensing for Crowd Motion and Firecracker Explosions in the Fireworks Show

Abstract Urban seismology has recently emerged as a vibrant scientific field, driven by the growing interest in seismic signals generated by major public events, sports gatherings, and transportation services. However, deploying dense traditional seismometers in economically active, densely populated urban areas with heavy traffic poses significant challenges. In this study, we conducted a field experiment utilizing distributed acoustic sensing (DAS) technology during a fireworks display in Guangzhou on 5 February 2023. About 572 m of optical fiber was turned into 286 seismic sensors and deployed on LingShan Island to monitor various vibration signals generated during the fireworks show. Our analysis revealed substantial correlations between crowd motions during different phases of the event and ambient noise features recorded by DAS. Moreover, the cross-correlation functions of the ambient noise with its dispersion characteristics pointed to near-field pedestrian activity as the primary noise source. Real-time heat maps of human crowd motions were reconstructed from DAS recording, offering significant insights into the variations of activity intensity across different locations. Discerning fireworks events on the DAS array is more effective than on a scattered seismometer array, because it is easier to ensure that the same event is picked for all the sites in the DAS dense linear configuration. The DAS data inspection allowed us to pick up a total of 549 firecracker explosions in comparison to the seismometer data that only allowed us to detect 116 firecracker events. The heights of fireworks were located by the grid-search method and predominantly distributed at 75–300 m, closely aligning with actual fireworks explosion locations. Our findings underscore that the DAS technology can monitor crowd motion and detect vibration signals in the air, bridging the gap between fundamental earth science research and human social activities.

The International Conference on Environmental and Earth Sciences

“Multidisciplinary Approach in the Environmental and Earth Sciences for Sustainable Development” ICEES 2023 (https://www.its.ac.id/tlingkungan/icees2023/) INTRODUCTION Institut Teknologi Sepuluh Nopember (ITS) celebrates its 63rd Anniversary on November 10th, 2023. To commemorate this moment, the Department of Environmental Engineering (DEE), Faculty of Civil, Planning, and Geo-Engineering (CIVPLAN) at ITS, in collaboration with the Department of Geophysics Engineering of ITS and the Faculty of Engineering and Built Environment of Universiti Kebangsaan Malaysia (UKM), conducted the International Conference on Environmental and Earth Sciences (ICEES). ICEES 2023 took place in conjunction with the CIVPLAN’s International Conference, which serves as a platform to connect with other seminars held by each department within the faculty. Environmental issues have been increasing as a result of many factors that lead to potential detrimental effects on our children’s future. For the past 40 years, DEE has been spending tremendous efforts to contribute ways to reduce and minimize environmental pollution.This conference takes the general theme of the Multidisciplinary Approach in the Environmental and Earth Sciences for Sustainable Development, supporting the implementation of Sustainable Development Goals (SDGs). Rapid population growth and economic development have become contributing factors to environmental degradation. Urban areas receive more pressure due to escalated urbanization, excess use of natural resources, and pollution of the environment. Environmental pollution has been linked to various diseases, such as water-borne diseases and respiratory diseases, and the endangerment of biodiversity. Over the past decades, cities in Asia have been rapidly growing. Climate change has exacerbated the deterioration in environmental quality in the cities. Communities living in small islands and coastal areas became more vulnerable than before.More efforts are needed to develop solutions to create sustainable cities and communities. ICEES 2023 emphasizes multidisciplinary research in environmental and earth sciences for sustainable development along with practices and fosters exchanges among scientists, practitioners, professionals, and young researchers who are concerned with the above aspects. DEE strives to contribute not only to society but also to knowledge updates.List of Objective, Conference Date and Platform, Participants, Acknowledgements, Sessions, Organizers, Contact person, Administration, Secretariat, Documentation, International Advisory Board, Scientific Committee are available in this Pdf.