Exploring deep learning for landslide mapping:A comprehensive review

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

Intelligent pavement condition survey:Overview of current researches and practices

Automated pavement condition survey is of critical importance to road network management.There are three primary tasks involved in pavement condition surveys,namely data collection,data processing and condition evaluation.Artificial intelligence(AI)has achieved many breakthroughs in almost every aspect of modern technology over the past decade,and undoubtedly offers a more robust approach to automated pavement condition survey.This article aims to provide a comprehensive review on data collection systems,data processing algorithms and condition evaluation methods proposed between 2010 and 2023 for intelligent pavement condition survey.In particular,the data collection system includes AI-driven hardware devices and automated pavement data collection vehicles.The AI-driven hardware devices including right-of-way(ROW)cameras,ground penetrating radar(GPR)devices,light detection and ranging(LiDAR)devices,and advanced laser imaging systems,etc.These different hardware components can be selectively mounted on a vehicle to simultaneously collect multimedia information about the pavement.In addition,this article pays close attention to the application of artificial intelligence methods in detecting pavement distresses,measuring pavement roughness,identifying pavement rutting,analyzing skid resistance and evaluating structural strength of pavements.Based upon the analysis of a variety of the state-of-the-art artificial intelligence methodologies,remaining challenges and future needs with respect to intelligent pavement condition survey are discussed eventually.

Research surveys and their evolution:Past,current and future uses in healthcare

Research surveys are believed to have originated in antiquity with evidence of them being performed in ancient Egypt and Greece.In the past century,their use has grown significantly and they are now one of the most frequently employed research methods including in the field of healthcare.Modern validation techniques and processes have allowed researchers to broaden the scope of qualitative data they can gather through these surveys such as an individual’s views on service quality to nationwide surveys that are undertaken regularly to follow healthcare trends.This article focuses on the evolution and current utility of research surveys,different methodologies employed in their creation,the advantages and disadvantages of different forms and their future use in healthcare research.We also review the role artificial intelligence and the importance of increased patient participation in the development of these surveys in order to obtain more accurate and clinically relevant data.

From Digitalized to Intelligentized Surveying and Mapping: Fundamental Issues and Research Agenda

Nowadays Surveying and Mapping(S&M)production and services are facing some serious challenges such as real-timization of data acquisition,automation of information processing,and intellectualization of service applications.The main reason is that current digitalized S&M technologies,which involve complex algorithms and models as the core,are incapable of completely describing and representing the diverse,multi-dimensional and dynamic real world,as well as addressing high-dimensional and nonlinear spatial problems using simple algorithms and models.In order to address these challenges,it is necessary to explore the use of natural intelligence in S&M,and to develop intelligentized S&M technologies,which are knowledge-guided and algorithm-based.This paper first discusses the basic concepts and ideas of intelligentized S&M,and then analyzes and defines its fundamental issues in the analysis and modeling of natural intelligence in S&M,the construction and realization of hybrid intelligent computing paradigm,and the mechanism and path of empowering production.Further research directions are then proposed in the four areas,including knowledge systems,technologies and methodologies,application systems,and instruments and equipments of intelligentized S&M.Finally,some institutional issues related to promoting scientific research and engineering applications in this area are discussed.

Development of Integrated and Intelligent Geodetic and Photogrammetry Satellites with Corresponding Key Technologies

Aerospace surveying and mapping has become the main method of global earth observation.It can be divided into the geodetic observation satellites and the topographic surveying satellites according to the disciplines.In this paper,the geodetic satellites and photographic satellites are introduced respectively.Then,the existing problems in Chinese earth observation satellites are analyzed,and the comprehensive satellite with integrated payloads,the intensive microsatellite constellation and the intelligent observation satellite are proposed as three different development ideas for the future earth observation satellites.The possibility of the three ideas is discussed in detail,as well as the related key technologies.

Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review

Landslides are one of the geological disasters with wide distribution,high impact and serious damage around the world.Landslide risk assessment can help us know the risk of landslides occurring,which is an effective way to prevent landslide disasters in advance.In recent decades,artificial intelligence(AI)has developed rapidly and has been used in a wide range of applications,especially for natural hazards.Based on the published literatures,this paper presents a detailed review of AI applications in landslide risk assessment.Three key areas where the application of AI is prominent are identified,including landslide detection,landslide susceptibility assessment,and prediction of landslide displacement.Machine learning(ML)containing deep learning(DL)has emerged as the primary technology which has been considered successfully due to its ability to quantify complex nonlinear relationships of soil structures and landslide predisposing factors.Among the algorithms,convolutional neural networks(CNNs)and recurrent neural networks(RNNs)are two models that are most widely used with satisfactory results in landslide risk assessment.The generalization ability,sampling training strategies,and hyperparameters optimization of these models are crucial and should be carefully considered.The challenges and opportunities of AI applications are also fully discussed to provide suggestions for future research in landslide risk assessment.

Explainable artificial intelligence models for mineral prospectivity mapping

Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectively handle and interpret.Artificial intelligence(AI)algorithms,which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration,have demonstrated excellent performance in MPM.However,AI-driven MPM faces several challenges,including difficult interpretability,poor generalizability,and physical inconsistencies.In this study,based on previous studies,we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence(XAI)models for MPM by embedding domain knowledge throughout the AI-driven MPM,from input data to model design and model output.This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process,thereby improving model interpretability and performance.Overall,the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process,presenting a valuable and promising area for future research designed to improve MPM.

Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

Purpose:Detection of research fields or topics and understanding the dynamics help the scientific community in their decisions regarding the establishment of scientific fields.This also helps in having a better collaboration with governments and businesses.This study aims to investigate the development of research fields over time,translating it into a topic detection problem.Design/methodology/approach:To achieve the objectives,we propose a modified deep clustering method to detect research trends from the abstracts and titles of academic documents.Document embedding approaches are utilized to transform documents into vector-based representations.The proposed method is evaluated by comparing it with a combination of different embedding and clustering approaches and the classical topic modeling algorithms(i.e.LDA)against a benchmark dataset.A case study is also conducted exploring the evolution of Artificial Intelligence(AI)detecting the research topics or sub-fields in related AI publications.Findings:Evaluating the performance of the proposed method using clustering performance indicators reflects that our proposed method outperforms similar approaches against the benchmark dataset.Using the proposed method,we also show how the topics have evolved in the period of the recent 30 years,taking advantage of a keyword extraction method for cluster tagging and labeling,demonstrating the context of the topics.Research limitations:We noticed that it is not possible to generalize one solution for all downstream tasks.Hence,it is required to fine-tune or optimize the solutions for each task and even datasets.In addition,interpretation of cluster labels can be subjective and vary based on the readers’opinions.It is also very difficult to evaluate the labeling techniques,rendering the explanation of the clusters further limited.Practical implications:As demonstrated in the case study,we show that in a real-world example,how the proposed method would enable the researchers and reviewers of the academic research to detect,summarize,analyze,and visualize research topics from decades of academic documents.This helps the scientific community and all related organizations in fast and effective analysis of the fields,by establishing and explaining the topics.Originality/value:In this study,we introduce a modified and tuned deep embedding clustering coupled with Doc2Vec representations for topic extraction.We also use a concept extraction method as a labeling approach in this study.The effectiveness of the method has been evaluated in a case study of AI publications,where we analyze the AI topics during the past three decades.

基于路径规划特点的语义目标导航方法

为了解决语义目标导航任务中存在的探索效率低、深度不精准等问题,本文构建了一个解决语义目标导航任务的框架,在语义地图构建模块中引入了深度图边缘处理以及地图纠错机制;在探索模块中引入了覆盖范围最大化算法;在路径规划模块中引入了替代点机制。本文在一个3D仿真环境下进行了实验。实验结果表明,本文提出的解决方案明显提升了语义目标导航任务的性能。此外,本文所提方法成功应用到了四足机器人上,从而验证了其在现实场景下的泛化性。

基于ESI的化学与人工智能领域热点交叉主题识别与趋势研究

探讨化学领域与人工智能的交叉现况,挖掘热点交叉研究主题,能够为基金项目资助决策提供有力依据,从而优化资源分配,促进化学领域前沿科学进展。本研究基于交叉领域的期刊文献和基金项目数据,从发文量、国家/机构合作、学科交叉三个维度揭示交叉态势,通过期刊文献关键词共现与聚类分析识别热门交叉研究主题,并进一步结合中国、美国在各主题下的基金项目资助情况研究交叉领域的发展趋势。研究发现化学领域与人工智能交叉的研究随时间发展显著增加,识别出泛函理论、虚拟筛选等七个热点交叉主题,揭示了人工智能在化学领域的应用潜力以及发展趋势,并给予基金资助应针对不同的主题定制不同资助策略的启示。

长城色彩折射的建造技艺、管理制度与民族智慧

与宫殿、寺庙、石窟等建筑遗产相比,长城在材料色彩方面“乏善可陈”,甚至还出现专有名词“长城灰”,将长城的色彩特征固化。本文通过无人机采集明长城全线图像并引入图像类人工智能提取其主色调,首次量化各段长城色彩、绘制全线色彩地图,证明长城“富彩”;并结合材料成分分析、史料挖掘,首次揭示色彩背后蕴含的长城材料色彩法则、色彩质量评价标准与奖励办法,以及以色立威、以建止战的核心意图。这一成果尝试填补古代建筑色彩研究的长城缺环、揭示长城作为军事遗产的材料色彩艺术造诣,挖掘出未开发长城段新的遗产景观资源,从全新的视角助推长城国家文化公园建设。

AI绘画在地图制图中的应用与挑战

进入信息时代,地图制图呈现大众化趋势,大众制图时代来临。人人都能制作地图,但制图质量却参差不齐,如何实现地图制图由专家主导的“授人以鱼”扩展为面向大众的“授人以渔”成为当前的研究热点,迫切需要发展智能化、个性化的制图方法。AI绘画通过机器学习和深度学习等技术,使计算机能够模拟人类艺术家的创造力进行图像创作、编辑和合成图像。AI绘画能否为智能化、个性化制图提供一种新的技术途径?针对这一问题,首先梳理出了由文本生成图像、图像编辑、草图转化为图像、风格化神经渲染、图像风格迁移5类典型AI绘画技术。然后,逐一分析这些技术在游戏地图绘制、地图脱密脱敏、心象地图绘制、艺术感地图绘制、地图风格迁移等方面的应用。最后,总结AI绘画在地图制图中面临的地图内容理解、制图知识规则嵌入以及结果地图的可读性等挑战,以期丰富对AI地图制图的讨论,启发创新性AI制图应用与研究。

AI使能的信道知识地图高效构建与应用

6G对通信速率、连接密度、延迟等关键性能指标提出了更高的要求。与此同时,6G系统更强大的感知定位能力以及智能化发展使得智能驱动的环境认知通信成为可能。探讨了信道知识地图与AI结合的基本概念、融合机理与方法。一方面,信道知识地图作为实现环境认知通信的重要技术,能够学习以收发机位置或虚拟位置为索引的信道知识,从而提前获取部分信道环境先验信息。另一方面,AI日益广泛的应用,使得通信系统能够从复杂的物理环境和位置信息中提取关键特征,并学习环境特征与信道特性之间的映射关系,从而有效提升系统的环境认知能力。因此,信道知识地图与AI的深度融合有望带来6G环境认知通信的新范式,极大提升通信与感知性能。此外,全面综述了AI使能信道知识地图的构建与应用,并对比了各融合方式之间的优势和劣势,展望了AI与信道知识地图融合的未来发展方向与潜力。

新工科背景下面向智能测绘的无人机测绘课程群开发路径研究

随着新工科建设和智能化测绘的不断推进,测绘类相关高校应及时对测绘类专业的课程进行改革,但目前大多开设无人机测绘相关课程的测绘类高校教学重点没有突出新工科背景下面向智能测绘的相关技术在无人机测绘中的应用,该文从教学内容、实践教学方式、考核方式三方面出发,对相关教学体系进行改革,以期能够推进新时代无人机测绘课程群的开发进度,培养更多符合时代要求的测绘学科人才。

中国人工智能研究热点与趋势——基于中文社会科学引文索引(CSSCI)论文的分析

人工智能对伦理、创新、教育和法律带来的影响日益凸显,但传统上关于人工智能的研究多以定性为主、少有数据分析,少数定量研究关注的维度较为单一。为更准确地把握人工智能研究的前沿热点,对发表于中文社会科学引文索引(CSSCI)数据库中2017—2023年有关人工智能的文献进行计量分析和系统评述,运用文献分析软件,对文献发表时间、出版期刊、核心作者、发文机构等方面进行可视化分析和知识图谱分析,识别出关于人工智能研究领域的热点。结果发现:中国人工智能的有关研究在2017年进入发展期,2018年进入爆发期,主要从哲学、管理学、教育学和法学的角度开展;人工智能伦理、管理方式变革、教育管理与政策以及人工智能的法律规制成为当前人工智能研究的主要内容;未来人工智能的热点前沿主要包括人工智能与数字经济的结合、人工智能发展的伦理困境、人工智能机器的地位辨析和人工智能对劳动力市场的影响等议题;未来学术界关于人工智能的研究重点会随着技术的进一步迭代和政策的持续深化而变化,研究主题会更为集中且向纵深发展,从识别一般规律到探寻具体对象的可操作化路径,研究路径从理论探索到实践指引演化。

生成式人工智能的教育应用监管路线图——UNESCO《教育和研究领域生成式人工智能使用指南》解读与启示

随着以ChatGPT为代表的生成式人工智能的问世及快速迭代,生成式人工智能对教育和研究产生巨大的促进作用,同时也带来前所未有的风险和挑战。2023年9月7日,联合国教科文组织发布首部关于生成式人工智能应用的全球指导性文件——《教育和研究领域生成式人工智能使用指南》。通过对该指南的文本分析,介绍文件的出台背景和内容梗概,讨论生成式人工智能所引发的争议和负面影响,呈现了教育和研究领域使用生成式人工智能的监管框架,绘制生成式人工智能在教育和研究领域的应用监管“路线图”,最后结合生成式人工智能在我国教育和研究中的使用现状,向政府部门、科技公司、学校等教育机构、教师、研究人员、学生分别提出相关建议:政府部门要完善政策框架,加强公众参与;科技公司要坚持以人为本,维护数字正义;学校等教育机构要优化校本政策,重塑学习环境,开展AI培训;教师要变革教学方式,开展人机合作,进行伦理示范;研究人员要拓展研究领域,提升研究效能;学生要合理使用工具,强化传统技能。

基于遗传算法的晶圆级芯片映射算法研究

近年来,随着人工智能领域的发展,深度学习已经成为如今最重要的计算负载之一,下一代人工智能以及高性能计算应用对计算平台的算力与通信能力提出了前所未有的需求,晶圆级芯片通过在整片晶圆上集成超高密度的晶体管数量以及互连通信能力,有望为未来的人工智能与超算平台提供革命性的算力解决方案。而其中,晶圆级芯片具有的超大计算资源和独特的新架构使得任务映射算法面临前所未有的新问题,相关研究成为近年来学术界的研究重点。专注于研究人工智能任务在晶圆级硬件资源的映射算法,即通过将人工智能算法表达为多个卷积核,再考虑卷积核的算力特性来基于遗传算法设计晶圆级芯片的映射算法。一系列映射任务下的仿真结果验证了映射算法的有效性,并揭示了执行时间、适配器成本等参数对代价函数的影响。

Causality in structural engineering: discovering new knowledge by tying induction and deduction via mapping functions and explainable artificial intelligence

Causality is the science of cause and effect.It is through causality that explanations can be derived,theories can be formed,and new knowledge can be discovered.This paper presents a modern look into establishing causality within structural engineering systems.In this pursuit,this paper starts with a gentle introduction to causality.Then,this paper pivots to contrast commonly adopted methods for inferring causes and effects,i.e.,induction(empiricism)and deduc-tion(rationalism),and outlines how these methods continue to shape our structural engineering philosophy and,by extension,our domain.The bulk of this paper is dedicated to establishing an approach and criteria to tie principles of induction and deduction to derive causal laws(i.e.,mapping functions)through explainable artificial intelligence(XAI)capable of describing new knowledge pertaining to structural engineering phenomena.The proposed approach and criteria are then examined via a case study.

面向新工科的人工智能与测绘课程融合教改思考与实践

人工智能技术的迅速发展,推动了其他领域的技术变革,测绘作为与人工智能密切相关的学科也开启了技术交叉。在遥感与测绘大数据时代,转型赋能的知识储备与技术在学术与产业界中交替升级,将人工智能新技术融入测绘课程教学可加快测绘行业的发展进程。以遥感科学与技术专业的《计算机视觉与模式识别》课程为例,通过实践项目将计算机视觉、信号处理和传统测绘理论与方法高度融合,在新工科与新基建背景下,将人工智能技术与测绘行业需求相结合,探索学科交叉的教学模式。理论与实践教学案例和经验表明,交叉学科的创新和跨学科思维的建立对于培养解决实际问题的多元化人才至关重要。

职业教育数字教材建设的探索与实践

人工智能技术等发展推动数字教材建设,在发展新质生产力背景下,职业教育数字教材研究尚处于起步阶段。在分析数字教材特征的基础上,从选题确定、项目(场景)设计、资源开发、应用终端开发等方面系统地介绍了职业教育数字教材的开发路径。探讨了人工智能技术在数字教材应用终端中的应用,在自主学习和班级教学两种不同模式下,构建虚拟仿真实训项目智能体和教材智能体,实现数字教材的学习评价创新。通过教育大模型构建,开发教材能力图谱,建设数字教材平台,支撑职业教育数字教材的建设与应用。