Continuous land cover change monitoring in the remote sensing big data era

Since the late 20th century,global change issues have attracted lots of attention.As a key component of global changes,land cover and land use information has been increasingly important for improved understanding of global environmental changes and feedbacks between social and environmental systems(Verburg et al.,2015).A set of national and global scale land cover/use products with higher spatial and temporal resolutions have been developed to fill this gap.In China,existing efforts include China’s

Agricultural remote sensing big data:Management and applications

Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.

Developments in Land Use and Land Cover Classification Techniques in Remote Sensing: A Review

Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem’s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.

Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making

Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Regression Tree (BRT) can address Big Data challenges to drive decision making. The challenge of this study is lack of interoperability since the data, a collection of GIS shapefiles, remotely sensed imagery, and aggregated and interpolated spatio-temporal information, are stored in monolithic hardware components. For the modelling process, it was necessary to create one common input file. By merging the data sources together, a structured but noisy input file, showing inconsistencies and redundancies, was created. Here, it is shown that BRT can process different data granularities, heterogeneous data and missingness. In particular, BRT has the advantage of dealing with missing data by default by allowing a split on whether or not a value is missing as well as what the value is. Most importantly, the BRT offers a wide range of possibilities regarding the interpretation of results and variable selection is automatically performed by considering how frequently a variable is used to define a split in the tree. A comparison with two similar regression models (Random Forests and Least Absolute Shrinkage and Selection Operator, LASSO) shows that BRT outperforms these in this instance. BRT can also be a starting point for sophisticated hierarchical modelling in real world scenarios. For example, a single or ensemble approach of BRT could be tested with existing models in order to improve results for a wide range of data-driven decisions and applications.

生态水文系统支撑城市韧性安全建设的若干思考

近40年城市生态水文学应用研究的重要进展,特别是区域水循环要素的优化调控,有效地支撑了韧性安全城市建设。在城市生态水文系统理念的指导下,北美、欧洲和中国相继出现了低影响开发、水框架指令、海绵城市与气候适应型城市建设。全球变化和人为活动相互作用产生了复杂的城市生态水文过程,自然水循环模式已转变为“自然-社会”相互作用的二元水循环模式。面对全球变化,韧性安全城市的建设更加关注对气候变化的减缓和适应,特别是对极端天气气候事件的适应能力。我国长期持续大规模的城市开发建设,强烈影响和改变着区域水循环关键要素,城市地下空间开发利用和地面沉降面临着极端天气气候情境下的灾害风险。因此,地学基础条件及其韧性构成了城市韧性安全底座的基础。基于二元水循环关键要素调控设计的减缓和适应对策,在新一轮气候适应型城市建设中发挥了重要作用。“气候变化—区域二元水循环—水平衡调控—灾害风险”“遥感大数据—台站网小数据—传递函数—云孪生”,可作为一种地球大数据支撑韧性城市建设与可持续发展的技术框架和方法组合。上海作为长江流域的龙头和海陆交互敏感区域,城市韧性安全还面临着海平面上升的考验。在气候变化情景下区域二元水循环关键要素调控,可作为地学服务上海地下空间安全及城市韧性安全的一个策略。

关于时空大数据助力乡村振兴的思考

数字乡村建设既是乡村振兴的战略方针,也是数字中国建设的重要内容。本文围绕“时空”联动“三农”,在如何利用时空信息技术帮助全国农村振兴、服务数字农业和数字乡村建设等方面,进行思考与探讨,旨在利用时空数据优势,通过实时发布处理的遥感影像资料、气象资料等多源时空数据,变时空信息技术大数据为农村振兴的“新基础设施”与基石;通过强化整合人工智能算法、作物模型等遥感应用,构建起农业农村大数据服务平台,进行正向思维,推进数据开放、实时共享,围绕精准农业、数字田地、智能监管等乡村振兴所需内容,积极推动形成多效合一的时空大数据基础基地,赋能农业农村发展,实现时空数字链接、自动化管理、农业农村农民智能增效,助力中国乡村振兴战略提速。

水利遥感数据规模化加工处理平台框架研究

以卫星遥感为代表的地表信息空天获取手段是支撑新时期智慧水利建设的重要途径。当前,遥感数据获取能力与处理服务能力之间出现了失衡,大量的对地观测卫星遥感数据被获取,而能够支撑水利业务的遥感数据产品即时服务能力和动态更新频次仍然存在瓶颈,“卫星数据量大、处理流程复杂、产品服务不足”的现状长期存在,制约着水利行业遥感应用的质量和效率。本文通过调研当前国内外遥感大数据加工处理进展,分析了面向智慧水利建设的水利遥感数据处理平台需求,设计了一种水利遥感数据规模化加工处理平台的框架,用于海量卫星遥感数据产品全链路自动化处理,以提升水利遥感数据产品处理质量和服务效率。该平台可以实现超过30种多源高分遥感数据全流程处理,通过动态分配GPU和CPU计算缓存,实现卫星遥感数据正射校正、融合、镶嵌、质检以及水利遥感专题信息提取等流程规模化、并行化和自动化处理,生产满足数字孪生流域建设的卫星遥感专题产品,支撑新时期智慧水利建设的遥感深度应用。

基于遥感影像的大当量爆炸建筑物毁伤评估模型

为了研究大当量爆炸建筑物毁伤评估问题,基于遥感影像解译和大数据分析构建了大当量爆炸建筑物毁伤评估模型。首先,基于大当量爆炸的具体历史案例构建了毁伤数据集,具体指基于遥感影像提取建筑物毁伤信息,辅助大数据信息补充毁伤细节,利用地理信息系统空间分析数字化毁伤信息,构成毁伤数据集。然后,基于毁伤数据集中的训练样本修正经验模型参数,构建了适用于大当量爆炸的针对不同类型建筑物的毁伤评估模型,并基于毁伤数据集中的验证样本测试了模型性能。实验证明:所构建模型拟合优度高于96%,检验样本准确度高于84%,整体误差在可接受范围内。所构建模型在一定精度要求下可为大当量爆炸事故评估提供参考。

矿山生态环境定量遥感监测与智能分析系统设计与实现

矿山生态环境监测与治理是国家生态文明建设和“双碳”目标下的重中之重,其信息化、智能化建设在新一代信息技术革命的助推下成为数字中国建设的重要一环,也是当下时代发展的必然趋势。然而,现有矿山生态环境监测系统仍然停留在单一专题、要素不全、基础量测、本地管理的初级阶段,无法满足现实环境中对矿山生态环境多要素、长时序、高频次监测与分析的需求。基于此,提出一种B/S架构下的矿山生态环境定量遥感监测与智能分析系统——矿山生态天眼,并详细介绍了其研发需求、技术架构、关键技术及核心功能。系统依托卫星遥感技术及其他监测手段,获取并聚合不同来源、信息丰富的矿山生态大数据,形成矿山分布一张图和数据资源服务;进而基于定量遥感反演矿山生态环境各生态参数,形成一套长时序、多要素的矿山生态监测产品,涵盖人类活动、自然地理条件和“植−土−水−气”各生态要素;在此基础上,系统提供GIS时空分析、统计分析及综合定量评价等工具集,分别实现对矿区土地利用、归一化植被指数(NDVI)等参数伴随采矿活动在空间上的变化监测,对土壤含水量、水体悬浮物浓度等生态要素历史统计值在不同时空位置和区域下的查询与可视化,对顾及多项生态因子的矿山生态环境质量综合定量评价,并最终形成矿山生态扰动与治理监测报告。矿山生态天眼的应用将服务于矿山生态环境全过程变化监测、数据管理、智能分析和决策应用,有望提高矿山生态环境监测与治理的效率和智能化水平,为推动生态文明信息化建设提供参考方案。

基于高分遥感图像处理技术的公路勘查方法研究

针对公路高分遥感勘测技术中,遥感图像易受天气干扰、数据分析精确度低等问题,研究了基于高分遥感图像处理技术的公路勘查方法。设计了双行信息采样电路以及采样信息保持电路,实现了公路勘查过程中的高清图像信息采集,由此避免因自身电源频率、外部磁场等因素的干扰而产生图像噪点,保证采集图像信息的准确性。此外,采用一种暗通道先验模型去云雾技术,能够有效排除云雾天气对遥感图像采集的干扰。通过基于Storm的大数据算法,提高系统的整体数据分析及传输能力。试验结果表明,该方法不仅增强了去云雾能力,还可以还原正常图像85%左右的图像特征点。该方法具有很强的数据处理、抗干扰以及数据传输能力。

Cloud-based storage and computing for remote sensing big data:a technical review

The rapid growth of remote sensing big data(RSBD)has attracted considerable attention from both academia and industry.Despite the progress of computer technologies,conventional computing implementations have become technically inefficient for processing RSBD.Cloud computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past years.This study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer science.First,we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications,i.e.raster storage,metadata management,data homogeneity,and computing paradigms.Second,we introduce state-of-the-art cloud-based data management technologies for RSBD storage.The unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies,which we name the RSBD data model.Four data models are suggested,i.e.scenes,ARD,data cubes,and composite layers.Third,we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing implementations.Finally,we categorize the architectures of mainstream RSBD platforms.This research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers.

Deep learning for processing and analysis of remote sensing big data:a technical review

In recent years,the rapid development of Earth observation tech-nology has produced an increasing growth in remote sensing big data,posing serious challenges for effective and efficient proces-sing and analysis.Meanwhile,there has been a massive rise in deeplearningbased algorithms for remote sensing tasks,providing a large opportunity for remote sensing big data.In this article,we initially summarize the features of remote sensing big data.Subsequently,following the pipeline of remote sensing tasks,a detailed and technical review is conducted to discuss how deep learning has been applied to the processing and analysis of remote sensing data,including geometric and radiometric processing,cloud masking,data fusion,object detection and extraction,landuse/cover classification,change detection and multitemporal ana-lysis.Finally,we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.

The Theoretical and Practical Foundations of Strong Earthquake Predictability

Earthquakes and the tsunamis they produce are the world’s most devastating natural disasters, affecting more than 100 countries. Not surprisingly, the problem of earthquake prediction has occupied scientists’ minds for more than two thousand years. This paper provides theoretical and practical arguments regarding the possibility of predicting strong and major earthquakes worldwide. Many strong and major earthquakes can be predicted at least two to five months in advance, based on identifying stressed areas that begin to behave abnormally before strong events, with the size of these areas corresponding to Dobrovolsky’s formula. We make predictions by combining knowledge from many different disciplines: physics, geophysics, seismology, geology, and earth science, among others. An integrated approach is used to identify anomalies and make predictions, including satellite remote sensing techniques and data from ground-based instruments. Terabytes of information are currently processed every day with many different multi-parametric prediction systems applied thereto. Alerts are issued if anomalies are confirmed by a few different systems. It has been found that geophysical patterns of earthquake preparation and stress accumulation are similar for all key seismic regions. The same earthquake prediction methodologies and systems have been successfully applied in global practice since 2013, with the technology successfully used to retrospectively test against more than 700 strong and major earthquakes since 1970. In other words, the earthquake prediction problem has largely been solved. Throughout 2017-2021, results were presented to more than 160 professors from 63 countries.

Global water cycle and remote sensing big data: overview, challenge, and opportunities

The Earth’s water cycle involves energy exchange and mass move-ment in the hydrosphere and thus sustains the dynamic balance of global hydrologic cycle.All water cycle variables on the Earth are closely interconnected with each other through the process of energy and water circulation.Observing,understanding and predict-ing the storage,movement,and quality of water remains a grand challenge for contemporary water science and technology,especially for researches across different spatio-temporal scales.The remote sensing observing platform has a unique advantage in acquiring complex water information and has already greatly improved obser-ving,understanding,and predicting ability of the water cycle.Methods of obtaining comprehensive water cycle data are also expanded by new remote sensing techniques,and the vast amount of data has become increasingly available and thus accelerated a new Era:the Remote Sensing Big Data Study of Global Water Cycle.The element inversion,time and space reconstruction,and scale conver-sion are three key scientific issues for remote sensing water cycle in suchEra.Moreover,it also presents a huge opportunity of capitalizing the combinations of Remote Sensing and Big Data to advance and improve the global hydrology and water security research and devel-opment,and uncork the new bottlenecks.

地学大数据背景下遥感课程的多阶段进阶混合式教学模式探索

地学大数据的爆发增长及人工智能的迅猛发展,为遥感学科注入了新的血液,促使我们思考遥感教学的改革方向。本文对遥感教学的线上、线下教学模式进行了深入分析,在此基础上,提出“线上+线下”的多阶段进阶混合式教学模式。从地球科学前沿问题入手,以防灾减灾、环境监测、资源开发等国家需求为专题牵引,建立项目驱动的进阶式教学模式、科研探索型的高阶思维训练方式,充分培养学生的科学思维、自主探索能力。相比传统课程,加强线上云平台与线下课堂教学的资源融合,重视大数据的处理分析能力,有助于提升学生在新时代的竞争力,具有推广和借鉴意义。

大数据智能时代遥感课程实践教学模式的改革与探索

大数据与人工智能的迅猛发展给遥感学科增添了新活力。许多高校在遥感课程的教学实践中依然遵循传统的教育模式,忽略了大数据与人工智能技术带来的遥感应用创新。为抢抓大数据与人工智能技术带来的机遇,应对人才知识结构面临的严峻挑战,迫切需要调整人才培养的知识结构体系和教学教育模式。为此,本文进行了大数据智能时代遥感类课程实践教学的尝试,旨在结合专业特色,因材施教地培养具有较强实践能力兼具创新意识的本科人才。

地理学领域的人工智能应用与思考

人工智能技术在地理学中的应用具有广阔的前景,广泛参与地理过程的观测、分析、模拟和预测.面向地理学领域的人工智能应用,以“智能感知-智慧表达”为脉络,梳理了人工智能在地理学中的表现形式和地理学各领域的应用.在此基础上归纳总结了目前应用在地理大数据智能处理、尺度效应、模型的不确定性等方面的问题,并提出未来在多源数据协调与协同、模型的集成、人工智能的可解释性和地理大模型的构建等方面的建议.强调针对人工智能地理学应用将逐步通过地理大数据的协同挖掘、学习大量地理要素数据、增强模型的集成与解释、训练大模型具备理解地理学三定律的能力.

遥感智能信息处理的发展及技术前景

遥感信息提取技术虽不断推陈出新,但在智能化、精准实用性方面始终存在巨大的瓶颈问题,有必要围绕遥感智能计算和信息提取这个发展主题进行总结和讨论。从“机理—尺度—数据—智能”4个层面,逐步就遥感信息提取与定量反演路径的发展融合、基于像素和面向对象不同处理单元模式、时空谱数据融合、遥感解译的智能化因素四方面进行剖析,从而提出未来“数据获取知识”和“知识引导数据”双向驱动、遥感大数据和地学知识图谱相融合的遥感智能计算架构,尝试推动遥感科学从经典向现代化的跃迁。

遥感大数据智能监测平台研究及应用

如何融合云计算、大数据、人工智能等先进技术手段,开展对遥感大数据的研究,进行遥感影像智能化处理,为空间智能应用提供更加精准、高效的数据支撑已成为必然,并具有广泛的应用前景。本文介绍了遥感大数据基本特征及研究现状,并运用深度神经网络等算法进行特征提取和目标识别,提出一种基于遥感大数据的智能监测方法,该方法结合了遥感技术和人工智能算法,通过对地表和大气信息的获取、分类和分析,实现了对城市环境、自然资源和生态系统的全面监测和管理。此外,开发了一套基于云计算和分布式处理的智能监测平台,该平台支持灵活的数据集成、可视化展示和实时预警功能,并已成功应用于城市的监测和管理任务中。