Satellite Interferometric Synthetic Aperture Radar(InSAR)is widely used for topographic,geological and natural resource investigations.However,most of the existing InSAR studies of ground deformation are based on rela...Satellite Interferometric Synthetic Aperture Radar(InSAR)is widely used for topographic,geological and natural resource investigations.However,most of the existing InSAR studies of ground deformation are based on relatively short periods and single sensors.This paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets,to address cases when partial overlaps and/or temporal gaps exist.A new Power Exponential Knothe Model(PEKM)fits and fuses overlaps in the deformation curves,while a Long Short-Term Memory(LSTM)neural network predicts and fuses any temporal gaps in the series.Taking the city of Wuhan(China)as experiment area,COSMO-SkyMed(2011-2015),TerraSAR-X(2015-2019)and Sentinel-1(2019-2021)SAR datasets were fused to map long-term surface deformation over the last decade.An independent 2011-2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for comparison.The correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset.The correlation coefficient of the overall results is 0.78,which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation curve.The experimental results are consistent with existing studies of surface deformation at Wuhan,demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.展开更多
Optical and Synthetic Aperture Radar(SAR)remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications,yet further advances are viable through the e...Optical and Synthetic Aperture Radar(SAR)remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications,yet further advances are viable through the exploitation of novel sensor data and imaging modes,big data and high-performance computing,advanced and automated analysis methods.This paper showcases the main research avenues in this field,with a focus on archaeological prospection and heritage site protection.Six demonstration use-cases with a wealth of heritage asset types(e.g.excavated and still buried archaeological features,standing monuments,natural reserves,burial mounds,paleo-channels)and respective scientific research objectives are presented:the Ostia-Portus area and the wider Province of Rome(Italy),the city of Wuhan and the Jiuzhaigou National Park(China),and the Siberian“Valley of the Kings”(Russia).Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite(e.g.Copernicus Sentinels and ESA Third Party Missions)and aerial(e.g.Unmanned Aerial Vehicles,UAV)platforms,as well as field-based evidence and ground truth,auxiliary topographic data,Digital Elevation Models(DEM),and monitoring data from geodetic campaigns and networks.The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes,identify threats to cultural heritage assets due to ground instability and urban development in large metropolises,and monitor post-disaster impacts in natural reserves.展开更多
近十年来,与地球科学相关的数据迎来爆发式增长.这些数据帮助研究人员从不同的领域了解人地系统,其中有相当一部分数据的详细信息由地球科学家发表公布在权威期刊上.如果能够有效提取这些期刊文献文本中存储的信息和知识,将为构建领域...近十年来,与地球科学相关的数据迎来爆发式增长.这些数据帮助研究人员从不同的领域了解人地系统,其中有相当一部分数据的详细信息由地球科学家发表公布在权威期刊上.如果能够有效提取这些期刊文献文本中存储的信息和知识,将为构建领域相关的高质量知识库提供有力的技术方案.然而,这一技术方案在地球科学领域尚未得到广泛的推广与应用,最大的障碍之一是缺乏公开可用的相关语料库和基线模型.为了填补这一空白,本文从国际期刊Earth System Science Data(ESSD)中获取了600篇文献摘要,并以此构建了地球科学数据语料库(Earth Science Data Corpus,ESDC).据我们所知,ESDC是第一个提供详细细节并开放开源的地学文献语料库,其可以为从大量文献中提取知识和构建领域知识图谱提供专业的训练数据集.ESDC的生成过程既考虑了时空实体的上下文语境特征,也考虑了学术文献的语言特征.此外,本文还为ESDC量身定制了标注指南和标注流程,以确保其可靠性.在实验部分,本文对比了零样本学习与少样本学习的ChatGPT模型、生成式的BARTNER模型和判别式的W2NER模型,以评估ESDC在命名实体识别任务中的性能.实验结果表明,BARTNER取得了最高的性能指标.本文还评估了每个模型在各个实体类型上的性能指标.接着,利用训练完成的BARTNER模型在一个更大范围的无标注的文献语料数据中进行模型推理,以自动化地抽取更为广泛和丰富的实体信息.随后,所抽取的实体信息被映射关联到地球科学数据知识图谱.围绕该知识图谱,本文验证了热点研究分析、科学计量分析和知识增强大型语言模型的问答系统等多个下游应用.这些应用证明了ESDC能够为不同学科的科学家提供地球科学数据信息,帮助他们更好地理解和获取数据,促进他们在各自专业领域的进一步探索.展开更多
Over the past ten years,large amounts of original research data related to Earth system science have been made available at a rapidly increasing rate.Such growing data stock helps researchers understand the human-Eart...Over the past ten years,large amounts of original research data related to Earth system science have been made available at a rapidly increasing rate.Such growing data stock helps researchers understand the human-Earth system across different fields.A substantial amount of this data is published by geoscientists as open-access in authoritative journals.If the information stored in this literature is properly extracted,there is significant potential to build a domain knowledge base.However,this potential remains largely unfulfilled in geoscience,with one of the biggest obstacles being the lack of publicly available related corpora and baselines.To fill this gap,the Earth Science Data Corpus(ESDC),an academic text corpus of 600 abstracts,was built from the international journal Earth System Science Data(ESSD).To the best of our knowledge,ESDC is the first corpus with the needed detail to provide a professional training dataset for knowledge extraction and construction of domain-specific knowledge graphs from massive amounts of literature.The production process of ESDC incorporates both the contextual features of spatiotemporal entities and the linguistic characteristics of academic literature.Furthermore,annotation guidelines and procedures tailored for Earth science data are formulated to ensure reliability.ChatGPT with zero-and few-shot prompting,BARTNER generative,and W2NER discriminative models were trained on ESDC to evaluate the performance of the name entity recognition task and showed increasing performance metrics,with the highest achieved by BARTNER.Performance metrics for various entity types output by each model were also assessed.We utilized the trained BARTNER model to perform model inference on a larger unlabeled literature corpus,aiming to automatically extract a broader and richer set of entity information.Subsequently,the extracted entity information was mapped and associated with the Earth science data knowledge graph.Around this knowledge graph,this paper validates multiple downstream applications,including hot topic research analysis,scientometric analysis,and knowledge-enhanced large language model question-answering systems.These applications have demonstrated that the ESDC can provide scientists from different disciplines with information on Earth science data,help them better understand and obtain data,and promote further exploration in their respective professional fields.展开更多
The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals(SDGs)that form part of the United Nations 2030 Sustainable...The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals(SDGs)that form part of the United Nations 2030 Sustainable Development Agenda.In terms of anthropogenic factors threatening the conservation of heritage properties,such a metric aids in the assessment of achievements toward heritage sustainability solving the problem of insufficient data availability.Therefore,in this study,589 cultural World Heritage List(WHL)properties from 115 countries were analyzed,encompassing globally distributed and statistically significant samples of“monuments and groups of buildings”(73.2%),“sites”(19.3%),and“cultural landscapes”(7.5%).Land-cover changes in the WHL properties between 2015 and 2020 were automatically extracted from big data collections of high-resolution satellite imagery accessed via Google Earth Engine using intelligent remote sensing classification.Sustainability indexes(SIs)were estimated for the protection zones of each property,and the results were employed,for the first time,to assess the progress of each country toward SDG Target 11.4.Despite the apparent advances in SIs(10.4%),most countries either exhibited steady(20.0%)or declining(69.6%)SIs due to limited cultural investigations and enhanced negative anthropogenic disturbances.This study confirms that land-cover changes are among serious threats for heritage conservation,with heritage in some countries wherein the need to address this threat is most crucial,and the proposed spatiotemporal monitoring approach is recommended.展开更多
WORLD HERITAGE AND SPACE TECHNOLOGY The Convention Concerning the Protection of the World Cultural and Natural Heritage(WHC),adopted by United Nations Educational,Scientific and Cultural Organization(UNESCO)on Novembe...WORLD HERITAGE AND SPACE TECHNOLOGY The Convention Concerning the Protection of the World Cultural and Natural Heritage(WHC),adopted by United Nations Educational,Scientific and Cultural Organization(UNESCO)on November 16,1972,aims to ensure the identification,protection,conservation,presentation,and transmission to future generations of the world’s cultural and natural heritage.The WHC works toward these goals by emphasizing the Outstanding Universal Value(OUV)of heritage sites and the unique contribution such places can make to conservation and human development agendas.1 As of the end of January 2023,theWHC has been signed by 194 state parties,covering 1,157 sites(including 900 cultural,218 natural,and 39 mixed properties),55 of which are considered to be in danger.These sites,totaling an area of more than 370 million hectares are designated as World Heritage(WH)sites(https://whc.unesco.org/en/list/).WH sites have played a significant role in the sustainable development of society globally and helped effectively maintain and preserve the cultural diversity and global biodiversity of the Earth.展开更多
基金funded by the National Natural Science Foundation of China[grant number 42250610212]the China Scholarship Council[No.202106270150].
文摘Satellite Interferometric Synthetic Aperture Radar(InSAR)is widely used for topographic,geological and natural resource investigations.However,most of the existing InSAR studies of ground deformation are based on relatively short periods and single sensors.This paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets,to address cases when partial overlaps and/or temporal gaps exist.A new Power Exponential Knothe Model(PEKM)fits and fuses overlaps in the deformation curves,while a Long Short-Term Memory(LSTM)neural network predicts and fuses any temporal gaps in the series.Taking the city of Wuhan(China)as experiment area,COSMO-SkyMed(2011-2015),TerraSAR-X(2015-2019)and Sentinel-1(2019-2021)SAR datasets were fused to map long-term surface deformation over the last decade.An independent 2011-2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for comparison.The correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset.The correlation coefficient of the overall results is 0.78,which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation curve.The experimental results are consistent with existing studies of surface deformation at Wuhan,demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.
基金supported by the European Space Agency(ESA)and the National Remote Sensing Center(NRSCC)-Ministry of Science and Technology(MOST)of the P.R.China under[grant number 58113]ESA[contract number 4000135360/21/I-NB,grant numbers 190791 and PP0085498]+3 种基金the German Aerospace Center(DLR)[grant number MTH3764]the Italian Space Agency(ASI)[COSMO-SkyMed license WUHAN-CSK]Planet Labs PBC under the Education and Research Program[grant number 412519]the National Natural Science Foundation of China[grant number 42250610212].
文摘Optical and Synthetic Aperture Radar(SAR)remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications,yet further advances are viable through the exploitation of novel sensor data and imaging modes,big data and high-performance computing,advanced and automated analysis methods.This paper showcases the main research avenues in this field,with a focus on archaeological prospection and heritage site protection.Six demonstration use-cases with a wealth of heritage asset types(e.g.excavated and still buried archaeological features,standing monuments,natural reserves,burial mounds,paleo-channels)and respective scientific research objectives are presented:the Ostia-Portus area and the wider Province of Rome(Italy),the city of Wuhan and the Jiuzhaigou National Park(China),and the Siberian“Valley of the Kings”(Russia).Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite(e.g.Copernicus Sentinels and ESA Third Party Missions)and aerial(e.g.Unmanned Aerial Vehicles,UAV)platforms,as well as field-based evidence and ground truth,auxiliary topographic data,Digital Elevation Models(DEM),and monitoring data from geodetic campaigns and networks.The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes,identify threats to cultural heritage assets due to ground instability and urban development in large metropolises,and monitor post-disaster impacts in natural reserves.
文摘近十年来,与地球科学相关的数据迎来爆发式增长.这些数据帮助研究人员从不同的领域了解人地系统,其中有相当一部分数据的详细信息由地球科学家发表公布在权威期刊上.如果能够有效提取这些期刊文献文本中存储的信息和知识,将为构建领域相关的高质量知识库提供有力的技术方案.然而,这一技术方案在地球科学领域尚未得到广泛的推广与应用,最大的障碍之一是缺乏公开可用的相关语料库和基线模型.为了填补这一空白,本文从国际期刊Earth System Science Data(ESSD)中获取了600篇文献摘要,并以此构建了地球科学数据语料库(Earth Science Data Corpus,ESDC).据我们所知,ESDC是第一个提供详细细节并开放开源的地学文献语料库,其可以为从大量文献中提取知识和构建领域知识图谱提供专业的训练数据集.ESDC的生成过程既考虑了时空实体的上下文语境特征,也考虑了学术文献的语言特征.此外,本文还为ESDC量身定制了标注指南和标注流程,以确保其可靠性.在实验部分,本文对比了零样本学习与少样本学习的ChatGPT模型、生成式的BARTNER模型和判别式的W2NER模型,以评估ESDC在命名实体识别任务中的性能.实验结果表明,BARTNER取得了最高的性能指标.本文还评估了每个模型在各个实体类型上的性能指标.接着,利用训练完成的BARTNER模型在一个更大范围的无标注的文献语料数据中进行模型推理,以自动化地抽取更为广泛和丰富的实体信息.随后,所抽取的实体信息被映射关联到地球科学数据知识图谱.围绕该知识图谱,本文验证了热点研究分析、科学计量分析和知识增强大型语言模型的问答系统等多个下游应用.这些应用证明了ESDC能够为不同学科的科学家提供地球科学数据信息,帮助他们更好地理解和获取数据,促进他们在各自专业领域的进一步探索.
基金supported by the National Natural Science Foundation of China(Grant No.42090011)。
文摘Over the past ten years,large amounts of original research data related to Earth system science have been made available at a rapidly increasing rate.Such growing data stock helps researchers understand the human-Earth system across different fields.A substantial amount of this data is published by geoscientists as open-access in authoritative journals.If the information stored in this literature is properly extracted,there is significant potential to build a domain knowledge base.However,this potential remains largely unfulfilled in geoscience,with one of the biggest obstacles being the lack of publicly available related corpora and baselines.To fill this gap,the Earth Science Data Corpus(ESDC),an academic text corpus of 600 abstracts,was built from the international journal Earth System Science Data(ESSD).To the best of our knowledge,ESDC is the first corpus with the needed detail to provide a professional training dataset for knowledge extraction and construction of domain-specific knowledge graphs from massive amounts of literature.The production process of ESDC incorporates both the contextual features of spatiotemporal entities and the linguistic characteristics of academic literature.Furthermore,annotation guidelines and procedures tailored for Earth science data are formulated to ensure reliability.ChatGPT with zero-and few-shot prompting,BARTNER generative,and W2NER discriminative models were trained on ESDC to evaluate the performance of the name entity recognition task and showed increasing performance metrics,with the highest achieved by BARTNER.Performance metrics for various entity types output by each model were also assessed.We utilized the trained BARTNER model to perform model inference on a larger unlabeled literature corpus,aiming to automatically extract a broader and richer set of entity information.Subsequently,the extracted entity information was mapped and associated with the Earth science data knowledge graph.Around this knowledge graph,this paper validates multiple downstream applications,including hot topic research analysis,scientometric analysis,and knowledge-enhanced large language model question-answering systems.These applications have demonstrated that the ESDC can provide scientists from different disciplines with information on Earth science data,help them better understand and obtain data,and promote further exploration in their respective professional fields.
基金We acknowledge the joint funding from the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals(grant no.CBAS2022IRP06)Jiangxi Provincial Technology Innovation Guidance Program(National Science and Technology Award Reserve Project Cultivation Program)(grant no.20212AEI91006)National Natural Science Foundation of China(NSFC)(grant no.42271327).
文摘The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals(SDGs)that form part of the United Nations 2030 Sustainable Development Agenda.In terms of anthropogenic factors threatening the conservation of heritage properties,such a metric aids in the assessment of achievements toward heritage sustainability solving the problem of insufficient data availability.Therefore,in this study,589 cultural World Heritage List(WHL)properties from 115 countries were analyzed,encompassing globally distributed and statistically significant samples of“monuments and groups of buildings”(73.2%),“sites”(19.3%),and“cultural landscapes”(7.5%).Land-cover changes in the WHL properties between 2015 and 2020 were automatically extracted from big data collections of high-resolution satellite imagery accessed via Google Earth Engine using intelligent remote sensing classification.Sustainability indexes(SIs)were estimated for the protection zones of each property,and the results were employed,for the first time,to assess the progress of each country toward SDG Target 11.4.Despite the apparent advances in SIs(10.4%),most countries either exhibited steady(20.0%)or declining(69.6%)SIs due to limited cultural investigations and enhanced negative anthropogenic disturbances.This study confirms that land-cover changes are among serious threats for heritage conservation,with heritage in some countries wherein the need to address this threat is most crucial,and the proposed spatiotemporal monitoring approach is recommended.
基金supported by the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals(grant CBAS2022IRP09)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(grant 2023135).
文摘WORLD HERITAGE AND SPACE TECHNOLOGY The Convention Concerning the Protection of the World Cultural and Natural Heritage(WHC),adopted by United Nations Educational,Scientific and Cultural Organization(UNESCO)on November 16,1972,aims to ensure the identification,protection,conservation,presentation,and transmission to future generations of the world’s cultural and natural heritage.The WHC works toward these goals by emphasizing the Outstanding Universal Value(OUV)of heritage sites and the unique contribution such places can make to conservation and human development agendas.1 As of the end of January 2023,theWHC has been signed by 194 state parties,covering 1,157 sites(including 900 cultural,218 natural,and 39 mixed properties),55 of which are considered to be in danger.These sites,totaling an area of more than 370 million hectares are designated as World Heritage(WH)sites(https://whc.unesco.org/en/list/).WH sites have played a significant role in the sustainable development of society globally and helped effectively maintain and preserve the cultural diversity and global biodiversity of the Earth.
