Pub Date : 2022-12-01DOI: 10.1016/j.jag.2022.103115
Wenhao Yu, Qinghong Huang
{"title":"A deep encoder-decoder network for anomaly detection in driving trajectory behavior under spatio-temporal context","authors":"Wenhao Yu, Qinghong Huang","doi":"10.1016/j.jag.2022.103115","DOIUrl":"https://doi.org/10.1016/j.jag.2022.103115","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"34 1","pages":"103115"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79774330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1016/j.jag.2022.103111
Jisoo Park, Yong K. Cho, S. Kim
{"title":"Deep learning-based UAV image segmentation and inpainting for generating vehicle-free orthomosaic","authors":"Jisoo Park, Yong K. Cho, S. Kim","doi":"10.1016/j.jag.2022.103111","DOIUrl":"https://doi.org/10.1016/j.jag.2022.103111","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"121 1","pages":"103111"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89359451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-17DOI: 10.48550/arXiv.2209.08286
Xuke Hu, Yeran Sun, J. Kersten, Zhiyong Zhou, Friederike Klan, H. Fan
A vast amount of geographic information exists in natural language texts, such as tweets and news. Extracting geographic information from texts is called Geoparsing, which includes two subtasks: toponym recognition and toponym disambiguation, i.e., to identify the geospatial representations of toponyms. This paper focuses on toponym disambiguation, which is usually approached by toponym resolution and entity linking. Recently, many novel approaches have been proposed, especially deep learning-based approaches, such as CamCoder, GENRE, and BLINK. In this paper, a spatial clustering-based voting approach that combines several individual approaches is proposed to improve SOTA performance in terms of robustness and generalizability. Experiments are conducted to compare a voting ensemble with 20 latest and commonly-used approaches based on 12 public datasets, including several highly ambiguous and challenging datasets (e.g., WikToR and CLDW). The datasets are of six types: tweets, historical documents, news, web pages, scientific articles, and Wikipedia articles, containing in total 98,300 places across the world. The results show that the voting ensemble performs the best on all the datasets, achieving an average Accuracy@161km of 0.86, proving the generalizability and robustness of the voting approach. Also, the voting ensemble drastically improves the performance of resolving fine-grained places, i.e., POIs, natural features, and traffic ways.
{"title":"How can voting mechanisms improve the robustness and generalizability of toponym disambiguation?","authors":"Xuke Hu, Yeran Sun, J. Kersten, Zhiyong Zhou, Friederike Klan, H. Fan","doi":"10.48550/arXiv.2209.08286","DOIUrl":"https://doi.org/10.48550/arXiv.2209.08286","url":null,"abstract":"A vast amount of geographic information exists in natural language texts, such as tweets and news. Extracting geographic information from texts is called Geoparsing, which includes two subtasks: toponym recognition and toponym disambiguation, i.e., to identify the geospatial representations of toponyms. This paper focuses on toponym disambiguation, which is usually approached by toponym resolution and entity linking. Recently, many novel approaches have been proposed, especially deep learning-based approaches, such as CamCoder, GENRE, and BLINK. In this paper, a spatial clustering-based voting approach that combines several individual approaches is proposed to improve SOTA performance in terms of robustness and generalizability. Experiments are conducted to compare a voting ensemble with 20 latest and commonly-used approaches based on 12 public datasets, including several highly ambiguous and challenging datasets (e.g., WikToR and CLDW). The datasets are of six types: tweets, historical documents, news, web pages, scientific articles, and Wikipedia articles, containing in total 98,300 places across the world. The results show that the voting ensemble performs the best on all the datasets, achieving an average Accuracy@161km of 0.86, proving the generalizability and robustness of the voting approach. Also, the voting ensemble drastically improves the performance of resolving fine-grained places, i.e., POIs, natural features, and traffic ways.","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"139 1","pages":"103191"},"PeriodicalIF":0.0,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83019238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.48550/arXiv.2206.07468
W. Shi, Pengxin Chen, Muyang Wang, Sheng Bao, Haodong Xiang, Yue Yu, Daping Yang
Constructing colorized point clouds from mobile laser scanning and images is a fundamental work in surveying and mapping. It is also an essential prerequisite for building digital twins for smart cities. However, existing public datasets are either in relatively small scales or lack accurate geometrical and color ground truth. This paper documents a multisensorial dataset named PolyU-BPCoMA which is distinctively positioned towards mobile colorized mapping. The dataset incorporates resources of 3D LiDAR, spherical imaging, GNSS and IMU on a backpack platform. Color checker boards are pasted in each surveyed area as targets and ground truth data are collected by an advanced terrestrial laser scanner (TLS). 3D geometrical and color information can be recovered in the colorized point clouds produced by the backpack system and the TLS, respectively. Accordingly, we provide an opportunity to benchmark the mapping and colorization accuracy simultaneously for a mobile multisensorial system. The dataset is approximately 800 GB in size covering both indoor and outdoor environments. The dataset and development kits are available at https://github.com/chenpengxin/
{"title":"PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System","authors":"W. Shi, Pengxin Chen, Muyang Wang, Sheng Bao, Haodong Xiang, Yue Yu, Daping Yang","doi":"10.48550/arXiv.2206.07468","DOIUrl":"https://doi.org/10.48550/arXiv.2206.07468","url":null,"abstract":"Constructing colorized point clouds from mobile laser scanning and images is a fundamental work in surveying and mapping. It is also an essential prerequisite for building digital twins for smart cities. However, existing public datasets are either in relatively small scales or lack accurate geometrical and color ground truth. This paper documents a multisensorial dataset named PolyU-BPCoMA which is distinctively positioned towards mobile colorized mapping. The dataset incorporates resources of 3D LiDAR, spherical imaging, GNSS and IMU on a backpack platform. Color checker boards are pasted in each surveyed area as targets and ground truth data are collected by an advanced terrestrial laser scanner (TLS). 3D geometrical and color information can be recovered in the colorized point clouds produced by the backpack system and the TLS, respectively. Accordingly, we provide an opportunity to benchmark the mapping and colorization accuracy simultaneously for a mobile multisensorial system. The dataset is approximately 800 GB in size covering both indoor and outdoor environments. The dataset and development kits are available at https://github.com/chenpengxin/","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"64 1","pages":"102962"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73831681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-03DOI: 10.48550/arXiv.2205.01380
Jiaxin Li, D. Hong, Lianru Gao, Jing Yao, Ke-xin Zheng, Bing Zhang, J. Chanussot
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way. With the joint utilization of EO data, much research on multimodal RS data fusion has made tremendous progress in recent years, yet these developed traditional algorithms inevitably meet the performance bottleneck due to the lack of the ability to comprehensively analyse and interpret these strongly heterogeneous data. Hence, this non-negligible limitation further arouses an intense demand for an alternative tool with powerful processing competence. Deep learning (DL), as a cutting-edge technology, has witnessed remarkable breakthroughs in numerous computer vision tasks owing to its impressive ability in data representation and reconstruction. Naturally, it has been successfully applied to the field of multimodal RS data fusion, yielding great improvement compared with traditional methods. This survey aims to present a systematic overview in DL-based multimodal RS data fusion. More specifically, some essential knowledge about this topic is first given. Subsequently, a literature survey is conducted to analyse the trends of this field. Some prevalent sub-fields in the multimodal RS data fusion are then reviewed in terms of the to-be-fused data modalities, i.e., spatiospectral, spatiotemporal, light detection and ranging-optical, synthetic aperture radar-optical, and RS-Geospatial Big Data fusion. Furthermore, We collect and summarize some valuable resources for the sake of the development in multimodal RS data fusion. Finally, the remaining challenges and potential future directions are highlighted.
{"title":"Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review","authors":"Jiaxin Li, D. Hong, Lianru Gao, Jing Yao, Ke-xin Zheng, Bing Zhang, J. Chanussot","doi":"10.48550/arXiv.2205.01380","DOIUrl":"https://doi.org/10.48550/arXiv.2205.01380","url":null,"abstract":"With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way. With the joint utilization of EO data, much research on multimodal RS data fusion has made tremendous progress in recent years, yet these developed traditional algorithms inevitably meet the performance bottleneck due to the lack of the ability to comprehensively analyse and interpret these strongly heterogeneous data. Hence, this non-negligible limitation further arouses an intense demand for an alternative tool with powerful processing competence. Deep learning (DL), as a cutting-edge technology, has witnessed remarkable breakthroughs in numerous computer vision tasks owing to its impressive ability in data representation and reconstruction. Naturally, it has been successfully applied to the field of multimodal RS data fusion, yielding great improvement compared with traditional methods. This survey aims to present a systematic overview in DL-based multimodal RS data fusion. More specifically, some essential knowledge about this topic is first given. Subsequently, a literature survey is conducted to analyse the trends of this field. Some prevalent sub-fields in the multimodal RS data fusion are then reviewed in terms of the to-be-fused data modalities, i.e., spatiospectral, spatiotemporal, light detection and ranging-optical, synthetic aperture radar-optical, and RS-Geospatial Big Data fusion. Furthermore, We collect and summarize some valuable resources for the sake of the development in multimodal RS data fusion. Finally, the remaining challenges and potential future directions are highlighted.","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"119 1","pages":"102926"},"PeriodicalIF":0.0,"publicationDate":"2022-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81209555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1016/j.jag.2022.102762
Baili Chen, Hongwei Zheng, Lili Wang, O. Hellwich, Chunbo Chen, Liao Yang, Tie Liu, G. Luo, A. Bao, X. Chen
{"title":"A joint learning Im-BiLSTM model for incomplete time-series Sentinel-2A data imputation and crop classification","authors":"Baili Chen, Hongwei Zheng, Lili Wang, O. Hellwich, Chunbo Chen, Liao Yang, Tie Liu, G. Luo, A. Bao, X. Chen","doi":"10.1016/j.jag.2022.102762","DOIUrl":"https://doi.org/10.1016/j.jag.2022.102762","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"62 1","pages":"102762"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74091614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1016/j.jag.2022.102748
Alycia Leonard, Scot Wheeler, M. McCulloch
{"title":"Power to the people: Applying citizen science and computer vision to home mapping for rural energy access","authors":"Alycia Leonard, Scot Wheeler, M. McCulloch","doi":"10.1016/j.jag.2022.102748","DOIUrl":"https://doi.org/10.1016/j.jag.2022.102748","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"15 1","pages":"102748"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84362315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1016/j.jag.2022.102763
Luiza Tymińska-Czabańska, P. Hawryło, J. Socha
{"title":"Assessment of the effect of stand density on the height growth of Scots pine using repeated ALS data","authors":"Luiza Tymińska-Czabańska, P. Hawryło, J. Socha","doi":"10.1016/j.jag.2022.102763","DOIUrl":"https://doi.org/10.1016/j.jag.2022.102763","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"44 1","pages":"102763"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82230306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1016/j.jag.2022.102721
R. Ramirez, Gi-Jun Lee, Shin-Kyu Choi, T. Kwon, Youngchul Kim, H. Ryu, Sangyoung Kim, Byungeol Bae, Chiho Hyun
{"title":"Monitoring of construction-induced urban ground deformations using Sentinel-1 PS-InSAR: The case study of tunneling in Dangjin, Korea","authors":"R. Ramirez, Gi-Jun Lee, Shin-Kyu Choi, T. Kwon, Youngchul Kim, H. Ryu, Sangyoung Kim, Byungeol Bae, Chiho Hyun","doi":"10.1016/j.jag.2022.102721","DOIUrl":"https://doi.org/10.1016/j.jag.2022.102721","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"3 1","pages":"102721"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75167323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-01DOI: 10.1016/j.jag.2022.102713
L. Lv, Tao Chen, J. Dou, A. Plaza
{"title":"A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping","authors":"L. Lv, Tao Chen, J. Dou, A. Plaza","doi":"10.1016/j.jag.2022.102713","DOIUrl":"https://doi.org/10.1016/j.jag.2022.102713","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"108 1","pages":"102713"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81887517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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