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研究生:蘇立澤
研究生(外文):Li-TseSu
論文名稱:基於空間排序指標之遙測影像智慧推薦機制
論文名稱(外文):Smart Remote Sensing Image Recommendation Mechanism based on Spatial Ranking Indicators
指導教授:洪榮宏洪榮宏引用關係
指導教授(外文):Jung-Hong Hong
學位類別:博士
校院名稱:國立成功大學
系所名稱:測量及空間資訊學系
學門:工程學門
學類:測量工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:190
中文關鍵詞:遙測影像詮釋資料空間排序機制遙測影像排序推薦
外文關鍵詞:RS-image metadataspatial rankingRS-image spatial recommendation
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近年來隨著感測器技術及網際網路服務機制的快速演進,愈來愈多的影像平台或是數位典藏機構透過網際網路提供遙測影像的流通服務。在各式各樣的空間資料應用領域中,遙測影像已經成為地理資訊應用不可或缺的資料項目,尤其在大數據之概念及架構成型後,遙測影像之應用層面將更為寬廣,使用量也將大幅增加。相較於向量格式空間資料,遙測影像中具有連續涵蓋、多版本及高更新頻率之特性,可針對使用者感興趣的區域提供更為全面且即時的判斷參考。面對現今數量爆炸成長的遙測影像流通環境,使用者面對的挑戰是如何由大量符合空間約制條件的影像中找到合適應用的遙測影像,如同網際網路搜尋引擎面對如何回應大量搜尋網頁資料之問題,有效的排序推薦機制將可大幅的降低使用者挑選遙測影像的困難與負擔。
根據ISO遙測影像詮釋資料標準項目,其中可以做為遙測影像排序推薦的基準很多,包含空間條件、時間條件、解析度條件、頻譜條件、雲遮蔽度等。因為涉及是否可提供感興趣區域之狀態,空間條件被視為是最關鍵且必要考量的條件。但由於影像並非依照感興趣區域客製化拍攝,這樣的涵蓋關係通常都有程度的分別,而有效的空間排序推薦機制必須可呈現並量化評比這樣的程度化差異,提供使用者後續存取實體影像的順序,以大幅減少耗費的時間及資源。當可供使用的遙測影像數量成長愈快,智慧化推薦與選用機制之重要性將隨之增加。
本研究提出一個創新的遙測影像空間搜尋引擎(Location-based RS-image Finding Engine, LIFE) ,透過內建之影像推薦機制,系統將可依使用者指定的感興趣區域(Area of Interest, AOI),進行遙測影像資料之篩選、排序及推薦,減低使用者挑選影像之困難度。LIFE框架中包含三個主要的元件,第一個元件是叢集式的遙測影像索引架構(cluster-based RS-image index structure),目標為儲存並應用遙測影像之詮釋資料,透過空間索引架構的輔助提供快速搜尋與比對合格的候選影像;第二個元件為空間排序推薦量化指標,包括兩個基於使用者選取行為而模式化之指標:AS(Available Space)指標係以感興趣區域之空間延伸性(Extensibility)設計,IE(Image Extension)指標則是以居中性(Centrality)為考量而設計。最後包括以主成份分析而整合AS、IE兩個指標的INDEX指標,可在兼顧兩類特性之狀況下提供影像選取之推薦。除發展量化之推薦機制外,本研究並發展遙測影像的評分平台,蒐集使用者對於影像選取結果之評分,以驗證本研究發展機制之推薦成果。本研究透過NDCG(normalized discounted cumulative gain)及其他多樣的統計量具體比較不同指標之表現行為,並經過與使用者評分結果之比較評估實用性。分析結果顯示空間排序推薦指標AS及IE均可基於其特性而提出有效的推薦成果,但兩者之排序結果並不相同,因此各有其優點與缺陷,僅考量單一特性可能產生不滿足另一特性之狀況,甚至可能有利於特定規格的影像。整合AS及IE指標的INDEX指標可發揮具備兩者觀點的綜效,提供較佳排序建議,無論對於混合或是單一類別的遙測影像平台,都可達到更貼近使用者需求的成果。相關成果均經過與使用者評分成果之綜合比對,顯示本機制之可行性及可替代人類使用者進行遙測影像選取之潛力。
Following the rapid evolution of sensor technology and Internet service mechanisms in recent years, an increasing number of image platforms or digital collection agencies are providing circulation services related to remote sensing images. In various fields of spatial data application, remote sensing images (RS-images) have become an indispensable resource for providing information about the continuously changing reality. In particular, the formation of the concept and structure of big data will contribute to RS-images being used at a greater scope and scale. Compared with the spatial data of vector format, RS-images are characterized by continuous coverage, multiple versions, and a high update frequency, which can provide users with a more comprehensive and real-time ground truth for the areas of interest (AOIs). In the context of the explosive growth of the circulation environment for RS-images, users face the challenge of finding and selecting suitable RS-images from a large number of images under given spatial constraints. Similar to how Internet search engines face the problem of responding to large quantities of search information, an effective ranking mechanism can considerably reduce the difficulty and burden for users in selecting RS-images. Many items included in the ISO metadata standard (ISO 19115-2:2019 Geographic Information Metadata Part 2: Extensions for Acquisition and Processing, https://www.iso.org/obp/ui/#iso:std:iso:19115:-2:en) for RS-images, such as spatial, temporal, resolution, spectral, and cloud coverage can serve as the basis of ranking and recommendation. Spatial aspect is considered the most crucial constraint as it determines if the image may qualify for providing information about the Area of Interests. However, because image acquisition cannot be customized in accordance with the AOIs, such coverage relationships generally differ. Thus, an effective spatial ranking recommendation mechanism must be capable of presenting and quantifying these degrees of difference to provide users with subsequent access to the physical images’ order, thereby resulting in significant time and resource savings. As the number of available RS-images grows rapidly, the importance of intelligent recommendation mechanisms for RS-image will increase accordingly. In this study, an innovative location-based RS-image finding engine (LIFE) was formulated. Through its built-in image recommendation mechanism, the system is capable of filtering, ranking, and recommending RS-images based on user-designated AOIs, thereby making it easier for users to select images. The LIFE framework comprises three major components. The first component is the cluster-based RS-image index structure, whose goal is to store and process the spatial coverage information parsed from RS-image metadata, where the spatial index framework then provides a rapid search and filter of the qualified candidate images. The second component is the quantitative indicators for spatial ranking and recommendation, which includes two indicators that were modeled according to users’ selection behavior. The available space (AS) indicator was based on the spatial extensibility of AOIs to the boundaries of RS-images, and the image extension (IE) indicator was designed in accordance with consideration of centrality for the AOIs contained by the RS-images. The final component is the INDEX indicator that integrates the ranking abilities of AS and IE indicators by means of principal component analysis. The INDEX-based mechanism provides a one-dimension ranking and recommendation for RS-image selection considering both characteristics from AS and IE indicators. In addition to the quantitative spatial ranking indicators and framework for RS-image recommendation, a scoring platform to collect user ratings according to the preferences for RS-image selection was also developed to verify the recommendation results of the developed indicators and mechanism. In this study, the normalized discounted cumulative gain (NDCG), among other statistics, were employed to specifically compare the performance of different indicators, and the practicability of the developed mechanism was evaluated by analysis of collected user scoring results. The analysis demonstrated that the spatial ranking and recommendation indicators AS and IE can both provide effective recommendation results based on their characteristics. However, the different ranking results provided by AS and IE suggested each method has its unique advantages and disadvantages. Thus, ranking by AS indicator may result in a situation of neglecting the characteristics of IE indicator, even though such situation may favor certain type of RS-image, for example, RS-images with large coverage. The INDEX indicator from the linear combination of AS and IE indicators can yield a synergistic effect that combines both perspectives and provide better spatial ranking recommendations more consistent with users’ preferences for RS-image data resources of hybrid types or single type. Furthermore, all experimental results were verified and validated by comprehensive user preference analysis based on collected user rating data, it demonstrated the potential of the proposed mechanism in providing a feasible alternative approach in RS-image selection for the public lack of RS-image application expertise and experience.
Content
中文摘要 I
ABSTRACT III
誌謝 VII
Content VIII
List of Tables XII
List of Figures XIV
Chapter 1 Introduction 1
1.1 Motivation 5
1.2 Overview of the Dissertation 8
1.2.1 LIFE Framework with AS and IE indicators 9
1.2.2 INDEX Indicator 12
1.2.3 User Score and NDCG Evaluations 13
1.3 Organization of the Dissertation 14
Chapter 2 Literature Review 15
2.1 Textual and Spatial Ranking 16
2.2 Web-based RS-image Browsing Services 21
2.2.1 Search Oriented Platforms 21
2.2.2 Platforms of WebGIS Function 29
2.3 The Spatial Constraint for Geospatial Data Query 33
2.4 The Design Strategies of RS-image Ranking Recommendation Indicator 38
2.4.1 Web Search Engine Ranking 38
2.4.2 Geospatial Information Retrieval 40
2.4.3 Recommender System 43
2.5 Variable Reduction Methods 46
2.5.1 Factor Analysis 47
2.5.2 Principal Component Analysis 52
2.5.3 Comparison of FA and PCA 53
Chapter 3 Available Space (AS) and Image Extension (IE) Indicators 57
3.1 Conceptual Framework of LIFE 59
3.2 Cluster-based Index Structure 60
3.3 Available Space (AS) Indicator 63
3.4 Image Extension (IE) Indicator 68
3.5 Normalization of Indicator Values 70
3.6 Fundamental Behavioral Analysis 72
3.6.1 Ranking of Regularly Distributed Images 73
3.6.2 General Ranking Behaviors of AS and IE Indicators 78
3.6.3 Comparison of Various Index Structure 83
3.7 Discussion 84
Chapter 4 The Index Indicator 86
4.1 Expanded Conceptual System Framework 87
4.2 Proposed Method for the INDEX Indicator 90
4.3 Fundamental Behavioral Analyses 95
4.3.1 AS Indicator Score According to the Sorting Pattern 96
4.3.2 IE Indicator Score-Based Sorting Pattern 99
4.3.3 INDEX Indicator Score-Based Sorting Pattern 102
4.4 Discussion 104
Chapter 5 User Score and Normalized Discounted Cumulative Gain Evaluations 106
5.1 User Score Analyses 107
5.1.1 Image Simulation for User Scoring 108
5.1.2 User Score Data Collecting Platform 110
5.1.3 User Rating Scores Analysis 112
5.2 Comparisons of the User Scores for Four Indicators 117
5.3 NDCG 120
5.3.1 Evaluation Methodology 120
5.3.2 Data Preprocessing Procedures 122
5.3.3 Comparison of AS, IE and Hausdorff distance Indicators 128
5.3.4 Impact of Various Categories of RS-images 131
5.3.5 Comparison Adding the INDEX Indicator 134
5.3.6 Relationship between NDCG and RS-image Types 135
5.3.7 NDCG Improvement Rate Analysis 136
5.3.8 Comparisons of NDCG for Each Type of RS-image 146
5.3.9 Evaluations of the INDEX Improvement Rates According to RS-image Type 158
5.4 Evaluation of Other Statistical Criteria 159
5.4.1 Precision Evaluation 163
5.4.2 Recall Evaluation 163
5.4.3 Precision-Recall Curve Evaluation 164
5.4.4 AP Evaluation 165
5.4.5 Mean AP Evaluation 166
5.5 Discussion 167
Chapter 6 Conclusions and Future Work 169
6.1 Conclusions 170
6.2 Future Work 174
6.2.1 Temporal Constraint RS-image Recommendation 174
6.2.2 Fuzzy Mining for RS-image Spatial Recommendation 175
6.2.3 Machine Learning and Other Interesting Issues 177
References 178
References
[1]H. J. Kramer, Observation of the Earth and its Environment: Survey of Missions and Sensors. Springer Science & Business Media, 2002.
[2]N. Skytland. What is NASA doing with Big Data today? https://open.nasa.gov/blog/what-is-nasa-doing-with-big-data-today/ (accessed 0918, 2019).
[3]P. Liu, L. Di, Q. Du, and L. Wang, Remote sensing big data: theory, methods and applications, Remote Sensing, 2018.
[4]X. Tan et al., Parallel agent-as-a-service (p-aaas) based geospatial service in the cloud, Remote Sensing, vol. 9, no. 4, p. 382, 2017.
[5]K. Wu, Q. Du, Y. Wang, and Y. Yang, Supervised sub-pixel mapping for change detection from remotely sensed images with different resolutions, Remote Sensing, vol. 9, no. 3, p. 284, 2017.
[6]Z. Ding, X. Liao, F. Su, and D. Fu, Mining Coastal Land Use Sequential Pattern and Its Land Use Associations Based on Association Rule Mining, Remote Sensing, vol. 9, no. 2, p. 116, 2017.
[7]H. Li, R. Hong, S. Zhu, and Y. Ge, Point-of-interest recommender systems: A separate-space perspective, in 2015 IEEE International Conference on Data Mining, 2015: IEEE, pp. 231-240.
[8]P. Kosmides et al., Providing recommendations on location-based social networks, Journal of Ambient Intelligence and Humanized Computing, vol. 7, no. 4, pp. 567-578, 2016.
[9]W. Wang, H. Yin, S. Sadiq, L. Chen, M. Xie, and X. Zhou, SPORE: A sequential personalized spatial item recommender system, in 2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016: IEEE, pp. 954-965.
[10]D. Lowe and A. Mitchell, Status report on NASA’s earth observing data and information system (EOSDIS), in Proceedings of the 42nd Meeting of the Working Group on Information Systems & Services, Frascati, Italy, 2016, pp. 19-22.
[11]U. E. R. Observation and a. S. E. Center. National Satellite Land Remote Sensing Data Archive Report. https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NSLRSDA_Report_February_2020.pdf (accessed 0525, 2020).
[12]J. Behnke, A. Mitchell, and H. Ramapriyan, NASA’s Earth Observing Data and Information System–Near-Term Challenges, 2019. [Online]. Available: https://datascience.codata.org/article/10.5334/dsj-2019-040/.
[13]D. M. Tralli, R. G. Blom, V. Zlotnicki, A. Donnellan, and D. L. Evans, Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 59, no. 4, pp. 185-198, 2005.
[14]J. F. Moses, F. Lindsay, and J. Behnke, Past and Future Operations Concepts of NASA's Earth Science Data and Information System, presented at the IN51F-0695 Fall AGU Meeting, San Francisco, CA, December 9-13, 2019, 2019.
[15]A. U. Frank, Qualitative spatial reasoning: Cardinal directions as an example, International Journal of Geographical Information Science, vol. 10, no. 3, pp. 269-290, 1996.
[16]19107 Geographic information-Spatial schema, ISO, 2003.
[17]19107:2019 Geographic information — Spatial schema, ISO, 2019. [Online]. Available: https://www.iso.org/standard/66175.html
[18]19136 geographic information–geography markup language, ISO, 2007.
[19]19136-1:2020 Geographic information — Geography Markup Language (GML) — Part 1: Fundamentals, ISO, 2020. [Online]. Available: https://www.iso.org/standard/75676.html
[20]Simple Feature Access - Part 2: SQL Option, O. G. Consortium, 2006. [Online]. Available: http://portal.opengeospatial.org/files/?artifact_id=25354
[21]K. K. Pandey and N. Pradhan, Internet Search Engine: Performance Evaluating the Google, Yahoo and Bing Web Search Engine based on their Searching Capabilities, 2018.
[22]M. Asmaran, Quantitative & Qualitative Evaluation of Three Search Engines (Google, Yahoo, and Bing), American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), vol. 26, no. 2, pp. 97-106, 2016.
[23]R. C. Balabantaray, M. Swain, and B. Sahoo, Evaluation of web search engine based on ranking of results and its features, International Journal of Information and Communication Technology, vol. 10, no. 4, pp. 392-405, 2017.
[24]I. Rahim, H. Mushtaq, and S. Ahmad, Evaluation of Search Engines using Advanced Search: Comparative analysis of Yahoo and Bing, Library Philosophy and Practice, pp. 1-12, 2019.
[25]S. Brin and L. Page, The anatomy of a large-scale hypertextual web search engine, 1998.
[26]W. Yang and P. Zheng, An improved pagerank algorithm based on time feedback and topic similarity, in 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2016: IEEE, pp. 534-537.
[27]W. Zheng, S. Mo, P. Duan, and X. Jin, An improved pagerank algorithm based on fuzzy C-means clustering and information entropy, in 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE), 2017: IEEE, pp. 615-618.
[28]F. Hasan, K. K. Ze, R. Razali, A. Buhari, and E. Tadiwa, An IMPROVED PAGERANK ALGORITHM BASED ON A HYBRID APPROACH, Science Proceedings Series, vol. 2, no. 1, pp. 17-21, 2020.
[29]T. Haveliwala, S. Kamvar, and G. Jeh, An analytical comparison of approaches to personalizing pagerank, Stanford, 2003.
[30]D. Fogaras, B. Rácz, K. Csalogány, and T. Sarlós, Towards scaling fully personalized pagerank: Algorithms, lower bounds, and experiments, Internet Mathematics, vol. 2, no. 3, pp. 333-358, 2005.
[31]B. Bahmani, A. Chowdhury, and A. Goel, Fast incremental and personalized pagerank, Proceedings of the VLDB Endowment, vol. 4, no. 3, pp. 173-184, 2010.
[32]S. Park, W. Lee, B. Choe, and S. Lee, A Survey on Personalized PageRank Computation Algorithms, IEEE Access, vol. 7, pp. 163049-163062, 2019.
[33]H. Tang et al., TensorFlow Solver for Quantum PageRank in Large-Scale Networks, arXiv preprint arXiv:2003.04930, 2020.
[34]B. Martins, M. J. Silva, and L. Andrade, Indexing and Ranking in GeoIR Systems, in In Proc. of the 2nd Int. Workshop on Geo-IR (GIR), 2005.
[35]R. R. Larson and P. Frontiera, Geographic Information Retrieval (GIR) Ranking Methods for Digital Libraries, Digital Libraries, 2004. Proceedings of the 2004 Joint ACM/IEEE Conference on, pp. 415-, 2004.
[36]S. Zhao, T. Zhao, H. Yang, M. R. Lyu, and I. King, STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation, in Thirtieth AAAI conference on artificial intelligence, 2016.
[37]S. Zhao, I. King, and M. R. Lyu, Geo-Pairwise Ranking Matrix Factorization Model for Point-of-interest Recommendation, presented at the International Conference on Neural Information Processing, Guangzhou, China, 2017.
[38]R. Ding, Z. Chen, and X. Li, Spatial-temporal distance metric embedding for time-specific POI recommendation, IEEE Access, vol. 6, pp. 67035-67045, 2018.
[39]M. J. Egenhofer and R. D. Franzosa, Point-set topological spatial relations, International Journal of Geographical Information System, vol. 5, no. 2, pp. 161-174, 1991.
[40]D. M. Mark, D. Comas, M. J. Egenhofer, S. M. Freundschuh, M. D. Gould, and J. Nunes, Evaluating and Refining Computational Models of Spatial Relations Through Cross-Linguistic Human-Subjects Testing, Lecture Notes in Computer Science, vol. 988, pp. 553-568, 1995.
[41]K. Beard and V. Sharma, Multidimensional ranking for data in digital spatial libraries, International Journal on Digital Libraries, vol. 1, no. 2, pp. 153-160, 1997.
[42]M. Erwig and M. Schneider, Spatio-Temporal Predicates, IEEE Transactions on Knowledge and Data Engineering, vol. 14, no. 4, pp. 881 - 901, 07 August 2002 2002, doi: 10.1109/TKDE.2002.1019220.
[43]G. Janée and J. Frew, Spatial Search, Ranking, and Interoperability, in 27th Annual International ACM SIGIR Conference, Sheffield, U.K., 2004.
[44]C. B. Jones, A. I. Abdelmoty, D. Finch, G. Fu, and S. Vaid, The SPIRIT Spatial Search Engine: Architecture, Ontologies and Spatial Indexing, Proc Third International Conference on Geographic Information Science GIScience 2004, Maryland, USA, Lecture Notes in Computer Science 3234, pp. 125-139, 2004.
[45]G. R. Hjaltason and H. Samet, Ranking in spatial databases, in International Symposium on Spatial Databases, 1995: Springer, pp. 83-95.
[46]R. R. Larson and P. Frontiera, Ranking and Representation for Geographic Information Retrieval, in Extended abstract in SIGIR 2004 Workshop on Geographic Information Retrieval, 2004.
[47]M. v. Kreveld, I. Reinbacher, A. Arampatzis, and R. v. Zwol, Distributed ranking methods for geographic information retrieval, in In 20th European Workshop on Computational Geometry, March 2004., 2004.
[48]A. Markowetz, Y.-Y. Chen, T. Suel, X. Long, and B. Seeger, Design and Implementation of a Geographic Search Engine, in In 8th Int. Workshop on the Web and Databases (WebDB), June 2005., 2005.
[49]DigitalGlobe. DigitalGlobe(MAXAR) company. http://www.digitalglobe.com/products/satellite-imagery (accessed May 24, 2020).
[50]C. Zhang, Multi-sensor System Applications in the Everglades Ecosystem. CRC Press, 2020.
[51]M. F. Goodchild, P. Fu, and P. M. Rich, Geographic information sharing: the case of the Geospatial One-Stop portal, Annals of the Association of American Geographers, vol. 97, no. 2, pp. 250-266, 2007.
[52]U. S. B. Library, The Alexandria Digital Research Library (ADRL), ed, 2014.
[53]J. Frew et al., The alexandria digital library architecture, in International Conference on Theory and Practice of Digital Libraries, 1998: Springer, pp. 61-73.
[54]L. L. Hill and G. Janée, The Alexandria digital library project: metadata development and use, Metadata in practice: A work in progress. Chicago: American Library Association, 2004.
[55]M. Berger, J. Moreno, J. A. Johannessen, P. F. Levelt, and R. F. Hanssen, ESA's sentinel missions in support of Earth system science, Remote Sensing of Environment, vol. 120, pp. 84-90, 2012.
[56]F. Sarti, A. C. Gómez, and C. Stewart, Earth Observation Capacity Building at ESA, in Space Capacity Building in the XXI Century: Springer, 2020, pp. 233-250.
[57]E. European Space Agency. About Sentinel Online. https://sentinel.esa.int/web/sentinel/about-sentinel-online (accessed May 25, 2020).
[58]E. European Space Agency. Access to Sentinel data. https://sentinel.esa.int/web/sentinel/sentinel-data-access (accessed May 25, 2020).
[59]J. A. E. A. (JAXA). About Sentinel Asia. https://sentinel-asia.org/aboutsa/AboutSA.html (accessed May 25, 2020).
[60]K. Kaku, Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia, International Journal of Disaster Risk Reduction, vol. 33, pp. 417-432, 2019.
[61]J. A. E. A. (JAXP), SENTINEL ASIA ANNUAL REPORT 2018, 2018. [Online]. Available: https://sentinel-asia.org/reports/Reports/SA_Annual_Report_2018.pdf
[62]B. Adriano, J. Xia, G. Baier, N. Yokoya, and S. Koshimura, Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia, Remote Sensing, vol. 11, no. 7, p. 886, 2019.
[63]E. Christian, Planning for the global earth observation system of systems (GEOSS), Space Policy, vol. 21, no. 2, pp. 105-109, 2005.
[64]Wikipedia. Global Earth Observation System of Systems. https://en.wikipedia.org/wiki/Global_Earth_Observation_System_of_Systems (accessed May 25, 2020).
[65]M. L. Butterfield, J. S. Pearlman, and S. C. Vickroy, A system-of-systems engineering GEOSS: Architectural approach, IEEE Systems Journal, vol. 2, no. 3, pp. 321-332, 2008.
[66]G. Giuliani et al., Sharing environmental data through GEOSS, in Emerging methods and multidisciplinary applications in geospatial research: IGI Global, 2013, pp. 266-281.
[67]Q. Huang, C. Yang, D. Nebert, K. Liu, and H. Wu, Cloud computing for geosciences: deployment of GEOSS clearinghouse on Amazon's EC2, in Proceedings of the ACM SIGSPATIAL international workshop on high performance and distributed geographic information systems, 2010, pp. 35-38.
[68]K. Liu et al., The GEOSS clearinghouse high performance search engine, in 2011 19th International Conference on Geoinformatics, 2011: IEEE, pp. 1-4.
[69]C. Yang et al., GEOSS clearinghouse: Integrating geospatial resources to support the global earth observation system of systems, in Big Data: Techniques and Technologies in Geoinformatics: CRC Press, 2014, pp. 31-54.
[70]G. o. E. Observations. GEOSS Platform Architecture. http://www.earthobservations.org/geoss.php#gci_architecture (accessed May 25, 2020).
[71]19115-2:2009 Geographic information — Metadata — Part 2: Extensions for imagery and gridded data, ISO, 2009. [Online]. Available: https://www.iso.org/standard/39229.html
[72]19115-2:2019 Geographic information — Metadata — Part 2: Extensions for acquisition and processing, ISO, 2019. [Online]. Available: https://www.iso.org/standard/67039.html
[73]Catalogue Services 3.0 - General Model, O. G. Consortium, 2016. [Online]. Available: http://docs.opengeospatial.org/is/12-168r6/12-168r6.html
[74]N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote sensing of Environment, vol. 202, pp. 18-27, 2017.
[75]L. Kumar and O. Mutanga, Google Earth Engine applications since inception: Usage, trends, and potential, Remote Sensing, vol. 10, no. 10, p. 1509, 2018.
[76]H. Tamiminia, B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli, and B. Brisco, Google Earth Engine for geo-big data applications: A meta-analysis and systematic review, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 164, pp. 152-170, 2020.
[77]X. Liu et al., High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform, Remote sensing of environment, vol. 209, pp. 227-239, 2018.
[78]A. Shelestov, M. Lavreniuk, N. Kussul, A. Novikov, and S. Skakun, Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping, 2017.
[79]A. A. Pericak et al., Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine, PloS one, vol. 13, no. 7, 2018.
[80]R. R. Larson and P. Frontiera, Spatial ranking methods for geographic information retrieval (GIR) in digital libraries, in International Conference on Theory and Practice of Digital Libraries, 2004: Springer, pp. 45-56.
[81]F. Lamberti, A. Sanna, and C. Demartini, A relation-based page rank algorithm for semantic web search engines, IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 1, pp. 123-136, 2008.
[82]A. U. Frank, Spatial concepts, geometric data models, and geometric data structures, Computers & Geosciences, vol. 18, no. 4, pp. 409-417, 1992.
[83]M. J. Egenhofer, E. Clementini, and P. Di Felice, Topological relations between regions with holes, International Journal of Geographical Information Science, vol. 8, no. 2, pp. 129-142, 1994.
[84]M. J. Egenhofer and J. Herring, Categorizing binary topological relations between regions, lines, and points in geographic databases, The, vol. 9, no. 94-1, p. 76, 1990.
[85]M. J. Egenhofer and R. D. Franzosa, On the equivalence of topological relations, International journal of geographical information systems, vol. 9, no. 2, pp. 133-152, 1995.
[86]E. Clementini, P. Di Felice, and P. Van Oosterom, A small set of formal topological relationships suitable for end-user interaction, in International Symposium on Spatial Databases, 1993: Springer, pp. 277-295.
[87]E. Clementini and P. Di Felice, A comparison of methods for representing topological relationships, Information sciences-applications, vol. 3, no. 3, pp. 149-178, 1995.
[88]E. Clementini and P. Di Felice, A model for representing topological relationships between complex geometric features in spatial databases, Information sciences, vol. 90, no. 1-4, pp. 121-136, 1996.
[89]C. Jun, L. Chengming, L. Zhilin, and C. Gold, Improving 9-intersection model by replacing the complement with Voronoi region, Geo-spatial Information Science, vol. 3, no. 1, pp. 1-10, 2000.
[90]Z. Sha and X. Li, Mining local association patterns from spatial dataset, in 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010, vol. 3: IEEE, pp. 1455-1460.
[91]M. Valiante, F. Bozzano, M. D. Seta, and D. Guida, Object-oriented geomorphological mapping model for landslide systems analysis, in Geophysical Research Abstracts, 2019, vol. 21.
[92]X. Xu-Feng, M. A. Mostafavi, and W. Chen, EXTENSION OF RCC TOPOLOGICAL RELATIONS FOR 3D COMPLEX OBJECTS COMPONENTS EXTRACTED FROM 3D LIDAR POINT CLOUDS, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, vol. 41, 2016.
[93]S. Gupta and A. Jindal, Contrast of link based web ranking techniques, in 2008 International Symposium on Biometrics and Security Technologies, 2008: IEEE, pp. 1-6.
[94]A. Pathak, S. Chakrabarti, and M. Gupta, Index design for dynamic personalized pagerank, in 2008 IEEE 24th International Conference on Data Engineering, 2008: IEEE, pp. 1489-1491.
[95]J.-H. Su, B.-W. Wang, and V. S. Tseng, Effective ranking and recommendation on web page retrieval by integrating association mining and PageRank, in 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008, vol. 3: IEEE, pp. 455-458.
[96]F. Yuan, C. Yin, and J. Liu, Improvement of pagerank for focused crawler, in Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007, vol. 2: IEEE, pp. 797-802.
[97]M. T. Aung and K. N. N. Tun, To construct implicit link structure by using frequent sequence miner (fs-miner), in 2009 International Conference on Computer Engineering and Technology, 2009, vol. 1: IEEE, pp. 549-553.
[98]N. Duhan, A. Sharma, and K. K. Bhatia, Page ranking algorithms: a survey, in 2009 IEEE International Advance Computing Conference, 2009: IEEE, pp. 1530-1537.
[99]A. Dixit, V. S. Rathore, and A. Sehgal, Improved Google Page Rank Algorithm, in Emerging Trends in Expert Applications and Security: Springer, 2019, pp. 535-540.
[100]V. Jindal, S. Bawa, and S. Batra, A review of ranking approaches for semantic search on web, Information Processing & Management, vol. 50, no. 2, pp. 416-425, 2014.
[101]E. Agichtein, E. Brill, and S. Dumais, Improving web search ranking by incorporating user behavior information, in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006, pp. 19-26.
[102]N. Bajpai and D. Arora, An Estimation of User Preferences for Search Engine Results and its Usage Patterns, in Progress in Intelligent Computing Techniques: Theory, Practice, and Applications: Springer, 2018, pp. 255-264.
[103]P. Rathod and S. Desmukh, A personalized mobile search engine based on user preference, in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2017: IEEE, pp. 1136-1141.
[104]D. Gupta and D. Singh, User preference based page ranking algorithm, in 2016 International Conference on Computing, Communication and Automation (ICCCA), 2016: IEEE, pp. 166-171.
[105]F. Alhaidari, S. Alwarthan, and A. Alamoudi, User Preference Based Weighted Page Ranking Algorithm, in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), 2020: IEEE, pp. 1-6.
[106]E. A. El-Kwae and M. R. Kabuka, A robust framework for content-based retrieval by spatial similarity in image databases, ACM Transactions on Information Systems (TOIS), vol. 17, no. 2, pp. 174-198, 1999.
[107]R. R. Larson, Ranking approaches for GIR, Sigspatial Special, vol. 3, no. 2, pp. 37-41, 2011.
[108]D. P. Huttenlocher and W. J. Rucklidge, A multi-resolution technique for comparing images using the Hausdorff distance, Cornell University, 1992.
[109]M. J. Atallah, A linear time algorithm for the Hausdorff distance between convex polygons, 1983.
[110]J. Zhang, J. Pang, J. Yu, and P. Wang, An efficient assembly retrieval method based on hausdorff distance, Robotics and Computer-Integrated Manufacturing, vol. 51, pp. 103-111, 2018.
[111]D. Karimi and S. E. Salcudean, Reducing the Hausdorff distance in medical image segmentation with convolutional neural networks, IEEE transactions on medical imaging, 2019.
[112]K. S. Kumar, T. Manigandan, D. Chitra, and L. Murali, Object recognition using Hausdorff distance for multimedia applications, Multimedia Tools and Applications, vol. 79, no. 5, pp. 4099-4114, 2020.
[113]J. Huang, G. Wang, and Z. Wang, Cross-subject page ranking based on text categorization, in 2008 International Conference on Information and Automation, 2008: IEEE, pp. 363-368.
[114]J.-H. Kim, T.-B. Yoon, K.-S. Kim, and J.-H. Lee, The trackback-rank algorithm for the blog search, in 2008 IEEE International Multitopic Conference, 2008: IEEE, pp. 454-459.
[115]F. Ricci, L. Rokach, and B. Shapira, Introduction to recommender systems handbook, in Recommender systems handbook: Springer, 2011, pp. 1-35.
[116]P. Gupta, A. Goel, J. Lin, A. Sharma, D. Wang, and R. Zadeh, Wtf: The who to follow service at twitter, in Proceedings of the 22nd international conference on World Wide Web, 2013, pp. 505-514.
[117]J. Zeng, F. Li, H. Liu, J. Wen, and S. Hirokawa, A restaurant recommender system based on user preference and location in mobile environment, in 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 2016: IEEE, pp. 55-60.
[118]M. Haldar et al., Applying deep learning to Airbnb search, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 1927-1935.
[119]H.-H. Chen, I. Ororbia, G. Alexander, and C. L. Giles, ExpertSeer: A keyphrase based expert recommender for digital libraries, arXiv preprint arXiv:1511.02058, 2015.
[120]H.-H. Chen, L. Gou, X. Zhang, and C. L. Giles, Collabseer: a search engine for collaboration discovery, in Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries, 2011, pp. 231-240.
[121]A. Felfernig, K. Isak, K. Szabo, and P. Zachar, The VITA financial services sales support environment, in Proceedings of the national conference on artificial intelligence, 2007, vol. 22, no. 2: Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, p. 1692.
[122]J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, Recommender systems survey, Knowledge-based systems, vol. 46, pp. 109-132, 2013.
[123]R. Chulyadyo, A new horizon for the recommendation: Integration of spatial dimensions to aid decision making, 2016.
[124]J. J. Levandoski, M. Sarwat, A. Eldawy, and M. F. Mokbel, Lars: A location-aware recommender system, in 2012 IEEE 28th international conference on data engineering, 2012: IEEE, pp. 450-461.
[125]W. Liu, H. Lai, J. Wang, G. Ke, W. Yang, and J. Yin, Mix geographical information into local collaborative ranking for POI recommendation, World Wide Web, vol. 23, no. 1, pp. 131-152, 2020.
[126]S. Honarparvar, R. Forouzandeh Jonaghani, A. A. Alesheikh, and B. Atazadeh, Improvement of a location-aware recommender system using volunteered geographic information, Geocarto International, vol. 34, no. 13, pp. 1496-1513, 2019.
[127]L.-W. Wei, H.-H. Lin, and C.-C. Chi, The establishment of rainfall thresholds for debris slide in Taiwan-with the combination of multivariate analysis and the IR index, Japanese Geotechnical Society Special Publication, vol. 2, no. 29, pp. 1069-1074, 2016.
[128]L. L. Thurstone, Multiple-factor analysis; a development and expansion of The Vectors of Mind. University of Chicago Press., 1947.
[129]N. Adler and B. Golany, Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe, European Journal of Operational Research, vol. 132, no. 2, pp. 260-273, 2001.
[130]L. R. Fabrigar, D. T. Wegener, R. C. MacCallum, and E. J. Strahan, Evaluating the use of exploratory factor analysis in psychological research, Psychological methods, vol. 4, no. 3, p. 272, 1999.
[131]E. Oja, Simplified neuron model as a principal component analyzer, Journal of mathematical biology, vol. 15, no. 3, pp. 267-273, 1982.
[132]Wikipedia. Multivariate statistics. https://en.wikipedia.org/wiki/Multivariate_statistics (accessed 0918, 2019).
[133]A. Wetzel, Factor analysis methods and validity evidence: A systematic review of instrument development across the continuum of medical education, 2011.
[134]C. Spearman, General Intelligence Objectively Determined and Measured, American Journal of Psychology, 2000.
[135]B. Habing. Exploratory Factor Analysis. http://www.stat.sc.edu/~habing/courses/530efa.pdf (accessed 0918, 2019).
[136]D. D. Suhr, Exploratory or confirmatory factor analysis?, in SUGI 31, San Francisco, California, K. J. LeBouton, Ed., 2006.
[137]M. S. Featherman and P. A. Pavlou, Predicting e-services adoption: a perceived risk facets perspective, International journal of human-computer studies, vol. 59, no. 4, pp. 451-474, 2003.
[138]P. Steyn. Which Test: Factor Analysis (FA, EFA, PCA, CFA). https://www.introspective-mode.org/factor-analysis-fa-efa-pca-cfa/ (accessed 0918, 2019).
[139]B. Thompson, Exploratory and confirmatory factor analysis: Understanding concepts and applications, Washington, DC, pp. 10694-000, 2004.
[140]J. DeCoster. Overview of factor analysis. http://www.stat-help.com/notes.html (accessed 1225, 2019).
[141]K. Pearson, LIII. On lines and planes of closest fit to systems of points in space, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 2, no. 11, pp. 559-572, 1901.
[142]S. Wold, K. Esbensen, and P. Geladi, Principal component analysis, Chemometrics and intelligent laboratory systems, vol. 2, no. 1-3, pp. 37-52, 1987.
[143]I. T. Jolliffe, Principal components in regression analysis, in Principal component analysis: Springer, 1986, pp. 129-155.
[144]L. Castro-Schilo. Principal components or factor analysis? https://community.jmp.com/t5/JMP-Blog/Principal-components-or-factor-analysis/ba-p/38347 (accessed 1215, 2019).
[145]J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
[146]P. Dong, C. Yang, X. Rui, L. Zhang, and Q. Cheng, An effective buffer generation method in GIS, in IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477), 2003, vol. 6: Ieee, pp. 3706-3708.
[147]C. D. Manning, P. Raghavan, and H. Schütze, Introduction to information retrieval. Cambridge university press, 2008.
[148]K. Boyd, K. H. Eng, and C. D. Page, Area under the precision-recall curve: point estimates and confidence intervals, in Joint European conference on machine learning and knowledge discovery in databases, Berlin, Heidelberg, K. K. Blockeel H., Nijssen S., Železný F., Ed., 2013, vol. 8190: Springer, pp. 451-466.
[149]G. V. Cormack and T. R. Lynam, Statistical precision of information retrieval evaluation, in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006, pp. 533-540.
[150]D. Laney, 3D data management: Controlling data volume, velocity and variety, META group research note, vol. 6, no. 70, p. 1, 2001.
[151]J. Y. Lizhe Wang, Yan Ma, Cloud Computing in Remote Sensing. New York: Chapman and Hall/CRC, 2019.
[152]J. Xiong et al., Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine, Remote Sensing, vol. 9, no. 10, p. 1065, 2017.
[153]G. Cavallaro, M. Riedel, M. Richerzhagen, J. A. Benediktsson, and A. Plaza, On understanding big data impacts in remotely sensed image classification using support vector machine methods, IEEE journal of selected topics in applied earth observations and remote sensing, vol. 8, no. 10, pp. 4634-4646, 2015.
[154]L. Zhang, Q. Du, and M. Datcu, Special section guest editorial: management and analytics of remotely sensed big data, Journal of Applied Remote Sensing, vol. 9, no. 1, pp. 1-2, 2015.
[155]H. Shahabi et al., Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier, Remote Sensing, vol. 12, no. 2, p. 266, 2020.
[156]L. Zhu et al., Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network, Sensors, vol. 20, no. 6, p. 1576, 2020.
[157]G. Jianya, S. Haigang, M. Guorui, and Z. Qiming, A review of multi-temporal remote sensing data change detection algorithms, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37, no. B7, pp. 757-762, 2008.
[158]C. Liu et al., An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks, Remote Sensing of Environment, vol. 229, pp. 114-132, 2019.
[159]J.-S. Lai and F. Tsai, Improving GIS-based landslide susceptibility assessments with multi-temporal remote sensing and machine learning, Sensors, vol. 19, no. 17, p. 3717, 2019.
[160]S. Baamonde, M. Cabana, N. Sillero, M. G. Penedo, H. Naveira, and J. Novo, Fully automatic multi-temporal land cover classification using Sentinel-2 image data, Procedia Computer Science, vol. 159, pp. 650-657, 2019.
[161]19108:2002 Geographic information — Temporal schema, ISO, 2005. [Online]. Available: https://www.iso.org/standard/26013.html
[162]E. H.-C. Lu, J.-H. Hong, Z. L.-T. Su, and C.-H. Chen, A fuzzy data mining approach for remote sensing image recommendation, in 2013 IEEE International Conference on Granular Computing (GrC), 2013: IEEE, pp. 213-218.
[163]D. J. Lary, A. H. Alavi, A. H. Gandomi, and A. L. Walker, Machine learning in geosciences and remote sensing, Geoscience Frontiers, vol. 7, no. 1, pp. 3-10, 2016.
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