{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:47:45Z","timestamp":1765547265096,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2023ORP03","42201415"],"award-info":[{"award-number":["CBAS2023ORP03","42201415"]}]},{"name":"National Natural Science Foundation of China","award":["CBAS2023ORP03","42201415"],"award-info":[{"award-number":["CBAS2023ORP03","42201415"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation and efficiency. Addressing these challenges is imperative for advancing sustainable development within the geological environment. This paper aims to address issues related to the generation of geological environment remote sensing thematic products, sorting through the overall process of remote sensing thematic product generation, exploring algorithm encapsulation, combination, and execution under technical methods for container and workflow, and relies on the Spark distributed processing architecture to achieve efficient thematic product generation supported by multiple geological environment data processing models. Finally, taking the three SDGs of SDG6, SDG11, and SDG15 as examples, we achieved the generation of a variety of thematic products such as the interpretation of water body distribution, extraction of urban informal settlements and distribution of water and soil erosion. Meanwhile, we comparatively analyzed the efficiency of thematic product generation on different processing architectures, and the experimental results further verified the feasibility and effectiveness of our proposed solution. This research provides a programme for the automated and intelligent generation of geological environment remote sensing thematic products and effectively assists the construction of sustainable development in the geological environment.<\/jats:p>","DOI":"10.3390\/rs16142529","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T13:34:38Z","timestamp":1720618478000},"page":"2529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Remote Sensing Thematic Product Generation for Sustainable Development of the Geological Environment"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9425-8981","authenticated-orcid":false,"given":"Jiabao","family":"Li","sequence":"first","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Wei","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3882-1616","authenticated-orcid":false,"given":"Wei","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0394-2357","authenticated-orcid":false,"given":"Xiaohui","family":"Huang","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Ao","family":"Long","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yuewei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guo, H., Huang, L., and Liang, D. (2022). Further promotion of sustainable development goals using science, technology, and innovation. Innovation, 3.","DOI":"10.1016\/j.xinn.2022.100325"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1038\/s41597-023-02803-x","article-title":"A future for digital public goods for monitoring SDG indicators","volume":"10","author":"Liang","year":"2023","journal-title":"Sci. Data"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1080\/13504509.2020.1823517","article-title":"Business models towards SDGs: The barriers for operationalizing Product-Service System (PSS) in Brazil","volume":"28","author":"Labbate","year":"2021","journal-title":"Int. J. Sustain. Dev. World Ecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102875","DOI":"10.1016\/j.earscirev.2019.102875","article-title":"Advances in quantitative remote sensing product validation: Overview and current status","volume":"196","author":"Wu","year":"2019","journal-title":"Earth Sci. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2022RG000777","DOI":"10.1029\/2022RG000777","article-title":"Satellite remote sensing of global land surface temperature: Definition, methods, products, and applications","volume":"61","author":"Li","year":"2023","journal-title":"Rev. Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"FG Assis, L.F., Ferreira, K.R., Vinhas, L., Maurano, L., Almeida, C., Carvalho, A., Rodrigues, J., Maciel, A., and Camargo, C. (2019). TerraBrasilis: A spatial data analytics infrastructure for large-scale thematic mapping. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8110513"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.ocecoaman.2014.03.003","article-title":"Potential of remote sensing in management of tidal flats: A case study of thematic mapping in the Korean tidal flats","volume":"102","author":"Ryu","year":"2014","journal-title":"Ocean. Coast. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"031501","DOI":"10.1117\/1.JRS.15.031501","article-title":"Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: An updated review","volume":"15","author":"Peyghambari","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"556","DOI":"10.31035\/cg2018050","article-title":"Geological resources and environmental carrying capacity evaluation review, theory, and practice in China","volume":"1","author":"Li","year":"2018","journal-title":"China Geol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, C., Zhang, Y., Zhang, J., Chen, Y., Duan, C., Qi, J., Cheng, Z., and Pan, Z. (2022). Comprehensive Evaluation of the Eco-Geological Environment in the Concentrated Mining Area of Mineral Resources. Sustainability, 14.","DOI":"10.3390\/su14116808"},{"key":"ref_11","first-page":"112","article-title":"Multi-and hyperspectral geologic remote sensing: A review","volume":"14","author":"Hecker","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1080\/00401706.2013.831774","article-title":"Spatio-temporal data fusion for very large remote sensing datasets","volume":"56","author":"Nguyen","year":"2014","journal-title":"Technometrics"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic map comparison","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0034-4257(78)90003-2","article-title":"Remote sensing: Statistical testing of thematic map accuracy","volume":"7","author":"Lock","year":"1978","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., and Qiu, C.W. (2021). Artificial intelligence: A powerful paradigm for scientific research. Innovation, 2.","DOI":"10.1016\/j.xinn.2021.100179"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"012007","DOI":"10.1088\/1742-6596\/1684\/1\/012007","article-title":"Review of the application of big data and artificial intelligence in geology","volume":"1684","author":"Chen","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, S., Chen, J., and Liu, C. (2022). Overview on the development of intelligent methods for mineral resource prediction under the background of geological big data. Minerals, 12.","DOI":"10.3390\/min12050616"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1016\/j.gsf.2018.12.005","article-title":"Rare earth elements: A review of applications, occurrence, exploration, analysis, recycling, and environmental impact","volume":"10","author":"Balaram","year":"2019","journal-title":"Geosci. Front."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2018.04.047","article-title":"Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images","volume":"212","author":"Fongaro","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4505515","DOI":"10.1109\/TGRS.2023.3307977","article-title":"Remote Sensing Interpretation for Soil Elements using Adaptive Feature Fusion Network","volume":"61","author":"Lu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4409917","DOI":"10.1109\/TGRS.2022.3160617","article-title":"Large-area land-cover changes monitoring with time-series remote sensing images using transferable deep models","volume":"60","author":"Yan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.isprsjprs.2023.05.032","article-title":"A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities","volume":"202","author":"Han","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, J., Han, W., Chen, J., and Wang, S. (2023). Improving Geological Remote Sensing Interpretation via Optimal Transport-Based Point\u2013Surface Data Fusion. Remote Sens., 16.","DOI":"10.3390\/rs16010053"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100379","DOI":"10.1016\/j.cosrev.2021.100379","article-title":"A survey on deep learning and its applications","volume":"40","author":"Dong","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4510314","DOI":"10.1109\/TGRS.2022.3183080","article-title":"Geological remote sensing interpretation using deep learning feature and an adaptive multisource data fusion network","volume":"60","author":"Han","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","first-page":"103536","article-title":"Lithological mapping of geological remote sensing via adversarial semi-supervised segmentation network","volume":"125","author":"Wang","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"114101","DOI":"10.1016\/j.rse.2024.114101","article-title":"Time-series land cover change detection using deep learning-based temporal semantic segmentation","volume":"305","author":"He","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.earscirev.2019.02.023","article-title":"Deep learning and its application in geochemical mapping","volume":"192","author":"Zuo","year":"2019","journal-title":"Earth Sci. Rev."},{"key":"ref_29","unstructured":"Han\u017el, P., and Verner, K. (2018). Basic Principles of Geological and Thematic Mapping, Czech Geological Survey."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rse.2012.12.003","article-title":"The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30 m Landsat data product generation","volume":"130","author":"Kovalskyy","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1016\/j.future.2017.02.044","article-title":"A cloud-based remote sensing data production system","volume":"86","author":"Yan","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2436","DOI":"10.3390\/rs5052436","article-title":"The Global Land Surface Satellite (GLASS) remote sensing data processing system and products","volume":"5","author":"Zhao","year":"2013","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s11069-013-0589-y","article-title":"Rapid mapping in support of emergency response after earthquake events","volume":"68","author":"Wegscheider","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pieschke, R.L. (2017). US Geological Survey Distribution of European Space Agency\u2019s Sentinel-2 Data, US Geological Survey. Technical report.","DOI":"10.3133\/fs20173026"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/17538947.2014.1003106","article-title":"Big data analytics for earth sciences: The EarthServer approach","volume":"9","author":"Baumann","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s12145-021-00744-w","article-title":"Using remote sensing data for geological mapping in semi-arid environment: A machine learning approach","volume":"15","year":"2022","journal-title":"Earth Sci. Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"112750","DOI":"10.1016\/j.rse.2021.112750","article-title":"A review of machine learning in processing remote sensing data for mineral exploration","volume":"268","author":"Shirmard","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xu, C., Du, X., Yan, Z., and Fan, X. (2020). ScienceEarth: A big data platform for remote sensing data processing. Remote Sens., 12.","DOI":"10.3390\/rs12040607"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MGRS.2018.2867592","article-title":"Mini-UAV-borne hyperspectral remote sensing: From observation and processing to applications","volume":"6","author":"Zhong","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1080\/20964471.2021.1964879","article-title":"Deep learning for processing and analysis of remote sensing big data: A technical review","volume":"6","author":"Zhang","year":"2022","journal-title":"Big Earth Data"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Nogueras-Iso, J., Zarazaga-Soria, F.J., and Muro-Medrano, P.R. (2005). Geographic Information Metadata for Spatial Data Infrastructures, Springer.","DOI":"10.1007\/978-3-540-30078-6_65"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S0167-9236(02)00208-7","article-title":"Metadata management: Past, present and future","volume":"37","author":"Sen","year":"2004","journal-title":"Decis. Support Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s10844-020-00608-7","article-title":"On data lake architectures and metadata management","volume":"56","author":"Sawadogo","year":"2021","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"baw075","DOI":"10.1093\/database\/baw075","article-title":"BioSharing: Curated and crowd-sourced metadata standards, databases and data policies in the life sciences","volume":"2016","author":"McQuilton","year":"2016","journal-title":"Database"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bordogna, G., Kliment, T., Frigerio, L., Brivio, P.A., Crema, A., Stroppiana, D., Boschetti, M., and Sterlacchini, S. (2016). A spatial data infrastructure integrating multisource heterogeneous geospatial data and time series: A study case in agriculture. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5050073"},{"key":"ref_47","unstructured":"Acharya, J.N., and Suthar, A.C. (2021, January 3\u20134). Docker container orchestration management: A review. Proceedings of the International Conference on Intelligent Vision and Computing, Sur, Oman."},{"key":"ref_48","first-page":"102784","article-title":"Analyzing large-scale Data Cubes with user-defined algorithms: A cloud-native approach","volume":"109","author":"Xu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Knoth, C., and N\u00fcst, D. (2017). Reproducibility and practical adoption of geobia with open-source software in docker containers. Remote Sens., 9.","DOI":"10.3390\/rs9030290"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Shah, J., and Dubaria, D. (2019, January 7\u20139). Building modern clouds: Using docker, kubernetes & Google cloud platform. Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2019.8666479"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Paraiso, F., Challita, S., Al-Dhuraibi, Y., and Merle, P. (July, January 27). Model-driven management of docker containers. Proceedings of the 2016 IEEE 9th International Conference on cloud Computing (CLOUD), San Francisco, CA, USA.","DOI":"10.1109\/CLOUD.2016.0100"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.array.2019.100002","article-title":"Big Data: Hadoop framework vulnerabilities, security issues and attacks","volume":"1","author":"Bhathal","year":"2019","journal-title":"Array"},{"key":"ref_53","first-page":"191","article-title":"Automated large-scale mapping of the jahazpur mineralised belt by a MapReduce model with an integrated elm method","volume":"90","author":"Roy","year":"2022","journal-title":"J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1186\/s40537-019-0245-9","article-title":"Multi-dimensional geospatial data mining in a distributed environment using MapReduce","volume":"6","author":"Alkathiri","year":"2019","journal-title":"J. Big Data"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1145\/2934664","article-title":"Apache spark: A unified engine for big data processing","volume":"59","author":"Zaharia","year":"2016","journal-title":"Commun. ACM"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.cageo.2019.06.003","article-title":"GeoBeam: A distributed computing framework for spatial data","volume":"131","author":"He","year":"2019","journal-title":"Comput. Geosci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s11629-021-6860-x","article-title":"Eco-geological environment quality assessment based on multi-source data of the mining city in red soil hilly region, China","volume":"19","author":"Zhao","year":"2022","journal-title":"J. Mt. Sci."},{"key":"ref_58","first-page":"45","article-title":"Application of remote sensing in earth sciences\u2014A review","volume":"10","author":"Shirazy","year":"2021","journal-title":"Int. J. Sci. Eng. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1007\/s11600-022-00871-y","article-title":"Remote sensing data processing and analysis for the identification of geological entities","volume":"71","author":"Chi","year":"2023","journal-title":"Acta Geophys."},{"key":"ref_60","first-page":"100519","article-title":"Penetrating remote sensing: Next-generation remote sensing for transparent earth","volume":"4","author":"Wang","year":"2023","journal-title":"Innovation"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1109\/ACCESS.2021.3137671","article-title":"Developing docker and docker-compose specifications: A developers\u2019 survey","volume":"10","author":"Reis","year":"2021","journal-title":"IEEE Access"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2529\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:12:45Z","timestamp":1760109165000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/14\/2529"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,10]]},"references-count":61,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16142529"],"URL":"https:\/\/doi.org\/10.3390\/rs16142529","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,7,10]]}}}