{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T19:40:01Z","timestamp":1777405201718,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The General Project of Humanities and Social Sciences of the Ministry of Education of China","award":["No. 24YJA760062"],"award-info":[{"award-number":["No. 24YJA760062"]}]},{"name":"The Natural Science Foundation of Inner Mongolia Autonomous Region","award":["No. 2024LHMS05030"],"award-info":[{"award-number":["No. 2024LHMS05030"]}]},{"name":"The Key R&amp;D and Achievement Transformation Program of Inner Mongolia Autonomous Region","award":["No. 2022YFDZ0017"],"award-info":[{"award-number":["No. 2022YFDZ0017"]}]},{"name":"Construction System and Key Technologies of Grassland Human Settlements","award":["No. YLXKZX-NGD-004"],"award-info":[{"award-number":["No. YLXKZX-NGD-004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Productive landscapes are an important part of intangible cultural heritage, and their protection and inheritance are of great significance to the prosperity and sustainable development of national culture. It not only reflects the wisdom accumulated through the long-term interaction between human production activities and the natural environment, but also carries a strong symbolic meaning of rural culture. However, current research and investigation on productive landscapes still rely mainly on field surveys and manual records conducted by experts and scholars. This process is time-consuming and costly, and it is difficult to achieve efficient and systematic analysis and comparison, especially when dealing with large-scale and diverse types of landscapes. To address this problem, this study takes the Inner Mongolia region as the main research area and builds a productive landscape feature data framework that reflects the diversity of rural production activities and cultural landscapes. The framework covers four major types of landscapes: agriculture, animal husbandry, fishery and hunting, and sideline production and processing. Based on artificial intelligence and deep learning technologies, this study conducts comparative experiments on several convolutional neural network models to evaluate their classification performance and adaptability in complex rural environments. The results show that the improved CEM-ResNet50 model performs better than the other models in terms of accuracy, stability, and feature recognition ability, demonstrating stronger generalization and robustness. Through a semantic clustering approach in image classification, the model\u2019s recognition process is visually interpreted, revealing the clustering patterns and possible sources of confusion among different landscape elements in the semantic space. This study reduces the time and economic cost of traditional field investigations and achieves efficient and intelligent recognition of rural productive landscapes. It also provides a new technical approach for the digital protection and cultural heritage transmission of productive landscapes, offering valuable references for future research in related fields.<\/jats:p>","DOI":"10.3390\/computers14120565","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T13:42:52Z","timestamp":1765978972000},"page":"565","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Intelligent Identification of Rural Productive Landscapes in Inner Mongolia"],"prefix":"10.3390","volume":"14","author":[{"given":"Xin","family":"Tian","sequence":"first","affiliation":[{"name":"Architecture College, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Grassland Human Settlement System and Low-Carbon Construction Technology, Hohhot 010051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Li","sequence":"additional","affiliation":[{"name":"Architecture College, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Grassland Human Settlement System and Low-Carbon Construction Technology, Hohhot 010051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nisha","family":"Ai","sequence":"additional","affiliation":[{"name":"Architecture College, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Grassland Human Settlement System and Low-Carbon Construction Technology, Hohhot 010051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songhua","family":"Gao","sequence":"additional","affiliation":[{"name":"Art College, Inner Mongolia Normal University, Hohhot 010028, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"Architecture College, Inner Mongolia University of Technology, Hohhot 010051, China"},{"name":"Inner Mongolia Key Laboratory of Grassland Human Settlement System and Low-Carbon Construction Technology, Hohhot 010051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"ref_1","first-page":"21","article-title":"From Objects to Processes: UNESCO\u2019S \u2018Intangible Cultural Heritage\u2019","volume":"1","author":"Bortolotto","year":"2007","journal-title":"J. Mus. Ethnogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1080\/13527258.2016.1269239","article-title":"A viewpoint on the reconstruction of destroyed UNESCO Cultural World Heritage Sites","volume":"23","author":"Khalaf","year":"2017","journal-title":"Int. J. Herit. Stud."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Santoro, A., Venturi, M., Bertani, R., and Agnoletti, M. (2020). A review of the role of forests and agroforestry systems in the FAO Globally Important Agricultural Heritage Systems (GIAHS) programme. Forests, 11.","DOI":"10.3390\/f11080860"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Plieninger, T., and Bieling, C. (2012). Resilience and the Cultural Landscape: Understanding and Managing Change in Human-Shaped Environments, Cambridge University Press.","DOI":"10.1017\/CBO9781139107778"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/13527250701570515","article-title":"World heritage cultural landscapes","volume":"13","author":"Aplin","year":"2007","journal-title":"Int. J. Herit. Stud."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1016\/j.renene.2007.05.003","article-title":"Sustainable energy policy indicators: Review and recommendations","volume":"33","author":"Patlitzianas","year":"2008","journal-title":"Renew. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1007\/s10980-025-02083-3","article-title":"The characterisation and future sustainability of a rural landscape: Using integrated approaches for temporal heritage landscape analysis in Northwest Spain","volume":"40","author":"Hearn","year":"2025","journal-title":"Landsc. Ecol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Santoro, A., Venturi, M., and Agnoletti, M. (2020). Agricultural heritage systems and landscape perception among tourists. The case of Lamole, Chianti (Italy). Sustainability, 12.","DOI":"10.3390\/su12093509"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vali, A., Comai, S., and Matteucci, M. (2020). Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sens., 12.","DOI":"10.3390\/rs12152495"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Dian, Y., Guo, Z., Yao, C., and Wu, X. (2023). A functional zoning method in rural landscape based on high-resolution satellite imagery. Remote Sens., 15.","DOI":"10.3390\/rs15204920"},{"key":"ref_11","unstructured":"Torquati, B., Vizzari, M., and Sportolaro, C. (2011). Participatory GIS for integrating local and expert knowledge in landscape planning. Agricultural and Environmental Informatics, Governance and Management: Emerging Research Applications, IGI Global Scientific Publishing."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.jenvman.2016.05.044","article-title":"Landscape character assessment with GIS using map-based indicators and photographs in the relationship between landscape and roads","volume":"180","author":"Ortega","year":"2016","journal-title":"J. Environ. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1080\/13658816.2018.1542698","article-title":"Fine-grained landuse characterization using ground-based pictures: A deep learning solution based on globally available data","volume":"34","author":"Srivastava","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Clark, A., Phinn, S., and Scarth, P. (2023). Pre-Processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation. Land, 12.","DOI":"10.2139\/ssrn.4329498"},{"key":"ref_15","first-page":"103591","article-title":"Mixed land use measurement and mapping with street view images and spatial context-aware prompts via zero-shot multimodal learning","volume":"125","author":"Wu","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1109\/TMM.2019.2891999","article-title":"Fine-grained land use classification at the city scale using ground-level images","volume":"21","author":"Zhu","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_18","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1080\/13683500308667945","article-title":"Negative image? Developing the visual in tourism research","volume":"6","author":"Feighey","year":"2003","journal-title":"Curr. Issues Tour."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1186\/s40494-024-01340-z","article-title":"MonuNet: A high performance deep learning network for Kolkata heritage image classification","volume":"12","author":"Sasithradevi","year":"2024","journal-title":"Herit. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cheng, Y., and Chen, W. (2025). Cultural Perception of Tourism Heritage Landscapes via Multi-Label Deep Learning: A Study of Jingdezhen, the Porcelain Capital. Land, 14.","DOI":"10.3390\/land14030559"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"24876","DOI":"10.1038\/s41598-025-06731-1","article-title":"Leveraging machine learning techniques for image classification and revealing social media insights into human engagement with urban wild spaces","volume":"15","author":"Khalid","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_24","first-page":"471","article-title":"Landscape image recognition and analysis based on deep learning algorithm","volume":"49","author":"Limei","year":"2025","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_25","first-page":"36","article-title":"Gathering predictors of biodiversity change and reconstructing land cover history in Central Apennines using machine learning and remote sensing data","volume":"2","author":"Lemenkova","year":"2025","journal-title":"J. Anatol. Geogr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.rse.2015.09.025","article-title":"Controlling for misclassified land use data: A post-classification latent multinomial logit approach","volume":"170","author":"Martinez","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kunwar, S., and Ferdush, J. (2023). Mapping of land use and land cover (LULC) using EuroSAT and transfer learning. arXiv.","DOI":"10.32604\/rig.2023.047627"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Albert, A., Kaur, J., and Gonzalez, M.C. (2017, January 13\u201317). Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098070"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6499","DOI":"10.3390\/heritage7110302","article-title":"ImageOP: The Image Dataset with Religious Buildings in the World Heritage Town of Ouro Preto for Deep Learning Classification","volume":"7","author":"Ottoni","year":"2024","journal-title":"Heritage"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3311950","article-title":"Machine learning for smart building applications: Review and taxonomy","volume":"52","author":"Djenouri","year":"2019","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_31","first-page":"102886","article-title":"Mapping human perception of urban landscape from street-view images: A deep-learning approach","volume":"112","author":"Wei","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","first-page":"82","article-title":"Landscape perception, classification, and use among Sahelian Fulani in Burkina Faso","volume":"49","author":"Krohmer","year":"2010","journal-title":"Landscape Ethnoecology: Concepts of Biotic and Physical Space"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gong, S., Zhang, L., Zhang, J., and Duan, Y. (2025). Rural Local Landscape Perception Evaluation: Integrating Street View Images and Machine Learning. ISPRS Int. J. Geo-Inf., 14.","DOI":"10.3390\/ijgi14070251"},{"key":"ref_34","first-page":"333","article-title":"World Heritage cultura landscapes: A UNESCO flagship programme 1992\u20132006","volume":"31","year":"2006","journal-title":"Landsc. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Akagawa, N. (2014). Heritage Conservation and Japan\u2019s Cultural Diplomacy: Heritage, National Identity and National Interest, Routledge.","DOI":"10.4324\/9781315886664"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Su, R., Zhang, Y.D., and Frangi, A.F. (2024). Deep Learning for Image Classification: A Review. Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023), Cambridge, UK, 9-10 December 2023, Springer. Lecture Notes in Electrical Engineering.","DOI":"10.1007\/978-981-97-1335-6"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1109\/TEVC.2022.3169490","article-title":"Genetic Programming for Image Classification: A New Program Representation with Flexible Feature Reuse","volume":"27","author":"Fan","year":"2023","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e12363","DOI":"10.1111\/spc3.12363","article-title":"Culture and attention: Recent empirical findings and new directions in cultural psychology","volume":"11","author":"Masuda","year":"2017","journal-title":"Soc. Personal. Psychol. Compass"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., and Miao, Y. (2021). Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sens., 13.","DOI":"10.3390\/rs13224712"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6756","DOI":"10.1109\/TMM.2022.3214431","article-title":"Fine-Grained Image Classification by Class and Image-Specific Decomposition with Multiple Views","volume":"25","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., and Li, M. (2019, January 15\u201320). Bag of Tricks for Image Classification with Convolutional Neural Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00065"},{"key":"ref_42","first-page":"3221","article-title":"Accelerating t-SNE using tree-based algorithms","volume":"15","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","first-page":"3507012","article-title":"An Analysis Method for Interpretability of Convolutional Neural Network in Bearing Fault Diagnosis","volume":"73","author":"Guo","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/12\/565\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T05:10:43Z","timestamp":1766466643000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/12\/565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":43,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["computers14120565"],"URL":"https:\/\/doi.org\/10.3390\/computers14120565","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,17]]}}}