The evolution and current landscape of AI in geographical research: A large-scale systematic review

Abstract

With the rapid advancement of Artificial Intelligence (AI) technologies, its applications have become increasingly widespread across various aspects of geography, offering unprecedented analytical capabilities across disciplinary boundaries. Despite this revolutionary potential, a comprehensive understanding of the current research landscape and development trajectory of AI in geographical sciences remains limited. To fill this gap, we conducted a large-scale systematic review based on 400,000 geographical publications published from 1990 to 2023. We utilized large language model (LLM) prompt engineering, topic modeling and other natural language processing techniques to analyze the publications. Our findings reveal that AI applications constitute 8.1 % of geographical research, with publication volume having increased 20-fold over three decades. Both China and the United States have been the leading contributors to AI-driven geographical studies, together accounting for 62.78 % of all publications in this field. Notably, more than half of the studies used traditional machine learning methods. Among the various geographical topics, remote sensing applications and spatial data analysis emerged as the most extensively explored areas using AI techniques, with image feature extraction being the topic with the deepest level of adoption and most significant ongoing impact of AI methods. This systematic review provides critical insights into the integration trajectory of AI within geographical sciences, establishing a foundation for identifying emerging research opportunities and enhancing our understanding of AI’s transformative role in advancing geographical knowledge.

Keywords

Geography / Artificial intelligence / LLM / Systematic review

CRediT authorship contribution statement

Chenjin An: Writing - original draft, Software, Methodology, Investigation, Formal analysis, Data curation. Jianghao Wang: Writing - review & editing, Writing - original draft, Supervision, Resources, Project administration, Investigation, Funding acquisition, Conceptualization. Chenghu Zhou: Writing - review & editing, Supervision, Conceptualization.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB0740100), and the National Natural Science Foundation of China (Grants No. 42571540 and 42222110). The funders had no role in conceptualization, design, data collection, analysis, publication decision, or manuscript preparation.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2025.100392.

References

[1] Araos M., Jagannathan K., Shukla R., Ajibade I., Perez E.C.d., Davis K., Ford J.D., Galappaththi E.K., Grady C., Hudson A.J., Joe E.T., Kirchhoff C.J., Lesnikowski A., Alverio G.N., Nielsen M., Orlove B., Pentz B., Reckien D., Siders A.R., Ulibarri N., Aalst M.v., Abu T.Z., Agrawal T., Berrang-Ford L., Kerr R.B., Coggins S., Garschagen M., Harden A., Mach K.J., Nunbogu A.M., Spandan P., Templeman S., Turek- Hankins L.L., 2021. Equity in human adaptation-related responses: a systematic global review. One Earth 4 (10), 1454-1467. doi: 10.1016/j.oneear.2021.09.001.

[2] Bai Y.-H., Tang Z., 2024. Enhanced effects of species richness on resistance and resilience of global tree growth to prolonged drought. Proc. Natl. Acad. Sci. U.S.A. 121 (39), e2410467121. doi: 10.1073/pnas.2410467121.

[3] Berrang-Ford L., Siders A.R., Lesnikowski A., Fischer A.P., Callaghan M.W., Haddaway N.R., Mach K.J., Araos M., Shah M.A.R., Wannewitz M., Doshi D., Leiter T., Matavel C., Musah-Surugu J.I., Wong-Parodi G., Antwi-Agyei P., Ajibade I., Chauhan N., Kakenmaster W., Grady C., Chalastani V.I., Jagannathan K., Galappaththi E.K., Sitati A., Scarpa G., Totin E., Davis K., Hamilton N.C., Kirchhoff C.J., Kumar P., Pentz B., Simpson N.P., Theokritoff E., Deryng D., Reckien D., Zavaleta- Cortijo C., Ulibarri N., Segnon A.C., Khavhagali V., Shang Y., Zvobgo L., Zommers Z., Xu J., Williams P.A., Canosa I.V., van Maanen N., van Bavel B., van Aalst M., Turek-Hankins L.L., Trivedi H., Trisos C.H., Thomas A., Thakur S., Templeman S., Stringer L.C., Sotnik G., Sjostrom K.D., Singh C., Siña M.Z., Shukla R., Sardans J., Salubi E.A., Safaee Chalkasra L.S., Ruiz-Díaz R., Richards C., Pokharel P., Petzold J., Penuelas J., Pelaez Avila J., Murillo J.B.P., Ouni S., Niemann J., Nielsen M., New M., Nayna Schwerdtle P., Nagle Alverio G., Mullin C.A., Mullenite J., Mosurska A., Morecroft M.D., Minx J.C., Maskell G., Nunbogu A.M., Magnan A.K., Lwasa S., Lukas-Sithole M., Lissner T., Lilford O., Koller S.F., Jurjonas M., Joe E.T., Huynh L.T.M., Hill A., Hernandez R.R., Hegde G., Hawxwell T., Harper S., Harden A., Haasnoot M., Gilmore E.A., Gichuki L., Gatt A., Garschagen M., Ford J.D., Forbes A., Farrell A.D., Enquist C.A.F., Elliott S., Duncan E., Coughlan de Perez E., Coggins S., Chen T., Campbell D., Browne K.E., Bowen K.J., Biesbroek R., Bhatt I.D., Bezner Kerr R., Barr S.L., Baker E., Austin S.E., Arotoma- Rojas I., Anderson C., Ajaz W., Agrawal T., Abu T.Z., 2021a. A systematic global stocktake of evidence on human adaptation to climate change. Nat. Clim. Chang. 11 (11), 989-1000. doi: 10.1038/s41558-021-01170-y.

[4] Berrang-Ford L., Sietsma A.J., Callaghan M., Minx J.C., Scheelbeek P.F.D., Haddaway N.R., Haines A., Dangour A.D., 2021b. Systematic mapping of global research on climate and health: a machine learning review. Lancet Planet. Health 5 (8), e514- e525. doi: 10.1016/S2542-5196(21)00179-0.

[5] Brandt M., Tucker C.J., Kariryaa A., Rasmussen K., Abel C., Small J., Chave J., Rasmussen L.V., Hiernaux P., Diouf A.A., Kergoat L., Mertz O., Igel C., Gieseke F., Schöning J., Li S., Melocik K., Meyer J., Sinno S., Romero E., Glennie E., Montagu A., Dendoncker M., Fensholt R., 2020. An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587 (7832), 78-82. doi: 10.1038/s41586-020-2824-5.

[6] Bucher M.J.J., Martini M., 2024. Fine-tuned ‘small’ LLMs (still) significantly outperform zero-shot generative AI models in text classification. arXiv. https://doi.org/10.48550/arXiv.2406.08660.

[7] Burger B., Maffettone P.M., Gusev V.V., Aitchison C.M., Bai Y., Wang X., Li X., Alston B.M., Li B., Clowes R., Rankin N., Harris B., Sprick R.S., Cooper A.I., 2020. A mobile robotic chemist. Nature 583 (7815), 237-241. doi: 10.1038/s41586-020-2442-2.

[8] Callaghan M., Schleussner C.-F., Nath S., Lejeune Q., Knutson T.R., Reichstein M., Hansen G., Theokritoff E., Andrijevic M., Brecha R.J., Hegarty M., Jones C., Lee K., Lucas A., van Maanen N., Menke I., Pfleiderer P., Yesil B., Minx J.C., 2021. Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies. Nat. Clim. Chang. 11 (11), 966-972. doi: 10.1038/s41558-021-01168-6.

[9] Callaghan M.W., Minx J.C., Forster P.M., 2020. A topography of climate change research. Nat. Clim. Chang. 10 (2), 118-123. doi: 10.1038/s41558-019-0684-5.

[10] Camps-Valls G., Fernández-Torres M.-Á., Cohrs K.-H., Höhl A., Castelletti A., Pacal A., Robin C., Martinuzzi F., Papoutsis I., Prapas I., Pérez-Aracil J., Weigel K., Gonzalez-Calabuig M., Reichstein M., Rabel M., Giuliani M., Mahecha M.D., Popescu O.-I., Pellicer-Valero O.J., Ouala S., Salcedo-Sanz S., Sippel S., Kondylatos S., Happé T., Williams T., 2025. Artificial intelligence for modeling and understanding extreme weather and climate events. Nat. Commun. 16 (1), 1919. doi: 10.1038/s41467-025-56573-8.

[11] Chang C.-H., Kidman G., 2023. The rise of generative artificial intelligence (AI) language models - challenges and opportunities for geographical and environmental education. Int. Res. Geogr. Environ. Educ. 32 (2), 85-89. doi: 10.1080/10382046.2023.2194036.

[12] Chang C.H., Erbaugh J.T., Fajardo P., Lu L., Molnár I., Papp D., Robinson B.E., Austin K.G., Castro M., Cheng S.H., Cook-Patton S., Ellis P.W., Garg T., Hochard J.P., Kroeger T., McDonald R.I., Poor E.E., Smart L.S., Tilman A.R., Welker P., Wood S.A., Masuda Y.J., 2024. Global evidence of human wellbeing and biodiversity impacts of natural climate solutions. Nat. Sustain. 8, 75-85. doi: 10.1038/s41893-024-01454-z.

[13] Chen H., Chandrasekar V., Cifelli R., Xie P., 2020. A machine learning system for precipitation estimation using satellite and ground radar network observations. IEEE Trans. Geosci. Remote Sens. 58 (2), 982-994. doi: 10.1109/TGRS.2019.2942280.

[14] Chen M., Xian Y., Huang Y., Zhang X., Hu M., Guo S., Chen L., Liang L., 2022. Finescale population spatialization data of China in 2018 based on real location-based big data. Sci. Data 9 (1), 624. doi: 10.1038/s41597-022-01740-5.

[15] Cho K., Van Merriënboer B., Gulcehre C., Bahdanau D., Bougares F., Schwenk H., Bengio Y., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Alessandro Moschitti B.P., Eds.), Daelemans, Walter (Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp. 1724-1734.

[16] Choi J., Lee B., 2024. Accelerating materials language processing with large language models. Commun. Mater. 5 (1), 13. doi: 10.1038/s43246-024-00449-9.

[17] Clarivate. Web of Science Core Collection: web of Science categories (accessed 1 April 2025). https://support.clarivate.com/ScientificandAcademicResearch/s/article/Web-of-Science-Core-Collection-Web-of-Science-Categories?language = en_US.

[18] Delgado-Chaves F.M., Jennings M.J., Atalaia A., Wolff J., Horvath R., Mamdouh Z.M., Baumbach J., Baumbach L., 2025. Transforming literature screening: the emerging role of large language models in systematic reviews. Proc. Natl. Acad. Sci. U.S.A. 122 (2), e2411962122. doi: 10.1073/pnas.2411962122.

[19] Devlin J., Chang M.-W., Lee K., Toutanova K., 2019. BERT:pre-training of deep bidirectional transformers for language understanding. In: Jill Burstein C.D., Eds.), Solorio, Thamar (Proceedings of the 2019 Conference of the North. Association for Computational Linguistics, Minneapolis, Minnesota, pp. 4171-4186.

[20] Domingos P., 2012. A few useful things to know about machine learning. Commun. ACM 55 (10), 78-87. doi: 10.1145/2347736.234775.

[21] Dubey A., Jauhri A., Pandey A., Kadian A., Al-Dahle A., Letman A., Mathur A., Schelten A., Yang A., Fan A., 2024. The Llama 3 herd of models. arXiv. https://doi.org/10.48550/arXiv.2407.21783.

[22] Ebrahimi M.K.V., Lee H., Won J., Kim S., Park S.S., 2023. Estimation of soil texture by fusion of near-infrared spectroscopy and image data based on convolutional neural network. Comput. Electron. Agric. 212, 108117. doi: 10.1016/j.compag.2023.108117.

[23] Fan Z., Zhang F., Loo B.P.Y., Ratti C., 2023. Urban visual intelligence: uncovering hidden city profiles with street view images. Proc. Natl. Acad. Sci. U.S.A. 120 (27), e2220417120. doi: 10.1073/pnas.2220417120.

[24] Fu B., 2017. Geography: from knowledge, science to decision making support. Acta Geogr. Sin. 72 (11), 1923-1932. doi: 10.11821/dlxb201711001, (in Chinese).

[25] Fu B., 2025. Hotspots and frontiers in geography. Teach. Ref. Middle School Geogr. (1) 25-28 (in Chinese).

[26] Gao S., 2024. Artificial intelligence and human geography. In: Warf B. (Ed.), The Encyclopedia of Human Geography. Springer Cham, pp. 1-7.

[27] Goodchild M.F., 2009. Geographic information systems and science: today and tomorrow. Ann. GIS 15 (1), 3-9. doi: 10.1016/j.proeps.2009.09.160.

[28] Goodfellow I.J., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y., 2014. Generative adversarial nets. In: Advances in Neural Information Processing Systems. MIT Press, Montreal, Canada, pp. 2672-2680.

[29] Gorelik A.J., Gorelik M.G., Ridout K.K., Nimarko A.F., Peisch V., Kuramkote S.R., Low M., Pan T., Singh S., Nrusimha A., Singh M.K., 2023. Evaluating efficiency and accuracy of deep-learning-based approaches on study selection for psychiatry systematic reviews. Nat. Ment. Health 1 (9), 623-632. doi: 10.1038/s44220-023-00109-w.

[30] Grootendorst M., 2022. BERTopic: neural topic modeling with a class-based TF-IDF procedure. arXiv. doi: 10.48550/arXiv.2203.05794.

[31] Gupta S., Langhans S.D., Domisch S., Fuso-Nerini F., Felländer A., Battaglini M., Tegmark M., Vinuesa R., 2021. Assessing whether artificial intelligence is an enabler or an inhibitor of sustainability at indicator level. Transp. Eng. 4, 100064. doi: 10.1016/j.treng.2021.100064.

[32] Halterman A., 2017. Mordecai: full text geoparsing and event geocoding. J. Open Source Softw. 2 (9), 91. doi: 10.21105/joss.00091.

[33] He H., Ma Y., 2013. Imbalanced Learning:Foundations, Algorithms, and Applications. Wiley-IEEE Press.

[34] Ho J., Jain A., Abbeel P., 2020. Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems. Curran Associates, Inc., Virtual Conference, pp. 6840-6851.

[35] Hochmair H.H., Juhász L., Li H., 2025. Advancing AI-driven geospatial analysis and data generation: methods, applications and future directions. ISPRS Int. J. Geo-Inf. 14 (2), 56. doi: 10.3390/ijgi14020056.

[36] Houlsby N., Giurgiu A., Jastrzebski S., Morrone B., De Laroussilhe Q., Gesmundo A., Attariyan M., Gelly S., 2019. Parameter-efficient transfer learning for NLP. In: Proceedings of the 36th International Conference on Machine Learning. PMLR, pp. 2790-2799.

[37] Husom E.J., Goknil A., Astekin M., Shar L.K., Kåsen A., Sen S., Mithassel B.A., Soylu A., 2025. Sustainable llm inference for edge AI: evaluating quantized llms for energy efficiency, output accuracy, and inference latency. arXiv. https://doi.org/10.48550/arXiv.2504.03360.

[38] Janowicz K., Gao S., McKenzie G., Hu Y., Bhaduri B., 2020. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. Int. J. Geogr. Inf. Sci. 34 (4), 625-636. doi: 10.1080/13658816.2019.1684500.

[39] Jumper J., Evans R., Pritzel A., Green T., Figurnov M., Ronneberger O., Tunyasuvunakool K., Bates R., Ž ídek A., Potapenko A., Bridgland A., Meyer C., Kohl S.A.A., Ballard A.J., Cowie A., Romera-Paredes B., Nikolov S., Jain R., Adler J., Back T., Petersen S., Reiman D., Clancy E., Zielinski M., Steinegger M., Pacholska M., Berghammer T., Bodenstein S., Silver D., Vinyals O., Senior A.W., Kavukcuoglu K., Kohli P., Hassabis D., 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596 (7873), 583-589. doi: 10.1038/s41586-021-03819-2.

[40] Kang Y., Gao S., Roth R.E., 2024. Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethics. Cartogr. Geogr. Inf. Sci. 51 (4), 599-630. doi: 10.1080/15230406.2023.2295943.

[41] Krenn M., Buffoni L., Coutinho B., Eppel S., Foster J.G., Gritsevskiy A., Lee H., Lu Y., Moutinho J.P., Sanjabi N., Sonthalia R., Tran N.M., Valente F., Xie Y., Yu R., Kopp M., 2023. Forecasting the future of artificial intelligence with machine learningbased link prediction in an exponentially growing knowledge network. Nat. Mach. Intell. 5 (11), 1326-1335. doi: 10.1038/s42256-023-00735-0.

[42] Krizhevsky A., Sutskever I., Hinton G.E., 2012. Imagenet classification with deep convolutional neural networks. In: Fernando Pereira C.J.C.B., Bottou L., Weinberger K.Q. (Conference on Neural Information Processing Systems. Curran Associates Inc., Lake Tahoe, Nevada USA,Eds.), pp.1097-1105.

[43] Lamb W.F., Creutzig F., Callaghan M.W., Minx J.C., 2019. Learning about urban climate solutions from case studies. Nat. Clim. Chang. 9 (4), 279-287. doi: 10.1038/s41558-019-0440-x.

[44] Li K., Rollins J., Yan E., 2017. Web of Science use in published research and review papers 1997-2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics 115 (1), 1-20. doi: 10.1007/s11192-017-2622-5.

[45] Lin Z., Chen A., Wang X., Liu Z., Piao S., 2024. Large language models reveal big disparities in current wildfire research. Commun. Earth Environ. 5 (1), 168. doi: 10.1038/s43247-024-01341-7.

[46] Ma J., Zhang L., Sun Y., 2020. ROI extraction based on multiview learning and attention mechanism for unbalanced remote sensing data set. IEEE Trans. Geosci. Remote Sens. 58 (9), 6210-6223. doi: 10.1109/TGRS.2020.2975380.

[47] Ma L., Liu Y., Zhang X., Ye Y., Yin G., Johnson B.A., 2019. Deep learning in remote sensing applications: a meta-analysis and review. ISPRS-J. Photogramm. Remote Sens. 152, 166-177. doi: 10.1016/j.isprsjprs.2019.04.015.

[48] Mai G., Xie Y., Jia X., Lao N., Rao J., Zhu Q., Liu Z., Chiang Y.-Y., Jiao J., 2025. Towards the next generation of Geospatial Artificial intelligence. Int. J. Appl. Earth Obs. Geoinf. 136, 103300. doi: 10.1016/j.jag.2025.104368.

[49] Matthews J.A., Herbert D.T., 2008. Geography: A Very Short Introduction, 1st ed.ed. Oxford University Press, Oxford, UK.

[50] Nguyen-Kim S., Nguyen-The V., Nguyen D.-B., 2023. A systematic review of artificial intelligence in geographic. In: Nghia P.T., Thai V.D., Thuy N.T., Son L.H., Huynh V.N. (International Conference on Advances in Information and Communication Technology.Eds.), Springer Cham, Thai Nguyen, Vietnam, pp. 20-31.

[51] Nieva H.R., Bakken S., Elhadad N., 2024. Mining the health disparities and minority health bibliome: a computational scoping review and gap analysis of 200,000+ articles. Sci. Adv. 10 (4), eadf9033. doi: 10.1126/sciadv.adf9033.

[52] Nowell L., Paolucci A., Dhingra S., Jacobsen M., Lorenzetti D.L., Lorenzetti L., Oddone-Paolucci E., 2022. Interdisciplinary mixed methods systematic reviews: reflections on methodological best practices, theoretical considerations, and practical implications across disciplines. Soc. Sci. Humanit. Open 6 (1), 100295. doi: 10.1016/j.ssaho.2022.100295.

[53] Nyberg B., Henstra G., Gawthorpe R.L., Ravnås R., Ahokas J., 2023. Global scale analysis on the extent of river channel belts. Nat. Commun. 14 (1), 2163. doi: 10.1038/s41467-023-37852-8.

[54] Oubara A., Wu F., Maleki R., Ma B., Amamra A., Yang G., 2024. Enhancing adversarial learning-based change detection in imbalanced datasets using artificial image generation and attention mechanism. ISPRS Int. J. Geo-Inf. 13 (4), 125. doi: 10.3390/ijgi13040125.

[55] Pichler M., Boreux V., Klein A.-M., Schleuning M., Hartig F., 2020. Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks. Methods Ecol. Evol. 11 (2), 281-293. doi: 10.1111/2041-210X.13329.

[56] Powell J., 1992. Historical geography and the history of geographical thought. GeoJournal 26 (2), 230-232. doi: 10.1007/BF00241228.

[57] Polak M.P., Morgan D., 2024. Extracting accurate materials data from research papers with conversational language models and prompt engineering. Nat. Commun. 15 (1), 1569. doi: 10.1038/s41467-024-45914-8.

[58] Rafiq K., Beery S., Palmer M.S., Harchaoui Z., Abrahms B., 2025. Generative AI as a tool to accelerate the field of ecology. Nat. Ecol. Evol. 9 (3), 378-385. doi: 10.1038/s41559-024-02623-1.

[59] Rocha J., Abrantes P., 2019. Geographic Information Systems and Science, 4th ed. John Wiley & Sons, Hoboken, New Jersey.

[60] Schmidt J., Schutte N.M., Buttigieg S., Novillo-Ortiz D., Sutherland E., Anderson M., de Witte B., Peolsson M., Unim B., Pavlova M., Stern A.D., Mossialos E., van Kessel R., 2024. Mapping the regulatory landscape for artificial intelligence in health within the European Union. npj Digit. Med. 7 (1), 229. doi: 10.1038/s41746-024-01221-6.

[61] Shen Z., Liu Z., Qin J., Savvides M., Cheng K.-T., 2021. Partial is better than all:revisiting fine-tuning strategy for few-shot learning. In: Proceedings of the AAAI conference on artificial intelligence, pp. 9594-9602.

[62] Song G., 2020. A review of recent researches and reflections on geospatial artificial intelligence. Geomat. Inf. Sci. Wuhan Univ. 45 (12), 1865-1874. doi: 10.13203/j.whugis20200597.

[63] Song Y., Kalacska M., Ga š parovi ć M., Yao J., Najibi N., 2023. Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping. Int. J. Appl. Earth Obs. Geoinf. 120, 103300. doi: 10.1016/j.jag.2023.103300.

[64] Sun Z., Sandoval L., Crystal-Ornelas R., Mousavi S.M., Wang J., Lin C., Cristea N., Tong D., Carande W.H., Ma X., Rao Y., Bednar J.A., Tan A., Wang J., Purushotham S., Gill T.E., Chastang J., Howard D., Holt B., Gangodagamage C., John A., 2022. A review of Earth Artificial Intelligence. Comput. Geosci. 159, 105034. doi: 10.1016/j.cageo.2022.105034.

[65] Tripathi S., Bachmann N., Brunner M., Rizk Z., Jodlbauer H., 2024. Assessing the current landscape of AI and sustainability literature: identifying key trends, addressing gaps and challenges. J. Big Data 11 (1), 65. doi: 10.1186/s40537-024-00912-x.

[66] Tuia D., Kellenberger B., Beery S., Costelloe B.R., Zuffi S., Risse B., Mathis A., Mathis M.W., van Langevelde F., Burghardt T., Kays R., Klinck H., Wikelski M., Couzin I.D., van Horn G., Crofoot M.C., Stewart C.V., Berger-Wolf T., 2022. Perspectives in machine learning for wildlife conservation. Nat. Commun. 13 (1), 792. doi: 10.1038/s41467-022-27980-y.

[67] van de Schoot, R., Schram R., Zahedi P., Weijdema F., Kramer B., Huijts M., Hoogerwerf M., Ferdinands G., Harkema A.,Willemsen, J., Ma, Y., Fang, Q., Hindriks, S., Tummers, L., Oberski, D.L., van de Schoot, R., de Bruin, J., Schram, R., Zahedi, P., de Boer, J., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., Harkema, A., Willemsen, J., Ma, Y., Fang, Q., Hindriks, S., Tummers, L., Oberski, D.L., 2021. An open source machine learning framework for efficient and transparent systematic reviews. Nat. Mach. Intell. 3, 125-133. doi: 10.1038/s42256-020-00287-7.

[68] Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser Ł., Polosukhin I., 2017. Attention is all you need. In: Guyon I., Luxburg U.V., Bengio S., Wallach H., Fergus R., Vishwanathan S., Garnett R. (Advances in Neural Information Processing Systems 30. Curran Associates, Inc., Long Beach CA,Eds.), USA, pp. 5998-6008.

[69] Vinuesa R., Azizpour H., Leite I., Balaam M., Dignum V., Domisch S., Felländer A., Langhans S.D., Tegmark M., Fuso Nerini F., 2020. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 11 (1), 233. doi: 10.1038/s41467-019-14108-y.

[70] Wang H., Fu T., Du Y., Gao W., Huang K., Liu Z., Chandak P., Liu S., Van Katwyk P., Deac A., Anandkumar A., Bergen K., Gomes C.P., Ho S., Kohli P., Lasenby J., Leskovec J., Liu T.-Y., Manrai A., Marks D., Ramsundar B., Song L., Sun J., Tang J., Veli čkovi ć P., Welling M., Zhang L., Coley C.W., Bengio Y., Zitnik M., 2023. Scientific discovery in the age of artificial intelligence. Nature 620 (7972), 47-60. doi: 10.1038/s41586-023-06221-2.

[71] Wang S., Huang X., Liu P., Zhang M., Biljecki F., Hu T., Fu X., Liu L., Liu X., Wang R., Huang Y., Yan J., Jiang J., Chukwu M., Naghedi S.R., Hemmati M., Shao Y., Jia N., Xiao Z., Tian T., Bao S., 2024a. Mapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography: an extensive systematic review. Int. J. Appl. Earth Obs. Geoinf. 128, 103734. doi: 10.1016/j.jag.2024.103734.

[72] Wang Y., Qu W., Ye X., 2024b. Selecting between BERT and GPT for text classification in political science research. arXiv. https://doi.org/10.48550/arXiv.2411.05050.

[73] Weng Q., Li Z., Cao Y., Lu X., Gamba P., Zhu X., Xu Y., Zhang F., Qin R., Yang M.Y., Ma P., Huang W., Yin T., Zheng Q., Zhou Y., Asner G., 2024. How will AI transform urban observing, sensing, imaging, and mapping? npj Urban Sustain. 4 (1), 50. doi: 10.1038/s42949-024-00188-3.

[74] Xi J., Liu X., Wang J., Yao L., Zhou C., 2023. A systematic review of COVID-19 geographical research: machine learning and bibliometric approach. Ann. Am. Assoc. Geogr. 113 (3), 581-598. doi: 10.1080/24694452.2022.2130143.

[75] Xu H., Zhang C., 2022. Development and applications of GIS-based spatial analysis in environmental geochemistry in the big data era. Environ. Geochem. Health 45 (4), 1079-1090. doi: 10.1007/s10653-021-01183-8.

[76] Yu D., Fang C., 2023. Urban remote sensing with spatial big data: a review and renewed perspective of urban studies in recent decades. Remote Sens 15 (5), 1307. doi: 10.3390/rs15051307.

[77] Zander K.K., Garnett S.T., Sterly H., Ayeb-Karlsson S., Š edová B., Lotze-Campen H., Richerzhagen C., Baggen H.S., 2022. Topic modelling exposes disciplinary divergence in research on the nexus between human mobility and the environment. Hum. Soc. Sci. Commun. 9 (1), 34. doi: 10.1057/s41599-022-01038-2.

[78] Zhang L., Zhang L., 2022. Artificial intelligence for remote sensing data analysis: a review of challenges and opportunities. IEEE Geosci. Remote Sens. Mag. 10 (2), 270-294. doi: 10.1109/MGRS.2022.3145854.

[79] Zhang W., Zhou T., Wu P., 2024. Anthropogenic amplification of precipitation variability over the past century. Science 385 (6707), 427-432. doi: 10.1126/science.adp0212.

[80] Zhao T., Wang S., Ouyang C., Chen M., Liu C., Zhang J., Yu L., Wang F., Xie Y., Li J., Wang F., Grunwald S., Wong B.M., Zhang F., Qian Z., Xu Y., Yu C., Han W., Sun T., Shao Z., Qian T., Chen Z., Zeng J., Zhang H., Letu H., Zhang B., Wang L., Luo L., Shi C., Su H., Zhang H., Yin S., Huang N., Zhao W., Li N., Zheng C., Zhou Y., Huang C., Feng D., Xu Q., Wu Y., Hong D., Wang Z., Lin Y., Zhang T., Kumar P., Plaza A., Chanussot J., Zhang J., Shi J., Wang L., 2024. Artificial intelligence for geoscience: progress, challenges, and perspectives. Innovation 5 (5), 100691. doi: 10.1016/j.xinn.2024.100691.

[81] Zheng Y., Lin Y., Zhao L., Wu T., Jin D., Li Y., 2023. Spatial planning of urban communities via deep reinforcement learning. Nat. Comput. Sci. 3 (9), 748-762. doi: 10.1038/s43588-023-00503-5.

[82] Zhou C., Wang H., Wang C., Hou Z., Zheng Z., Shen S., Cheng Q., Feng Z., Wang X., Lv H., Fan J., Hu X., Hou M., Zhu Y., 2021. Geoscience knowledge graph in the big data era. Sci. China-Earth Sci. 64 (7), 1105-1114. doi: 10.1007/s11430-020-9750-4.

[83] Zhou T., 2023. Application of artificial intelligence in geography. J. Phys. Conf. Ser. 2646 (1), 012006. doi: 10.1088/1742-6596/2646/1/012006.