Using geospatial artificial intelligence to advance the Sustainable Development Goals
Chenzhe Fan , Chunhui Wang , Jing Song , Huilin Yu , Gang Li
Geography and Sustainability ›› 2026, Vol. 7 ›› Issue (3) : 100476
| [1] |
Beale, C.M., Lennon, J.J., Yearsley, J.M., Brewer, M.J., Elston, D.A., 2010. Regression analysis of spatial data. Ecol. Lett. 13 (2), 246-264. doi: 10.1111/j.1461-0248.2009.01422.x. |
| [2] |
Grekousis, G., 2019. Artificial neural networks and deep learning in urban geography: a systematic review and meta-analysis. Comput. Environ. Urban Syst. 74, 244-256. doi: 10.1016/j.compenvurbsys.2018.10.008. |
| [3] |
Imanian, H., Shirkhani, H., Mohammadian, A., Cobo, J.H., Payeur, P., 2023. Spatial interpolation of soil temperature and water content in the land-water interface using artificial intelligence. Water 15 (3), 473. doi: 10.3390/w15030473. |
| [4] |
Janowicz, K., Gao, S., McKenzie, G., Hu, Y.J., 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. |
| [5] |
Mai, G.C., Hu, Y.J., Gao, S., Cai, L., Martins, B., Scholz, J., Gao, J., Janowicz, K., 2022. Symbolic and subsymbolic GeoAI: geospatial knowledge graphs and spatially explicit machine learning. Trans. GIS 26 (8), 3118-3124. doi: 10.1111/tgis.13012. |
| [6] |
Nilsson, M., Chisholm, E., Griggs, D., Howden-Chapman, P., McCollum, D., Messerli, P., Neumann, B., Stevance, A.S., Visbeck, M., Stafford-Smith, M., 2018. Mapping interactions between the sustainable development goals: lessons learned and ways forward. Sustain. Sci. 13 (6), 1489-1503. doi: 10.1007/s11625-018-0604-z. |
| [7] |
Ouchra, H., Belangour, A., Erraissi, A., 2023. An overview of GeoSpatial Artificial Intelligence technologies for city planning and development. In: 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT). February 22-24, 2023, Erode, India. IEEE, pp. 1-7. doi: 10.1109/ICECCT56650.2023.10179796. |
| [8] |
Song, J., Wang, C.H., Nunes, L.M., Liang, Z.Y., Li, G., 2025. The historical distribution and future expansion of paddy rice fields in Asian Highlands. npj Sustain. Agric. 3, 65. doi: 10.1038/s44264-025-00107-8. |
| [9] |
Tahmasebi, P., Kamrava, S., Bai, T., Sahimi, M., 2020. Machine learning in geo- and environmental sciences: from small to large scale. Adv. Water Resour. 142, 103619. doi: 10.1016/j.advwatres.2020.103619. |
| [10] |
Tian, F.Y., Wu, B.F., Zeng, H.W., Watmough, G.R., Zhang, M., Li, Y.R., 2022. Detecting the linkage between arable land use and poverty using machine learning methods at global perspective. Geogr. Sustain. 3 (1), 7-20. doi: 10.1016/j.geosus.2022.01.001. |
| [11] |
|
| [12] |
|
| [13] |
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, 233. doi: 10.1038/s41467-019-14108-y. |
| [14] |
Xie, Y.Q., Eftelioglu, E., Ali, R., Tang, X., Li, Y., Doshi, R., Shekhar, S., 2017. Transdisciplinary foundations of geospatial data science. ISPRS Int. J. Geo-Inf. 6 (12), 395. doi: 10.3390/ijgi6120395. |
| [15] |
Yang, W.T., Wan, X.F., Deng, M., 2024. Spatial ensemble learning for predicting the potential geographical distribution of invasive species. Int. J. Geogr. Inf. Sci. 38 (11), 2216-2234. doi: 10.1080/13658816.2024.2376325. |
/
| 〈 |
|
〉 |