Scholarly record
GIS EDUCATION FOR CLIMATE RESILIENCE INTEGRATING DIGITAL TOOLS AND AI-BASED IMAGE ANALYSIS
Abstract
The integration of Geographic Information Systems (GIS) into higher education represents a transformative pathway for preparing students to address global environmental challenges. While GIS training traditionally emphasizes spatial data handling and map-making, its potential for capacity building is significantly enhanced when combined with emerging artificial intelligence (AI) tools. This paper presents an interdisciplinary educational model that aligns with SDG 4 (Quality Education) and SDG 13 (Climate Action), where students engage not only with digital geospatial platforms such as QGIS, PostgreSQL/PostGIS, and web mapping frameworks, but also with AI-supported image analysis. In addition to conventional tasks such as hazard mapping, urban sustainability analysis, and resource monitoring, an experimental module introduced the use of a pretrained CLIP model for categorizing photographs of environmental issues. Example applications included classifying levels of forest fire damage, detecting signs of deforestation, and identifying indicators of water pollution. By storing these AI-generated classifications within a spatial database, students learned how unstructured image data can be transformed into structured, queryable information for environmental monitoring. The integration pipeline connected the CLIP zero-shot image classifier with PostGIS spatial layers, enabling automatic labeling of geotagged photographs directly within GIS environments for analysis and visualization. The outcomes highlight improved technical fluency across GIS and AI tools, enhanced systems thinking through the integration of geospatial and machine learning methods, and increased awareness of ethical and sustainability implications in digital monitoring. This approach equips students with interdisciplinary competencies required to contribute to climate resilience and sustainable resource management.
Publication Impact Profile
Publication details
References15
Hysenaj, M., & Barjami, R., GIS integration and evolution into the Albanian system education and market. International Journal of Modern Manufacturing Technologies, 4(2), pp. 33 39, 2012.
UNESCO. Education for sustainable development goals: Learning objectives. UNESCO Publishing. DOI: 10.54675/CGBAP153, 2017.
Milson, A. J., Demirci, A., & Kerski, J. J. (Eds.)., International perspectives on teaching and learning with GIS in secondary schools. Springer. DOI: 10.1007/978-94-007-2120-3, 2012.
Demirci, A., How do teachers approach new technologies: Geography teachers attitudes towards geographic information systems (GIS). European Journal of Educational Studies, 1(1), pp. 43 53, 2009.
Bednarz, S. W., Geographic information systems: A tool to support geography and environmental education? GeoJournal, 60(2), pp. 191 199, 2004. DOI: 10.1023/B:GEJO.0000033574.44345.c9
Hysenaj, M., Development of GIS technologies and methods in education. Applied Computer Science, 11(4), pp. 30-41, 2015.
Krajcik, J. S., & Blumenfeld, P. C., Project-based learning. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (pp. 317 334). Cambridge University Press, 2006. DOI: 10.1017/cbo9780511816833.020
Blumenfeld, P. C., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palincsar, A., Motivating project-based learning: Sustaining the doing, supporting the learning. Educational Psychologist, 26(3 4), pp. 369-398, 1991 DOI: 10.1080/00461520.1991.9653139
Hmelo-Silver, C. E., Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), pp. 235-266, 2004 DOI: 10.1023/B:EDPR.0000034022.16470.f3
Blumenfeld, P. C., Krajcik, J. S., Marx, R. W., & Soloway, E., Lessons learned: How collaboration in project-based learning can enhance student learning. The Elementary School Journal, 94(5), pp. 539-551, 1994. DOI: 10.1086/461782
Goodchild, M. F., Reimagining the history of GIS. Annals of GIS, 24(1), pp. 1-8. DOI: 10.1080/19475683.2018.1424737, 2018.
Kerski, J. J., Geo-awareness, geo-enablement, geotechnologies, citizen science, and storytelling: Geography on the world stage. Geography Compass, 9(1), pp. 14-26. DOI: 10.1111/gec3.12193, 2015.
Hysenaj, M., & Barjami, R., Web GIS Albania platform, an informative technology for the Albanian territory. Informatica, 36(4), pp. 431-439, 2012.
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F., Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), pp. 8-36, 2017. DOI: 10.1109/MGRS.2017.2762307
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I., Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning, 139, pp. 8748 8763. PMLR, 2021. http://proceedings.mlr.press/v139/radford21a.html
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.