Title: A Systematic Review of Open-Source GIS Platforms: Capabilities, Limitations, and Adoption
Keywords:
Geographical Information System (GIS), Remote Sensing, Artificial Intelligence, Open-Source GIS, Spatial Analysis.
Abstract
The integration of Geographic Information Systems (GIS) with remote sensing and artificial intelligence (AI) is transforming the landscape of spatial analysis. This review explores the evolution, capabilities, and adoption of prominent open source GIS platforms including QGIS, GRASS GIS, gvSIG, SAGA GIS, ILWIS, and Whitebox GAT. These tools have emerged as robust alternatives to proprietary software, offering flexibility, extensibility, and community-driven innovation. The paper examines their origins, key features, licensing models, and comparative strengths in handling raster-vector analysis, plugin support, and web-based mapping. It further highlights adoption trends in academia, government, and non-profit sectors, along with educational integration and migration patterns from commercial platforms. Current limitations such as steep learning curves and performance with large datasets are addressed. The review concludes by outlining future directions, including AI integration, real time analytics, cloud deployment, and enhanced interoperability. By synthesizing literature and case examples, this paper underscores how open-source GIS, in combination with remote sensing and AI, is shaping a new era in spatial intelligence.
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36. Lemenkova, P., SAGA GIS for computing multispectral vegetation indices by Landsat TM for mapping vegetation greenness. Contemporary Agriculture, 2021. 70(1-2): p. 67-75.
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39. Alamri, S., The geospatial crowd: emerging trends and challenges in crowdsourced spatial analytics. ISPRS International Journal of Geo-Information, 2024. 13(6): p. 168.
40. Alam, M.M., L. Torgo, and A. Bifet, A survey on spatio-temporal data analytics systems. ACM Computing Surveys, 2022. 54(10s): p. 1-38.
41. Lemenkova, P., Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy. Journal of Imaging, 2025. 11(5): p. 153.
42. Pettit, C., et al., Open access, open source and cloud computing: a glimpse into the future of GIS. Handbook of Planning Support Science, 2020: p. 56-71.
43. Li, W., M. Batty, and M.F. Goodchild, Real-time GIS for smart cities. 2020, Taylor & Francis. p. 311-324.
44. Bermudez, L., New frontiers on open standards for geo-spatial science. Geo-spatial information science, 2017. 20(2): p. 126-133.
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2. Patel, J., N. Sharma, and S. Mohan, Introduction to Remote Sensing and GIS, in Smart Buildings and Cities with Remote Sensing and GIS. 2025, Chapman and Hall/CRC. p. 3-34.
3. Zhang, B., et al., Progress and challenges in intelligent remote sensing satellite systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022. 15: p. 1814-1822.
4. Prokakis, E., Free and Open-Source Software: Freedom, Transparency and Efficiency in the Digitalization Era. Journal of Politics and Ethics in New Technologies and AI, 2022. 1(1): p. e31230-e31230.
5. Löwe, P., et al., Open Source–GIS, in Springer Handbook of Geographic Information. 2022, Springer. p. 807-843.
6. Hu, T., et al., FAIR principles in workflows: A GIScience workflow management system for reproducible and replicable studies. International Journal of Applied Earth Observation and Geoinformation, 2025. 138: p. 104477.
7. Daud, M., F.M. Ugliotti, and A. Osello, Comprehensive analysis of the use of Web-GIS for natural hazard management: A systematic review. Sustainability, 2024. 16(10): p. 4238.
8. de Lange, N., Geoinformation systems, in Geoinformatics in Theory and Practice: An Integrated Approach to Geoinformation Systems, Remote Sensing and Digital Image Processing. 2023, Springer. p. 375-433.
9. Lemenkova, P., Reclassification Scheme for Image Analysis in GRASS GIS Using Gradient Boosting Algorithm: A Case of Djibouti, East Africa. Journal of Imaging, 2025. 11(8): p. 249.
10. Lemenkova, P. and O. Debeir, GDAL and PROJ libraries integrated with grass GIS for terrain modelling of the georeferenced raster image. Technologies, 2023. 11(2): p. 46.
11. Antunes, C., et al., Environmental Noise-Integrating and sharing geographic data sets. REVISTA INTERNACIONAL MAPPING, 2023. 32(210): p. 28-34.
12. La Licata, M., et al., FTIR-derived soil degradation indices and stochastic modelling of organic matter–sediment dynamics in a Mediterranean watershed: A Northern Apennines case study. PLoS One, 2025. 20(8): p. e0330252.
13. Pearson, R.A., et al., GeoFabrics 1.0. 0: An open-source Python package for automatic hydrological conditioning of digital elevation models for flood modelling. Environmental Modelling & Software, 2023. 170: p. 105842.
14. Mitra, S., et al., An Overview of Morphometry Software Packages, Tools, and Add‐ons. Advances in Remote Sensing Technology and the Three Poles, 2022: p. 49-57.
15. Singh, A. and V. Vyas, A Review on remote sensing application in river ecosystem evaluation. Spatial Information Research, 2022. 30(6): p. 759-772.
16. Ho, Y.-F., et al., GEDTM30: global ensemble digital terrain model at 30 m and derived multiscale terrain variables. PeerJ, 2025. 13: p. e19673.
17. Xia, J., et al., Open Vector Analysis, in Open GIS. 2024, Springer. p. 175-198.
18. Sharma, S., et al., Remote Sensing and GIS in Natural Resource Management: Comparing tools and emphasizing the importance of in-situ data. Remote Sensing, 2024. 16(22): p. 4161.
19. Malakar, K.D., S. Roy, and M. Kumar, Practical approaches to GIS data analysis, in Geospatial Technologies in Coastal Ecologies Monitoring and Management. 2025, Springer. p. 159-190.
20. Kaushik, P. and S. Jabin, Advancements and Challenges in Business Applications of SAR Images, in Big Data Analytics. 2021, Auerbach Publications. p. 87-111.
21. Lemenkova, P., Big Earth data processing using machine learning for integrated mapping of the dead sea fault, Jordan. Glasnik Šumarskog fakulteta Univerziteta u Banjoj Luci, 2021(31): p. 79-103.
22. AbdelRahman, M.A. and A.A. Afifi, Digital mapping of soil properties using geomatics: integration of GIS, GPS, and remote sensing applications. Arabian Journal of Geosciences, 2024. 17(12): p. 330.
23. Lamas, V., et al., GIS-Publisher: Simplifying web-based GIS application development for enhanced data dissemination. SoftwareX, 2024. 28: p. 101942.
24. Duarte, L. and A.C. Teodoro, GIS open-source plugins development: A 10-year bibliometric analysis on scientific literature. Geomatics, 2021. 1(2): p. 206-245.
25. Thakuri, S., et al., Open-source data alternatives and models for flood risk management in Nepal. Remote Sensing, 2022. 14(22): p. 5660.
26. Wang, C., et al., CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation. International Journal of Geographical Information Science, 2025: p. 1-34.
27. Gnanappazham, L., K.A. Prasad, and V. Dadhwal, Geospatial tools for mapping and monitoring coastal mangroves, in Mangroves: Ecology, Biodiversity and Management. 2021, Springer. p. 475-551.
28. Chen, C., J. Judge, and D. Hulse, PyLUSAT: An open-source Python toolkit for GIS-based land use suitability analysis. Environmental Modelling & Software, 2022. 151: p. 105362.
29. Chen, G., et al., GeoJSEval: an automated evaluation framework for large language models on JavaScript-based geospatial computation and visualization code generation. ISPRS International Journal of Geo-Information, 2025. 14(10): p. 382.
30. Kumar, N., et al., Open-source satellite data and GIS for land resource mapping, in Geospatial Technologies in Land Resources Mapping, Monitoring and Management. 2018, Springer. p. 185-200.
31. Girgin, S., J. Gohil, and I. Mydur, A streamlined GIS interface for Citizen Science activities: QGIS Light. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2025. 48: p. 127-134.
32. Szemán, I., Comparison of the most popular open-source GIS software in the field of landscape ecology. Acta Geographica Debrecina Landscape & Environment series, 2012. 6(2): p. 76-92.
33. Sui, D., Opportunities and impediments for open GIS. Transactions in GIS, 2014. 18(1): p. 1-24.
34. Mason, B.C. and S. Dragicevic, Web GIS and knowledge management systems: An integrated design for collaborative community planning, in Collaborative geographic information systems. 2006, IGI Global Scientific Publishing. p. 263-284.
35. Ahmad, M., Mapping for Good: Harnessing OpenStreetMap for Corporate Philanthropy, in Navigating Business Through Essential Sustainable Strategies. 2025, IGI Global. p. 279-302.
36. Lemenkova, P., SAGA GIS for computing multispectral vegetation indices by Landsat TM for mapping vegetation greenness. Contemporary Agriculture, 2021. 70(1-2): p. 67-75.
37. Abernathy, D.R., The next layer: Towards open pedagogy in geospatial education. Transactions in GIS, 2023. 27(5): p. 1467-1478.
38. Hazra, S., K.S. Bora, and P.A. Singh, Mapping World: A Comparative Analysis of Functionality and Usability between QGIS and ArcGIS. Recent Advances in Computer Science and Communications, 2025. 18(4): p. E26662558332529.
39. Alamri, S., The geospatial crowd: emerging trends and challenges in crowdsourced spatial analytics. ISPRS International Journal of Geo-Information, 2024. 13(6): p. 168.
40. Alam, M.M., L. Torgo, and A. Bifet, A survey on spatio-temporal data analytics systems. ACM Computing Surveys, 2022. 54(10s): p. 1-38.
41. Lemenkova, P., Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy. Journal of Imaging, 2025. 11(5): p. 153.
42. Pettit, C., et al., Open access, open source and cloud computing: a glimpse into the future of GIS. Handbook of Planning Support Science, 2020: p. 56-71.
43. Li, W., M. Batty, and M.F. Goodchild, Real-time GIS for smart cities. 2020, Taylor & Francis. p. 311-324.
44. Bermudez, L., New frontiers on open standards for geo-spatial science. Geo-spatial information science, 2017. 20(2): p. 126-133.
45. Jelokhani-Niaraki, M., Collaborative spatial multicriteria evaluation: A review and directions for future research. International Journal of Geographical Information Science, 2021. 35(1): p. 9-42.
46. Malakar, K.D. and S. Roy, GIS for All: Challenges and Future Directions, in Mapping Geospatial Citizenship: The Power of Participatory GIS. 2024, Springer. p. 73-85.
47. Coetzee, S., et al., Open geospatial software and data: A review of the current state and a perspective into the future. ISPRS International Journal of Geo-Information, 2020. 9(2): p. 90.
Published
2025-10-16
How to Cite
Partab Rathore, Muhammad Junaid Ahmad, Muhammad Zain, Ayesha Shafiq, & Muhammad Junaid Bin Zulfiqar. (2025). Title: A Systematic Review of Open-Source GIS Platforms: Capabilities, Limitations, and Adoption. International Journal of Education, Vocational and Social Science , 4(04), 338-360. https://doi.org/10.63922/ijevss.v4i04.2386
Contact Person +6282139474255
Email : ijevss@citakonsultindo.or.id / editor.ijevss@gmail.com
