Remote Sensing and Ensemble Machine Learning for Illegal Dumpsite Susceptibility Mapping: A Review
DOI:
https://doi.org/10.47941/je.4089Keywords:
Illegal Dumping, Susceptibility Mapping, Remote Sensing, Ensemble Learning, Stacking, SVM, Random Forest, XGBoostAbstract
Purpose: This review asks whether, and under what conditions, combining satellite remote sensing with machine learning, and ensemble learning in particular, improves the mapping of illegal-dumpsite susceptibility, and where the evidence for that claim is still missing, with emphasis on sub-Saharan Africa and Nigeria.
Methodology: A structured narrative review with a non-systematic, purposive selection of studies was carried out. Candidate studies published from 2000 to 2026 were identified through targeted keyword searches of scholarly databases and search engines (Google Scholar, Scopus-indexed journals and African Journals Online), combining dumping terms (illegal dump, illegal landfill, dumpsite) with remote-sensing, susceptibility and machine-learning terms, and through citation chasing from recent surveys. Empirical studies that detected dumpsites or mapped dumping susceptibility with remote-sensing or GIS predictors and reported a validation outcome were included, and each was appraised for source type and validation design. Twenty-one empirical studies (16 international detection and susceptibility studies and 5 Nigerian studies) were synthesised, supported by methodological literature.
Findings: Susceptibility studies outside Africa report strong discrimination with comparatively simple models (logistic regression, overall accuracy 92–94%; discriminant analysis, AUC 0.97–0.98), and the largest head-to-head comparison (seven classifiers on the same data) found AUC values within a narrow range (0.86–0.89). The two African susceptibility studies (Dakar, Blantyre) used expert-weighted multicriteria analysis rather than machine learning. Only one study used spatial cross-validation, none used a stacking ensemble, and no Nigerian study has modelled susceptibility predictively.
Unique Contribution to Theory, Policy and Practice: Unlike recent surveys that focus on detecting waste in imagery, this review compares susceptibility-mapping methods on their reported performance and validation practice, maps the regional evidence gap for Nigeria, and sets out minimum standards (simple-model baselines, spatial validation and treatment of pseudo-absences) that future ensemble studies must meet before ensembles can be recommended to waste-management authorities.
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References
Aderoju, O. M., Dias, G. A., & Gonçalves, A. J. (2020). A GIS-based analysis for sanitary landfill sites in Abuja, Nigeria. Environment, Development and Sustainability, 22(1), 551–574. https://doi.org/10.1007/s10668-018-0206-z
Barbet-Massin, M., Jiguet, F., Albert, C. H., & Thuiller, W. (2012). Selecting pseudo-absences for species distribution models: How, where and how many? Methods in Ecology and Evolution, 3(2), 327–338.
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.
Biotto, G., Silvestri, S., Gobbo, L., Furlan, E., Valenti, S., & Rosselli, R. (2009). GIS, multi-criteria and multi-factor spatial analysis for the probability assessment of the existence of illegal landfills. International Journal of Geographical Information Science, 23(10), 1233–1244.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). ACM.
Daramola, S. O., & Makinde, E. O. (2024). Modeling air pollution around major dumpsites in Lagos State using geospatial methods with solutions. Environmental Challenges, 16, 100969.
Das, S., & Morshed, M. M. (2026). A GeoAI framework for detecting risk zones from illegal dumping sites in Khulna, Bangladesh. PLOS ONE, 21(3), e0345178. https://doi.org/10.1371/journal.pone.0345178
Du, L., Zuo, J., Vanzo, J., Chang, R., & Zillante, G. (2023). Assessing and predicting the illegal dumping risks in relation to road characteristics. Waste Management, 169, 332–341.
Džeroski, S., & Ženko, B. (2004). Is combining classifiers with stacking better than selecting the best one? Machine Learning, 54(3), 255–273.
Elkan, C., & Noto, K. (2008). Learning classifiers from only positive and unlabeled data. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 213–220). ACM.
Ferronato, N., & Torretta, V. (2019). Waste mismanagement in developing countries: A review of global issues. International Journal of Environmental Research and Public Health, 16(6), 1060.
Fraternali, P., Morandini, L., & Herrera González, S. L. (2024). Solid waste detection, monitoring and mapping in remote sensing images: A survey. Waste Management, 189, 88–102. https://doi.org/10.1016/j.wasman.2024.08.003
Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266.
Garaba, S. P., & Dierssen, H. M. (2018). An airborne remote sensing case study of synthetic hydrocarbon detection using short wave infrared absorption features identified from marine-harvested macro- and microplastics. Remote Sensing of Environment, 205, 224–235.
Geng, J., Ding, Y., Xie, W., Fang, W., Liu, M., Ma, Z., Yang, J., & Bi, J. (2024). An ensemble machine learning model to uncover potential sites of hazardous waste illegal dumping based on limited supervision experience. Fundamental Research, 4(4), 972–978. https://doi.org/10.1016/j.fmre.2023.06.010
Gill, J., Faisal, K., Shaker, A., & Yan, W. Y. (2019). Detection of waste dumping locations in landfill using multi-temporal Landsat thermal images. Waste Management & Research, 37(4), 386–393.
Giusti, L. (2009). A review of waste management practices and their impact on human health. Waste Management, 29(8), 2227–2239.
Glanville, K., & Chang, H.-C. (2015). Mapping illegal domestic waste disposal potential to support waste management efforts in Queensland, Australia. International Journal of Geographical Information Science, 29(6), 1042–1058. https://doi.org/10.1080/13658816.2015.1008002
Gómez Maturano, J., Mendoza Santana, J. D., Aguilar García, A. L., & Serna Hernández, M. (2024). Remote sensing of illegal dumps through supervised classification of satellite images: Application in Oaxaca, Mexico. Cuadernos de Investigación Geográfica, 50(2), 157–177. https://doi.org/10.18172/cig.6273
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621.
He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Schölkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and Their Applications, 13(4), 18–28.
Hojnik, B., Horvat, G., Mongus, D., Brumen, M., & Kamnik, R. (2025). Towards sustainable waste management: Predictive modelling of illegal dumping risk zones using circular data loops and remote sensing. Sustainability, 17(18), 8280. https://doi.org/10.3390/su17188280
Jazat, J. P., Akande, J. A., & Ogunbode, T. O. (2023). State of solid waste disposal and suggested fixes for Iwo and Ibadan Metropolis, Nigeria. Frontiers in Sustainability, 3, 1022519. https://doi.org/10.3389/frsus.2022.1022519
Jordá-Borrell, R., Ruiz-Rodríguez, F., & Lucendo-Monedero, Á. L. (2014). Factor analysis and geographic information system for determining probability areas of presence of illegal landfills. Ecological Indicators, 37, 151–160. https://doi.org/10.1016/j.ecolind.2013.10.001
Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9), 100804.
Karimi, N., Ng, K. T. W., & Richter, A. (2022). Development and application of an analytical framework for mapping probable illegal dumping sites using nighttime light imagery and various remote sensing indices. Waste Management, 143, 195–205.
Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a waste 2.0: A global snapshot of solid waste management to 2050. World Bank.
Kruse, C., Boyda, E., Chen, S., Karra, K., Bou-Nahra, T., Hammer, D., Mathis, J., Maddalene, T., Jambeck, J., & Laurier, F. (2023). Satellite monitoring of terrestrial plastic waste. PLOS ONE, 18(1), e0278997. https://doi.org/10.1371/journal.pone.0278997
Kuncheva, L. I., & Whitaker, C. J. (2003). Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 51(2), 181–207.
Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1), 33–41.
Lucendo-Monedero, A. L., Jordá-Borrell, R., & Ruiz-Rodríguez, F. (2015). Predictive model for areas with illegal landfills using logistic regression. Journal of Environmental Planning and Management, 58(7), 1309–1326. https://doi.org/10.1080/09640568.2014.993751
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (pp. 4765–4774).
McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432.
Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., Avtar, R., & Abderrahmane, B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 103225.
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259.
Mvula, R. L. S., Banda, Y. M., Njunju, M. A., Tholo, H. M., Chisenga, C., Nyengere, J., Njalam’mano, J., Eregno, F. E., & Kadewa, W. (2025). Mapping illegal dumping sites in a low-resource region using GIS and remote sensing: The case of Blantyre City, Malawi. Urban Science, 9(7), 254. https://doi.org/10.3390/urbansci9070254
Ndoma, E. E., Songu, G., & Buba, S. (2018). Mapping of existing solid waste dumpsites using geographic information system in Kaduna South Local Government Area. Jalingo Journal of Social and Management Sciences, 1(1).
Ogunlade, S. O., Afolabi, O. O., Akorede, S. A., & Odupe, S. O. (2021). Geospatial assessment of site suitability for solid waste dump sites in Ido Local Government Area, Oyo State, Nigeria. European Modern Studies Journal, 5(5), 95–107.
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4), 231–259.
Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., & Ranagalage, M. (2020). Sentinel-2 data for land cover/use mapping: A review. Remote Sensing, 12(14), 2291.
Ploton, P., Mortier, F., Réjou-Méchain, M., Barbier, N., Picard, N., Rossi, V., Dormann, C., Cornu, G., Viennois, G., Bayol, N., Lyapustin, A., Gourlet-Fleury, S., & Pélissier, R. (2020). Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nature Communications, 11, 4540.
Quesada-Ruiz, L. C., Rodriguez-Galiano, V., & Jordá-Borrell, R. (2018). Identifying the main physical and socioeconomic drivers of illegal landfills in the Canary Islands. Waste Management & Research, 36(11), 1049–1060. https://doi.org/10.1177/0734242X18804031
Quesada-Ruiz, L. C., Rodriguez-Galiano, V., & Jordá-Borrell, R. (2019). Characterization and mapping of illegal landfill potential occurrence in the Canary Islands. Waste Management, 85, 506–518. https://doi.org/10.1016/j.wasman.2019.01.015
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., & Dormann, C. F. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8), 913–929.
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the Third Earth Resources Technology Satellite-1 Symposium. NASA.
Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
Scharf, N., Ducry, B., Sy, B., Djim, A., & Lacroix, P. (2025). Integrating remote sensing, GIS, and citizen science to map illegal waste dumping susceptibility in Dakar, Senegal. Sustainability, 17(24), 11137. https://doi.org/10.3390/su172411137
Seror, N., & Portnov, B. A. (2018). Identifying areas under potential risk of illegal construction and demolition waste dumping using GIS tools. Waste Management, 75, 22–29.
Seror, N., & Portnov, B. A. (2020). Estimating the effectiveness of different environmental law enforcement policies on illegal C&D waste dumping in Israel. Waste Management, 102, 241–248.
Strobl, C., Boulesteix, A.-L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8, 25.
Sun, X., Yin, D., Qin, F., Yu, H., Lu, W., Yao, F., He, Q., Huang, X., Yan, Z., Wang, P., Deng, C., Liu, N., Yang, Y., Liang, W., Wang, R., Wang, C., Yokoya, N., Hänsch, R., & Fu, K. (2023). Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery. Nature Communications, 14, 1444. https://doi.org/10.1038/s41467-023-37136-1
Tasaki, T., Kawahata, T., Osako, M., Matsui, Y., Takagishi, S., Morita, A., & Akishima, S. (2007). A GIS-based zoning of illegal dumping potential for efficient surveillance. Waste Management, 27(2), 256–267.
Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(Suppl.), 234–240.
Torres, R. N., & Fraternali, P. (2021). Learning to identify illegal landfills through scene classification in aerial images. Remote Sensing, 13(22), 4520. https://doi.org/10.3390/rs13224520
United Nations Environment Programme. (2024). Global waste management outlook 2024: Beyond an age of waste – Turning rubbish into a resource. UNEP. https://doi.org/10.59117/20.500.11822/44939
Vaverková, M. D. (2019). Landfill impacts on the environment—Review. Geosciences, 9(10), 431.
Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.
Yusuf, T. (2018). Analysis of spatial distribution and perception of the risks of illegal solid waste dumpsites in Minna, Niger State, Nigeria [Unpublished master’s thesis]. Ahmadu Bello University.
Zhou, L., Luo, T., Du, M., Chen, Q., Liu, Y., Zhu, Y., He, C., Wang, S., & Yang, K. (2021). Machine learning comparison and parameter setting methods for the detection of dump sites for construction and demolition waste using the Google Earth Engine. Remote Sensing, 13(4), 787. https://doi.org/10.3390/rs13040787
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