References

gesj

GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY

2071-93882542-1565

Russian Geographical Society

10.24057/2071-9388-2026-4152

gesj-4625

Research Article

RESEARCH PAPER

Статьи

Integration of Ai in Gis for Identifying and Locating Illegal Waste Deposits in Algerian Municipalities

Chergui

Soumia

Soumia Chergui - Earth Sciences, Geography, and Land Planning Faculty.

BP 32 Bab Ezzouar, Algiers 16111

cherguibenarieb@gmail.com

Farhi

Abdallah

Abdallah Farhi - Architecture and Urban Planning Department.

BP 145, Biskra 07000

Saib

Bouthina

Bouthina Saib - Computer Science Department, Mathematics and Computer Science Faculty.

Batna 05000

University of Science and Technology Houari Boumediene (USTHB)AlgeriaUniversity of Mohamed Khider BiskraAlgeriaMustapha Ben Boulaid UniversityAlgeria

2026

31032026

191139151

2026

Chergui S., Farhi A., Saib B.

Данная работа распространяется под лицензией Creative Commons Attribution 4.0.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://ges.rgo.ru/jour/article/view/4625

Like many municipalities around the world, Algerian municipalities are faced with the challenge of managing the collection of illegal waste deposits located across their territories. These deposits may occur both at authorized household waste collection points and at unauthorized locations. The management of these deposits presents a challenge, as their handling is incompatible with refuse trucks typically used for household waste collection or because they must be covered by collection services other than those provided by the municipality. It is therefore essential to identify and locate these deposits to ensure appropriate handling.

This article aims to address this issue through an innovative solution that integrates artificial intelligence (AI) into geographic information systems (GIS). The method is based on transfer learning combined with MobileNetV2 to generate a classification model for images of illegal waste deposits at authorized and unauthorized points. This model is integrated into a plugin created with QGIS software to perform image classification, enabling the location and identification of these deposits. The model achieved an accuracy of 98% during training, and its application to images from Biskra municipality illustrates its potential effectiveness. Beyond this case study, the approach offers a scalable and adaptable solution for improving illegal waste deposit management practices in diverse municipal contexts.

Illegal waste deposits.AIGISTransfer LearningMobileNetV2Image classification Model

References1

Abdulah S., Alamri F., Nag P., Sun Y., Ltaief H., Keyes D. E., and Genton M. G. (2022). The second competition on spatial statistics for large datasets. arXiv preprint arXiv:2211.03119.

Abdulai H., Hussein R., Bevilacqua E., and Storrings M. (2015). GIS based mapping and analysis of municipal solid waste collection system in Wa, Ghana. Journal of Geographic Information System, 7(2), 85–94. DOI: 10.4236/JGIS.2015.72008.

Adipah S. and Kwame O. N. (2019). A novel introduction of municipal solid waste management. Journal of Environmental Science and Public Health, 3(2), 147–157.

Ahmad M. (2023). AI-Enabled Spatial Intelligence: Revolutionizing Data Management and Decision Making in Geographic Information Systems. In AI and Its Convergence With Communication Technologies, pp. 137–166. IGI Global.

Ahmed Z. Y. (2024). Artificial Intelligence Geographic Information Systems-AI GIS. International Journal of Advanced Engineering and Business Sciences, 5(1).

Agence Nationale des Déchets (2021). Rapport sur l’État de la Gestion des Déchets en Algérie. Agence Nationale des Déchets, 150p.

Asefa E. M., Barasa K. B., and Mengistu D. A. (2022). Application of geographic information system in solid waste management. In Geographic Information Systems and Applications in Coastal Studies. IntechOpen.

Awaluddin M., Hidayat R., and Pratama A. (2023). Enhancing image classification through geometric transformations: A comparative study. Journal of Computer Vision and Applications, 18(2), 45–62.

Awasare S. and Sutar A. (2015). Solid waste management and GIS. International Journal of Research in Environmental Science and Technology, 5(1), 22–28.

Bodea C., Ozunu A., Baciu N., and Măcicăşan V. (2014). Using GIS in waste management – some conceptual considerations. ECOTERRA – Journal of Environmental Research and Protection, 11(1), 61–65.

Bošković G., Cvetanović A. M., Jovičić N., Jovanović A., Jovičić M., and Milojević S. (2024). Digital technologies for advancing future municipal solid waste collection services. In Digital Transformation and Sustainable Development in Cities and Organizations, pp. 167–192. IGI Global. DOI: 10.4018/979-8-3693-3567-3.ch008.

Brus J., Vrkoč J., and Kubásek M. (2016). Design of decision support tools for the quality assessment of illegal dumping notifications based on crowd-sourced data.

Cai T. and Ge J. (2023, July). Design and application of artificial intelligence GIS algorithm based on deep learning technology. In 2023 International Conference on Data Science and Network Security (ICDSNS), pp. 1–5. IEEE. DOI: 10.1109/icdsns58469.2023.10245905.

Caputo A. C. and Pelagagge P. M. (2000). Integrated geographical information system (GIS) for urban solid waste management. WIT Transactions on Ecology and the Environment, 39, 159–169. DOI: 10.2495/URS000181.

Choi Y. (2023). GeoAI: Integration of artificial intelligence, machine learning, and deep learning with GIS. Applied Sciences, 13(6), 3895.

Del Carmen-Niño V., Herrera-Navarrete R., Juárez-López A. L., Sampedro-Rosas M. L., and Reyes-Umaña M. (2023). Municipal solid waste collection: Challenges, strategies and perspectives in the optimization of a municipal route in a southern Mexican town. Sustainability, 15(2), 1083.

Devesa M. R. and Brust A. V. (2021). Mapping illegal waste dumping sites with neural-network classification of satellite imagery. arXiv preprint arXiv:2110.08599.

Direction de la Programmation et du Suivi Budgétaire de Biskra (DPSB). (2024). Monographie de la wilaya de Biskra. Biskra, Algeria.

Dregulo A. M. and Khodachek A. M. (2022). Waste management reform in regions of the Russian Federation: Implementation issues on the way to sustainable development. Geography, Environment, Sustainability, 15(1), 6–13. DOI: 10.24057/2071-9388-2021-078.

Fatunmibi O. and Gbopa A. O. (2021). Dump sites location and its health implications within the Polytechnic, Ibadan using geographical information system approach. International Journal of Research, 8(5), 467–478. DOI: 10.52403/IJRR.20210557.

Ghosh M. and Thirugnanam A. (2021). Introduction to artificial intelligence. In Artificial Intelligence for Information Management: A Healthcare Perspective, pp. 23–44.

Gondhalekar G., Bathala N. K., Merugu N. B., Joshi N., and Kumari P. L. (2024). Enhancing image classification performance through transfer learning and adaptive augmentation: A MobileNetV2 approach.

Hernandez-Garcia A. (2020). Data augmentation and image understanding. arXiv preprint arXiv:2012.14185.

Howard A., Zhmoginov A., Chen L. C., Sandler M., and Zhu M. (2018, June). Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520.

Hua T. M., Nguyen T. K., Van Dinh Thi H., and Thi N. A. N. (2016, December). Towards a decision support system for municipal waste collection by integrating geographical information system map, smart devices and agent-based model. In Proceedings of the 7th Symposium on Information and Communication Technology, pp. 139–146.

Iacoboaea C. and Aldea M. (2011). The assessment of GIS use in waste management. Journal of Applied Engineering Sciences, 1(4), 33–38.

Inamdar S., More P., Chavan M., Pawar A., Patil S., and Raje U. (2023). Smart surveillance system for illegal garbage dumping. International Journal of Advanced Research in Science, Communication and Technology, pp. 293–297. DOI: 10.48175/ijarsct-8599.

Jakeni Y., Maphanga T., Madonsela B. S., and Malakane K. C. (2024). Identification of illegal dumping and community views in informal settlements, Cape Town: South Africa. Sustainability, 16(4), 1429. DOI: 10.3390/su16041429.

Jimoh R., Moradeyo A., Chuma V., Olubukola O., and Yusuf A. (2019). GIS based appraisal of waste disposal for environmental assessment and management in Mainland area of Lagos state, NG. International Journal of Environment and Geoinformatics, 6(1), 76–82.

Karimi N. and Ng K. T. W. (2022). Mapping and prioritizing potential illegal dump sites using geographic information system network analysis and multiple remote sensing indices. Earth, 3(4), 1123–1137.

Khalil R., Benyettou A., and Toumi A. (2023). Zoom-based augmentation for small object detection in urban environments. International Journal of Artificial Intelligence and Smart Systems, 11(1), 77–89.

Khan M. S. A., Husen A., Nisar S., Ahmed H., Muhammad S. S., and Aftab S. (2024). Offloading the computational complexity of transfer learning with generic features. PeerJ Computer Science, 10, e1938.

Kim Y. and Cho J. (2022). AIDM-Strat: augmented illegal dumping monitoring strategy through deep neural network-based spatial separation attention of garbage. Sensors, 22(22), 8819.

Krsmanović M., Šušnjar S., Golijanin J., and Valjarević A. (2022). GIS based vulnerability assessment of illegal waste disposal – case study East Sarajevo. DOI: 10.7251/afts.2022.1427.063k.

Kumar S., Patel R., and Singh A. (2025). Affine transformations for robust image classification: A multi-domain analysis. Pattern Recognition and Machine Intelligence, 39(1), 112–130.

Marange F., Muteveri M., Chipfunde F., and Mapira J. (2023). Challenges confronting local authorities in solid waste management: The case of Dangamvura residential area, Mutare, Zimbabwe. European Journal of Social Sciences Studies, 8(5). DOI: 10.46827/ejsss.v8i5.1477.

Mohanta S. K., Mohapatra A. G., Mohanty A., and Nayak S. (2024). Deep learning is a state-of-the-art approach to artificial intelligence. In Deep Learning Concepts in Operations Research, pp. 27–43. Auerbach Publications.

Muñoz-Aseguinolaza D., García-González A., and Pérez J. (2023). Rotation-invariant convolutional networks for aerial waste detection. Remote Sensing Letters, 14(5), 389–405.

Nandi G. C. (2023). Deep learning. pp. 123–158. DOI: 10.1002/9781394173167.ch4.

Nanni L., Lumini A., and Brahnam S. (2021). Image translation techniques for deep learning: A survey and experimental evaluation. Expert Systems with Applications, 165, 113891.

Narayanan N. and Arjun K. P. (2024). Introduction to deep learning. pp. 32–60. DOI: 10.1201/9781003504900-2.

Office National des Statistiques (ONS). (2008). General Census of Population and Housing. Algiers, Algeria.

Padubidri C., Kamilaris A., and Karatsiolis S. (2022, March). Accurate detection of illegal dumping sites using high resolution aerial photography and deep learning. In 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops), pp. 451–456. IEEE.

Panesar A. (2020). What is artificial intelligence? pp. 1–18. DOI: 10.1007/978-1-4842-6537-6_1.

Paunović S. and Krstić F. (2014). GIS application in the spatial analysis of illegal landfills in big cities: A case study of Belgrade. Bulletin of the Serbian Geographical Society, 94(3), 41–54.

Pathak N., Biswal G., Goushal M., Mistry V., Shah P., Li F., and Gao J. (2024). Smart city community watch — camera-based community watch for traffic and illegal dumping. Smart Cities, 7(4), 2232–2257.

Puigcerver J., Riquelme C., Mustafa B., Renggli C., Pinto A. S., Gelly S., and Houlsby N. (2020). Scalable transfer learning with expert models. arXiv preprint arXiv:2009.13239.

Qi Y., Zhang L., and Chen H. (2021). Rotation-based data augmentation for improved generalization in CNNs. IEEE Transactions on Image Processing, 30, 4567–4579.

Raj A. (2024). Artificial intelligence. International Journal for Science Technology and Engineering, 12(11), 646–655. DOI: 10.22214/ijraset.2024.64695.

Rao K. R., Sreekeshava K. S., Dharek M. S., and Sunagar P. (2020). Issues on planning of solid waste management scheme through evaluation in integrated data information system. In AIP Conference Proceedings, Vol. 2204, No. 1. AIP Publishing. DOI: 10.1063/1.5141548.

Sakshi, Neeti K., and Singh R. (2023). Diverse applications of remote sensing and geographic information systems in implementing integrated solid waste management: A short review. Engineering Proceedings, 56(1), 109. DOI: 10.3390/asec2023-15340.

Sandler M., Howard A., Zhu M., Zhmoginov A., and Chen L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520.

Saxena P., Saxena V., Pandey A., Flato U., and Shukla K. (2023). Multiple aspects of artificial intelligence. Book Saga Publications.

Shafik W. (2024). Deep learning impacts in the field of artificial intelligence. In Deep Learning Concepts in Operations Research, pp. 9–26. Auerbach Publications.

Shahab S. and Anjum M. (2022). Solid waste management scenario in India and illegal dump detection using deep learning: An AI approach towards sustainable waste management. Sustainability, 14(23), 15896.

Shrivastava A., Pandey A., Singh N., Srivastava S., Srivastava M., and Srivastava A. (2024). Artificial intelligence (AI): Evolution, methodologies, and applications. International Journal for Research in Applied Science and Engineering Technology, 12(4), 5501–5505.

Syafrudin S., Ramadan B. S., Budihardjo M. A., Munawir M., Khair H., Rosmalina R. T., and Ardiansyah S. Y. (2023). Analysis of factors influencing illegal waste dumping generation using GIS spatial regression methods. Sustainability, 15(3), 1926.

Talaei Khoei T., Ould Slimane H., and Kaabouch N. (2023). Deep learning: Systematic review, models, challenges, and research directions. Neural Computing and Applications, 35(31), 23103–23124.

Thompson A. F., Afolayan A. H., and Ibidunmoye E. O. (2013, July). Application of geographic information system to solid waste management. In 2013 Pan African International Conference on Information Science, Computing and Telecommunications (PACT), pp. 206–211. IEEE. DOI: 10.1109/SCAT.2013.7055110.

Torres R. N. and Fraternali P. (2021). Learning to identify illegal landfills through scene classification in aerial images. Remote Sensing, 13(22), 4520.

United Nations Development Programme (2023). Harnessing the role of private sector in waste management through South-South and Triangular Cooperation for inclusive urbanization, pp. 10–10. DOI: 10.18356/9789213585207c004.

Vozenilek V. (2009). Artificial intelligence and GIS: Mutual meeting and passing. In 2009 International Conference on Intelligent Networking and Collaborative Systems, pp. 279–284. IEEE.

Wang T., Li Y., and Zhou M. (2025). Scaling strategies in convolutional neural networks: Impacts on classification accuracy. Journal of Machine Learning Research, 26(4), 215–230.

Yang X., Wang Y., Lu K., Wu Y., and Zhao D. (2024). Artificial neural network modelling in GIS spatial analysis. Academic Journal of Computing and Information Science, 7(6), 32–37.

Yu D., Yoon J., and Lee Y. (2024, July). Detection and register of illegal garbage dumping action using the consecutive processing and Embedded-NAS. In 2024 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8. IEEE. DOI: 10.1109/avss61716.2024.10672606.

Zainun N. Y., Rahman I. A., and Rothman R. A. (2016, November). Mapping of construction waste illegal dumping using geographical information system (GIS). In IOP Conference Series: Materials Science and Engineering, Vol. 160, No. 1, p. 012049. IOP Publishing. DOI: 10.1088/1757-899X/160/1/012049.

Zeng W. (2024). Image data augmentation techniques based on deep learning: A survey. Mathematical Biosciences and Engineering, 21(6), 6190–6224.

Zorić M., Štula M., Markić I., and Braović M. (2024, September). Transfer learning in building neural network model case study. In 2024 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–6. IEEE.

The authors declare that there are no conflicts of interest present.