VETO AI as a Spatial Data-Based Toll Road Vegetation Maintenance Decision Support System Using Clustering and Regression Approaches
Abstract
Vegetation maintenance along toll road corridors is generally conducted using routine schedules without considering spatial and environmental variability, which can lead to inefficient resource allocation. This study proposes VETO AI, a spatial data-based decision support system that integrates NDVI, geospatial data, and artificial intelligence to support adaptive vegetation maintenance. The framework combines clustering for toll road segmentation and regression for estimating maintenance needs based on vegetation and environmental variables. The model was evaluated using 500 observations and demonstrates strong predictive capability with acceptable error levels. Visual validation indicates that the model captures the relationship between variables without significant bias. The integration of analytical outputs enables the classification of maintenance priorities into actionable levels, supporting a transition from routine-based to condition-based maintenance. Although implemented at a prototype stage, the system is designed to be scalable for broader network applications. This study contributes a practical and data-driven approach to improving efficiency in toll road vegetation management.
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Astra Digital, “Digital transformation challenges in toll road operations: Efficiency and maintenance optimization,” Jakarta, 2024. https://astradigital.id/astranauts.
Badan Pengatur Jalan Tol (BPJT), “Peran vegetasi dalam keselamatan dan kinerja infrastruktur jalan tol,” Jakarta, 2023. https://bpjt.pu.go.id/peluncuran-buku-tahunan-bpjt-tahun-2022-peningkatan-kualitas-jalan-tol-melalui-inovasi-teknologi-operasi-dan-pemeliharaan/.
A. Subash, S. A. Ganeshan, V. Nandakumar, and N. Gowthaman, “An AI-Powered Crop Health Monitoring System Using NDVI and Predictive Soil-Climate Modeling,” in 2026 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), 2026, pp. 1–6. doi: 10.1109/IITCEE67948.2026.11393979.
S. A. Anees et al., “Spatiotemporal dynamics of vegetation cover: integrative machine learning analysis of multispectral imagery and environmental predictors,” Earth Sci. Inform., vol. 18, no. 1, p. 152, 2025, doi: 10.1007/s12145-024-01673-0.
N. H. Alayaid, M. O. Albaidhani, A. M. Alghamdi, R. A. Sindi, A. S. Alharbi, and S. Ghanem, “Deep learning-based vegetation monitoring using the NDVI index,” in 2025 International Conference on Innovation in Artificial Intelligence and Internet of Things (AIIT), 2025, pp. 1–8. doi: 10.1109/AIIT63112.2025.11082800.
V. A. Nan, G. Badea, A. C. Badea, and A. P. Grădinaru, “A Systematic Review of AI-Based Classifications Used in Agricultural Monitoring in the Context of Achieving the Sustainable Development Goals,” Oct. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/su17198526.
H. Zhang, L. Ye, D. Xie, and A. Wang, “Advances in Resources Research The application status, challenges, and prospects of artificial intelligence technology in vegetation ecological protection,” vol. 5, no. 4, pp. 1848–1867, 2025, doi: 10.50908/arr.5.4_1848.
W. Ma and L. Zhang, “Advances in Resources Research Data-driven assessment of wetland vegetation health: A new perspective on artificial intelligence,” vol. 5, no. 1, pp. 302–328, 2025, doi: 10.50908/arr.5.1_302.
D. Abdullah, “Smart Forest Monitoring Using Remote Sensing and AI for Early Detection of Climate-Induced Forest Stress,” National Journal of Forest Sustainability and Climate Change, vol. 2, no. 1, pp. 62–68, doi: 10.17051/NJFSCC/02.01.09.
A. Gholinia and P. Abbaszadeh, “Agricultural Drought Monitoring: A Comparative Review of Conventional and Satellite-Based Indices,” Sep. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/atmos15091129.
Y. Wang, S. Jin, and G. Dardanelli, “Vegetation Classification and Evaluation of Yancheng Coastal Wetlands Based on Random Forest Algorithm from Sentinel-2 Images,” Remote Sens. (Basel)., vol. 16, no. 7, Apr. 2024, doi: 10.3390/rs16071124.
M. A. Jabed and M. A. Azmi Murad, “Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability,” Dec. 30, 2024, Elsevier Ltd. doi: 10.1016/j.heliyon.2024.e40836.
F. Shahzad et al., “Comparing machine learning algorithms to predict vegetation fire detections in Pakistan,” Fire Ecology, vol. 20, no. 1, Dec. 2024, doi: 10.1186/s42408-024-00289-5.
D. Kerapatan Vegetasi Dan Suhu Permukaan Lahan Kabupaten Banyuwangi, A. Khoirunnisa, I. Gede Astra Wesnawa, I. Putu Ananda Citra, and M. Dwipayana, “Menggunakan Citra Landsat 8-Oli (Studi Kasus Tahun 2023),” Journal Of Social Science Research, vol. 4, pp. 1446–1460, 2024. doi: https://doi.org/10.31004/innovative.v4i5.14882
R. Vidican et al., “Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review,” Dec. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/agronomy13123040.
Y. Chen, Y. Wang, F. Zhang, Y. Dong, Z. Song, and G. Liu, “Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities,” Sep. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/min13091153.
T. Furusawa, T. Koera, R. Siburian, A. Wicaksono, K. Matsudaira, and Y. Ishioka, “Time-series analysis of satellite imagery for detecting vegetation cover changes in Indonesia,” Sci. Rep., vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-35330-1.
D. K. Raj and T. Gopikrishnan, “Machine learning models for predicting vegetation conditions in Mahanadi River basin,” Environ. Monit. Assess., vol. 195, no. 12, p. 1401, 2023, doi: 10.1007/s10661-023-12006-x.
M. Naghdyzadegan Jahromi et al., “Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices,” European Journal of Agronomy, vol. 146, p. 126820, 2023, doi: https://doi.org/10.1016/j.eja.2023.126820.
K. A. García-Pardo, D. Moreno-Rangel, S. Domínguez-Amarillo, and J. R. García-Chávez, “Remote sensing for the assessment of ecosystem services provided by urban vegetation: A review of the methods applied,” Aug. 01, 2022, Elsevier GmbH. doi: 10.1016/j.ufug.2022.127636.
B. F. R. Davies et al., “Multi- and hyperspectral classification of soft-bottom intertidal vegetation using a spectral library for coastal biodiversity remote sensing,” Remote Sens. Environ., vol. 290, May 2023, doi: 10.1016/j.rse.2023.113554.
R. Neyns and F. Canters, “Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review,” Feb. 01, 2022, MDPI. doi: 10.3390/rs14041031.
H. T. Pham, J. Awange, M. Kuhn, B. Van Nguyen, and L. K. Bui, “Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices,” Sensors, vol. 22, no. 3, Feb. 2022, doi: 10.3390/s22030719.
R. Beigaitė et al., “Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes,” Glob. Chang. Biol., vol. 28, no. 11, pp. 3557–3579, Jun. 2022, doi: 10.1111/gcb.16110.
Z. Ji, Y. Pan, X. Zhu, D. Zhang, and J. Wang, “A generalized model to predict large-scale crop yields integrating satellite-based vegetation index time series and phenology metrics,” Ecol. Indic., vol. 137, Apr. 2022, doi: 10.1016/j.ecolind.2022.108759.
M. Dastigerdi, M. Nadi, M. R. Sarjaz, and K. Kiapasha, “زمانی سری از استفاده با گیاهی پوشش روند تحلیل / ... دستی مرتضی گردی و همکاران 135 Vegetation trend analysis using NDVI time series of Modis satellite in the northeast of Iran”, doi: 10.22069/jwsc.2022.20208.3554
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