Prediction of Landslides Using Extracted Environmental and Topographic Features from Multispectral Satellite Images
Received: 26 September 2025 | Revised: 21 November 2025 | Accepted: 1 December 2025 | Online: 9 February 2026
Corresponding author: Chethana Vasudevaiah
Abstract
Landslides are natural disasters that lead to the loss of lives and economic damage in a particular location. In India, particularly in hilly areas, landslides occur owing to factors such as rainfall, vegetation changes, changes in atmospheric conditions, and topographic features. The occurrence of landslides disrupts transportation, destroys infrastructure, and blocks roads for people near the location. Using satellite images, environmental and topographic features such as aspect, slope, curvature, contour, rainfall, soil moisture, atmospheric changes, and variations in vegetation index values can be extracted. These extracted features were used for landslide prediction, and the newly labeled dataset, comprising 1,738 landslides and 1,598 randomly generated non-landslide locations in India, was used for our experiment. Some previous studies considered either topographic or environmental features, whereas others used small datasets. In this work, both environmental and topographic features of all these locations were extracted from multispectral Landsat satellite images. A new dataset with features such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), slope, aspect, elevation, and precipitation was prepared. Using this dataset, Artificial Intelligence (AI) models were built utilizing various Machine Learning (ML) algorithms, such as the Light Gradient Boosting Machine (LightGBM), Random Forest (RF), CatBoost, and Multilayer Perceptron (MLP) neural network models, to predict landslides. These models were assessed using confusion matrices, accuracy, Receiver Operating Characteristic (ROC) curves, and comparative metrics. RF, LightGBM, and CatBoost achieved the highest accuracy of 96%, and the MLP achieved an accuracy of 93%. The ROC–Area Under the Curve (ROC–AUC) score was 0.86 for the LightGBM model, which was the highest compared to the other three models. This work used multispectral satellite images and advanced ML models for reliable landslide predictions in India.
Keywords:
landslide, Machine Learning (ML), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), CatBoost, Multilayer Perceptron (MLP) neural network, topographic featuresDownloads
References
A. Sharma et al., "Artificial Intelligence Techniques for Landslides Prediction Using Satellite Imagery," IEEE Access, vol. 12, pp. 117318–117334, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3446037
O. Ghorbanzadeh et al., "The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection From Multisource Satellite Imagery," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 9927–9942, 2022. DOI: https://doi.org/10.1109/JSTARS.2022.3220845
M. R. Sumalatha, S. K. Yasvinippriyaa, V. Vetrivendan, N. Ravikumar, and S. Shivalingam, "Landslide Susceptibility Mapping for South India with Dense Neural Network using Rainfall and Satellite Data-Driven Novel Dataset," in 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, Bengaluru, India, 2025, pp. 1591–1596. DOI: https://doi.org/10.1109/IDCIOT64235.2025.10914901
V. Acharya, A. Ghosh, I. Kang, T. Munasinghe, and K. C. Binita, "Landslide Likelihood Prediction using Machine Learning Algorithms," in 2022 IEEE International Conference on Big Data, Osaka, Japan, 2022, pp. 5395–5403. DOI: https://doi.org/10.1109/BigData55660.2022.10020433
P. Varangaonkar and S. V. Rode, "Research on Efficient Landslide Prediction Approaches using Machine Learning Techniques," in 2022 5th International Conference on Advances in Science and Technology, Mumbai, India, 2022, pp. 64–68. DOI: https://doi.org/10.1109/ICAST55766.2022.10039507
B. Sarada, P. Varsha, N. Sindhu, and M. Uma, "An Intutive Apporach with CNN Model for Landslide Prediction using Satellite Imagery," in 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics, Ballari, India, 2025, pp. 1–8. DOI: https://doi.org/10.1109/ICDCECE65353.2025.11035499
G. A, M. D. C, R. P, Y. G, S. K, and I. K, "A Comprehensive Hybrid CNN-LSTM Deep Learning Model for Accurate Landslide Prediction," in 2024 International BIT Conference, Dhanbad, India, 2024, pp. 1–4. DOI: https://doi.org/10.1109/BITCON63716.2024.10985324
S. Sutawane and S. Mitra, "Landslide Susceptibility Assessment Through Fuzzy Inference Analysis on Remotely Sensed Satellite Image: A Case Study on Irshalwadi, Maharashtra," in 2024 Second International Conference on Advances in Information Technology, Chikkamagaluru, Karnataka, India, 2024, vol. 1, pp. 1–8. DOI: https://doi.org/10.1109/ICAIT61638.2024.10690686
Y. J. Kumar et al., "Developing Sustainable Solutions Using Technological Approaches for Disaster Management and Energy Access in Mountainous Ranges: A Case Study of Sera Village, India," in 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, Bhilai, India, 2024, pp. 1–6. DOI: https://doi.org/10.1109/ICAECT60202.2024.10469266
S. L. Ullo et al., "Landslide Geohazard Assessment with Convolutional Neural Networks Using Sentinel-2 Imagery Data," in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 9646–9649. DOI: https://doi.org/10.1109/IGARSS.2019.8898632
G. Kour and N. Rathour, "Landslide Detection and Mapping using Remote Sensing U-Net Model In Leh-Ladakh Region," in 2024 7th International Conference on Contemporary Computing and Informatics, Greater Noida, India, 2024, pp. 1044–1049. DOI: https://doi.org/10.1109/IC3I61595.2024.10829154
S. P. S, K. P, S. RM, and K. P, "Hybrid Slide Sense: A Holistic Approach to Predicting Landslide Susceptibility via Deep Learning and Machine Learning," in 2024 International Conference on Emerging Technologies and Innovation for Sustainability, Greater Noida, India, 2024, pp. 300–305. DOI: https://doi.org/10.1109/EmergIN63207.2024.10961733
K. V. Vishnu Vardhan, V. H. S. S. Kaushik, K. L. Sailaja, and P. R. Kumar, "Detection and Prediction of Landslide Vulnerability through Case Study using DInSAR Technique and U-net Model," in 2023 5th International Conference on Smart Systems and Inventive Technology, Tirunelveli, India, 2023, pp. 176–182. DOI: https://doi.org/10.1109/ICSSIT55814.2023.10061077
G. B. Raj, M. K. Patan, A. K. Gupta, C.Lakshmi, G. D, and A. S. Yadav, "A Novel Methodology to Predict Flood and Landslide Disasters using Learning Assisted Artificial Intelligence (AI) Logic," in 2025 International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, 2025, pp. 1860–1867. DOI: https://doi.org/10.1109/ICEARS64219.2025.10940940
H. Yan, A. Khan, A. Jamil, B. Abdeldjalil, T. Saidani, and N. Y. Rebouh, "Deep Learning-Based Spatial Prediction of Landslide Risk in Coastal Areas Using GIS and Multicriteria Decision Making: A DeepLabV3+ Approach," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 15222–15235, 2025. DOI: https://doi.org/10.1109/JSTARS.2025.3578822
Y. He et al., "A Heterogeneous Ensemble Learning Method Combining Spectral, Terrain, and Texture Features for Landslide Mapping," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 3746–3765, 2025. DOI: https://doi.org/10.1109/JSTARS.2025.3525633
D. Anil and S. H. Manjula, "High-Precision Landslide Susceptibility Mapping Using CNN-LSTM-Attention Models," Engineering, Technology & Applied Science Research, vol. 15, no. 4, pp. 25486–25491, Aug. 2025. DOI: https://doi.org/10.48084/etasr.11505
D. Kirschbaum, T. Stanley, and Y. Zhou, "Spatial and temporal analysis of a global landslide catalog," Geomorphology, vol. 249, pp. 4–15, Nov. 2015. DOI: https://doi.org/10.1016/j.geomorph.2015.03.016
C. Funk et al., "The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes," Scientific Data, vol. 2, no. 1, Dec. 2015, Art. no. 150066. DOI: https://doi.org/10.1038/sdata.2015.66
Downloads
How to Cite
License
Copyright (c) 2025 V. Chethana, S. Rashmi, J. Rajeshwari
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
