Abstract
There has been a lot of interest in utilizing remote sensing images to anticipate landslides. We propose a novel framework for automatic landslide detection and landslide region localization from the input remote sensing image. The framework consists of pre-processing, dynamic segmentation, automatic feature extraction, classification, and localization. The pre-processing is the integrated step that performs atmospheric corrections, geometric corrections, and unnecessary region removal with denoising using 2D median filtering. The pre-processed image is then segmented using the dynamic segmentation approach to extract the Region of Interest (ROI). We propose lightweight Convolutional Neural Network (CNN) layers for automatic feature extraction and scaling using the ResNet50 model. The CNN layers are designed systematically for automatic feature extraction to improve accuracy and reduce computational requirements. The Long-Term Short Memory (LSTM), Artificial Neural Network (ANN), and Support Vector Machine (SVM) classifiers are designed to perform the landslide prediction. If landslides are forecast, the post-processing stages are intended to identify potential landslide locations. The experimental results show that the proposed CNN-LSTM model outperformed the existing solutions in terms of accuracy, F1 score, precision, and recall rates. The experimental outcomes reveal that the proposed model improves the overall prediction accuracy by 2% and reduces the computational complexity by 35% compared to state-of-the-art methods.
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References
Aksoy B, Ercanoglu M (2012) Landslide identification and classification by object-based image analysis and fuzzy logic: an example from the Azdavay region (Kastamonu, Turkey). Comput Geosci 38(1):87–98. https://doi.org/10.1016/j.cageo.2011.05.010
Azarafza M, Azarafza M, Akgün H, Atkinson PM, Derakhshani R (2021) Deep learning-based landslide susceptibility mapping. Sci Rep 11:24112. https://doi.org/10.1038/s41598-021-03585-1
Bakkouri I, Afdel K (2020) Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multimed Tools Appl 79:20483–20518. https://doi.org/10.1007/s11042-019-07988-1
Bakkouri I, Afdel K, Benois-Pineau J, Initiative GCFA (2022) BG-3DM2F: bidirectional gated 3D multi-scale feature fusion for Alzheimer’s disease diagnosis. Multimed Tools Appl 81:10743–10776. https://doi.org/10.1007/s11042-022-12242-2
Bui T-A, Lee P-J, Lum K-Y, Loh C, Tan K (2020) Deep learning for landslide recognition in satellite architecture. IEEE Access 8:143665–143678. https://doi.org/10.1109/access.2020.3014305
Chang H-H, Chan W-C (2021) Automatic Registration of Remote Sensing Images Based on Revised SIFT With Trilateral Computation and Homogeneity Enforcement. IEEE Trans Geosci Remote Sens:1–16. https://doi.org/10.1109/TGRS.2021.3052926
Das I, Stein A, Kerle N et al (2011) Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas. India Landslides 8:293–308. https://doi.org/10.1007/s10346-011-0257-9
Deep learning algorithms for medical image processing. Multimed Tools Appl 79, 9845 (2020). https://doi.org/10.1007/s11042-020-08673-4.
Ghorbanzadeh O, Shahabi H, Crivellari A, Homayouni S, Blaschke T, Ghamisi P (2022) Landslide detection using deep learning and object-based image analysis. Landslides 19:929–939. https://doi.org/10.1007/s10346-021-01843-x
Hwang S, Guevarra IF, Yu B (2009) Slope failure prediction using a decision tree: a case of engineered slopes in South Korea. Eng Geol 104(1–2):126–134. https://doi.org/10.1016/j.enggeo.2008.09.004
Khalifa NE, Loey M, Mirjalili S (2022) A comprehensive survey of recent trends in deep learning for digital images augmentation. Artif Intell Rev 55:2351–2377. https://doi.org/10.1007/s10462-021-10066-4
Khanlari GR, Heidari M, Momeni AA, Abdilor Y (2012) Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods. Eng Geol 131-132:11–18. https://doi.org/10.1016/j.enggeo.2011.12.006
Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113. https://doi.org/10.1007/s002540100310
Lee S, Hwang J, Park I (2013) Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea. CATENA 100:15–30. https://doi.org/10.1016/j.catena.2012.07.014
Li L, Cheng S, Wen Z (2021) Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model. J Mt Sci 18:2130–2142. https://doi.org/10.1007/s11629-020-6396-5
Lu W, Zhao L, Xu R (2021) Remote sensing image processing technology based on mobile augmented reality technology in surveying and mapping engineering. Soft Comput 27:423–433. https://doi.org/10.1007/s00500-021-05650-3
Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225–234. https://doi.org/10.1016/j.enggeo.2011.09.006
Meena SR, Soares LP, Grohmann CH, van Westen C, Bhuyan K, Singh RP, Floris M, Catani F (2022) Landslide detection in the Himalayas using machine learning algorithms and U-net. Landslides 19:1209–1229. https://doi.org/10.1007/s10346-022-01861-3
Mohan A, Kumar B, Dwivedi R (2021) Review on remote sensing methods for landslide detection using machine and deep learning. Trans Emerg Telecommun Technol 32. https://doi.org/10.1002/ett.3998
Nanare I, Bhoyar D, Balamwar S (2021) Remote sensing satellite image analysis for deforestation in Yavatmal District, Maharashtra, India. 684-688. https://doi.org/10.1109/ICSPC51351.2021.9451744
Nhu V-H, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, Jaafari A, Chen W, Miraki S, Dou J, Luu C, Górski K, Thai Pham B, Nguyen HD, Ahmad BB (2020) Shallow landslide susceptibility mapping: a comparison between logistic model tree, logistic regression, Naïve Bayes tree, artificial neural network, and support vector machine algorithms. Int J Environ Res Public Health 17(8):2749. https://doi.org/10.3390/ijerph17082749
Pain CD, Egan GF, Chen Z (2022) Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement. Eur J Nucl Med Mol Imaging 49:3098–3118. https://doi.org/10.1007/s00259-022-05746-4
Parsa M, Maghsoudi A, Shokouh B (2014) Landslide Susceptibility Mapping of Komroud Sub-basin Using Fuzzy Logic Approach 02
Pawłuszek K, Marczak S, Borkowski A, Tarolli P (2019) Multi-aspect analysis of object-oriented landslide detection based on an extended set of LiDAR-derived terrain features. ISPRS Int J Geo Inf 8(8):321. https://doi.org/10.3390/ijgi8080321
Perkins S (2012) Death toll from landslides vastly underestimated. http://www.nature.com/. http://www.emdat.be/database.
Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365. https://doi.org/10.1016/j.cageo.2012.08.023
Qin S, Guo X, Sun J, Qiao S, Zhang L, Yao J, Cheng Q, Zhang Y (2021) Landslide detection from open satellite imagery using distant domain transfer learning. Remote Sens 13:3383. https://doi.org/10.3390/rs13173383
Rasyid AR, Bhandary NP, Yatabe R (2016) Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenviron Disasters 3:19. https://doi.org/10.1186/s40677-016-0053-x
Roy J, Saha S (2019) Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India. Geoenviron Disasters 6:11. https://doi.org/10.1186/s40677-019-0126-8
Sajadi P, Sang Y-F, Gholamnia M, Bonafoni S (2022) Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan plateau region by five learning algorithms. Geoscience Letters 9. https://doi.org/10.1186/s40562-022-00218-x
San BT (2014) An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: the Candir catchment area (western Antalya, Turkey). Int J Appl Earth Obs Geoinf 26:399–412. https://doi.org/10.1016/j.jag.2013.09.010
Sarker IH (2021) Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput sci 2:420. https://doi.org/10.1007/s42979-021-00815-1
Sezer E, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38:8208–8219. https://doi.org/10.1016/j.eswa.2010.12.167
Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep 5:9899. https://doi.org/10.1038/srep09899
Shahabi H, Rahimzad M, Tavakkoli Piralilou S, Ghorbanzadeh O, Homayouni S, Blaschke T, Lim S, Ghamisi P (2021) Unsupervised deep learning for landslide detection from multispectral Sentinel-2 imagery. Remote Sens 13(22):4698. https://doi.org/10.3390/rs13224698
Srivastava P, Shukla A, Bansal A (2021) A comprehensive review on soil classification using deep learning and computer vision techniques. Multimed Tools Appl 80:14887–14914. https://doi.org/10.1007/s11042-021-10544-5
Tavakkoli Piralilou S, Shahabi H, Jarihani B, Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena S, Aryal J (2019) Landslide detection using multi-scale image segmentation and different machine learning models in the higher Himalayas. Remote Sens 11(21):2575. https://doi.org/10.3390/rs11212575
Thi Ngo PT, Panahi M, Khosravi K, Ghorbanzadeh O, Karimnejad N, Cerda A, Lee S (2020) Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci Front 12:505–519. https://doi.org/10.1016/j.gsf.2020.06.013
Tien Bui D (2012) Modeling of rainfall-induced landslide hazard for the Hoa Binh, province of Vietnam. Norwegian University of Life Sciences. Ph. D Thesis
Ullo SL, Langenkamp MS, Oikarinen TP, DelRosso MP, Sebastianelli A, Piccirillo FP, Sica S (2019) Landslide Geohazard Assessment with Convolutional Neural Networks Using Sentinel-2 Imagery Data. IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/igarss.2019.8898632
Wang Y, Wang X, Jian J (2019) Remote sensing landslide recognition based on convolutional neural network. Math Probl Eng 2019:1–12. https://doi.org/10.1155/2019/8389368
Wang H, Zhang L, Yin K, Luo H, Li J (2020) Landslide identification using machine learning. Geosci Front 12:351–364. https://doi.org/10.1016/j.gsf.2020.02.012
Wang Y, Wen H, Sun D, Li Y (2021) Quantitative assessment of landslide risk based on susceptibility mapping using random Forest and GeoDetector. Remote Sens 13(13):2625. https://doi.org/10.3390/rs13132625
Yalcin A, Reis S, Aydinoglu A, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA 85:274–287. https://doi.org/10.1016/j.catena.2011.01.014
Yang G, Liu J, Qu M, Wang S, Ye D, Zhong H (2021) FaasRS: Remote Sensing Image Processing System on Serverless Platform. 258–267. https://doi.org/10.1109/COMPSAC51774.2021.00044
Ye C, Wei R, Ge Y et al (2022) GIS-based spatial prediction of landslide using road factors and random forest for Sichuan-Tibet Highway. J Mt Sci 19:461–476. https://doi.org/10.1007/s11629-021-6848-6
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Varangaonkar, P., Rode, S.V. Lightweight deep learning model for automatic landslide prediction and localization. Multimed Tools Appl 82, 33245–33266 (2023). https://doi.org/10.1007/s11042-023-15049-x
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DOI: https://doi.org/10.1007/s11042-023-15049-x