This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the OPEN ACCESS Remote Sens. 2015, 7 4319 simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.

The quality of digital elevation models (DEMs), as well as their spatial resolution, are important issues in geomorphic studies. However, their influence on landslide susceptibility mapping (LSM) remains poorly constrained. This work determined the scale dependency of DEM-derived geomorphometric factors in LSM using a 5 m LiDAR DEM, LiDAR resampled 30 m DEM, and a 30 m ASTER DEM. To verify the validity of our approach, we first compiled an inventory map comprising of 267 landslides for Sihjhong watershed, Taiwan, from 2004 to 2014. Twelve landslide causative factors were then generated from the DEMs and ancillary data. Afterward, popular statistical and machine learning techniques, namely, logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented to produce the LSM. The accuracies of models were evaluated by overall accuracy, kappa index and the receiver operating characteristic curve indicators. The highest accuracy was attained from the resampled 30 m LiDAR DEM derivatives, indicating a fine-resolution topographic data does not necessarily achieve the best performance. Additionally, RF attained superior performance between the three presented models. Our findings could contribute to opt for an appropriate DEM resolution for mapping landslide hazard in vulnerable areas.

No abstract

No abstract

No abstract

No abstract

This study presents a novel computer vision based approach to automatically identify rutting appeared on asphalt pavement of road. The developed model is established base on a hybridization of image processing techniques and an advanced machine learning model with support of a metaheuristic optimization engine. Gabor filter and discrete cosine transform are employed to implement context computation for image data, accordingly generate initially extracted features of rutting and non-rutting. Least Squares Support Vector Classification (LSSVC) is then used to learn categorization of rutting and non-rutting based on the extracted features. The final LSSVC prediction model is constructed via a loop of optimization process which is controlled by a novel metaheuristic optimization algorithm, called forensic-based investigation (FBI), to attain optimal model's configuration with ultimate prediction accuracy. This study further utilized a dynamic feature selection (FS) method to integrate in the searching loop to appropriately remove redundant features that provide inconsistent information leading to the compromising of model performance. A dataset of 2000 image samples has been collected during field trip of pavement survey in Da Nang city to form and verify the newly developed model. The statistical results of 20 run times using k-fold cross validation method have demonstrated the hybrid model of FBI-LSSVC-FS to achieve the most desired rutting detection performance with classification accuracy rate, precision, recall, and F1 score of 98.9%, 0.994, 0.984 and 0.989, respectively. Hence, this paper contributes to the core body of knowledge a novel AI-based prediction model to assist transportation agencies in the task of periodic asphalt pavement survey.

No abstract