2 A Supervised Object-Based Detectionof Landslides and Man-Made Slopes Using Airborne Laser Scanning Data

In recent years, airborne-derived products from light detection and ranging (LiDAR) measurements, such as high-resolution digital elevation models (DEMs), slope, curvature, shaded relief, and maps of landslides obtained from beneath dense vegetation, are becoming increasingly important for producing a detailed landslide inventory map (Eeckhaut et al. 2007). LiDAR applications include the construction of DEMs and shaded relief maps, detection of historical landslides under forested area, creation of topographic contours, tracking of multitemporal digital terrain model (DTM) of landslides, hydrological modeling, landform and/or soil classification, and understanding fine-scale landslide patterns (McKean and Roering 2004). The rapidness of LiDAR technology in landslide mapping of terrains through quantitative or visual analysis provides several advantages. Landslide inventory maps provide baseline information of landslide types, location, distribution, and boundaries in landslide-prone areas. In addition, landslide inventory provides information on displacement and slope measurements that affect a failure (Galli et al. 2008). Moreover, landslide inventories are significantly useful for various purposes, such as recording of landslide magnitude, implementing the initial stage for landslide susceptibility, and hazard and risk assessments. Object-based image analysis (OBIA) is a well-known technique resulting from the recent advances in computer vision and machine intelligence, with the main purpose of automatically extracting both man-made and natural objects from remote sensing images (Akcay and Aksoy 2008). OBIA, in which the information content of an object is used to classify a landscape, is a step toward replicating human interpretation process (Navulur 2006). In addition, OBIA can detect landslides accurately and meaningfully by integrating contextual information to image analysis (Martha 2011), which reduces the time and cost for producing a decent landslide inventory map, especially in large areas. Several techniques have been proposed for landslide mapping, such as field observation and aerial photointerpretation; however, these techniques have some limitations, such as lack of proper resolution for aerial photographs required for the mapping of small-scale landslides caused by morphologic feature obscuration by thick vegetation cover, time-consuming, and difficulties in field mapping (Gorum et al. 2011). Remote sensing data and methods have been proven efficient in landslide mapping because of their wide area coverage, relatively cheap cost, and remarkably high-resolution data for landslide mapping, in which even minor landslides can be mapped easily. Landslide inventory maps resulting from the application of OBIA techniques can be easily converted to GIS data, which is considered as an initial stage for a more advance analysis, such as susceptibility, and hazard and risk analysis. Landslides can be triggered by various factors. These factors can be man-made (such as mineral mining, road cutting, and urbanization) or natural (e.g., extreme rainfall events and earthquakes (Zêzere et al. 1999). However, in tropical areas, a rapid and accurate method for landslide mapping is required because of the rapid growth of vegetation that covers the land surface characteristics in those areas. Furthermore, several cut slopes are generally created to mitigate the risk of land failure for areas that have high probability to fail because of the frequent occurrence of landslides. These requirements create new challenges for landslide identification and mapping in these areas. Thus, new methods should be developed for automatic landslide detection to produce high-quality landslide inventory map. B. Pradhan (&) A. Alsaleh Department of Civil Engineering, University Putra Malaysia, Serdang, Malaysia e-mail: biswajeet24@gmail.com