Meter-level resolution Digital Elevation Models (DEMs) have become widely available since airborne LiDAR systems are popular. Although displaying high-resolution DEMs with stereo viewing screen allows us to realistically experience the topographic changes, it is difficult to overlay or inter-analyze with other thematic maps. This study devotes to the theory of deriving topographic parameters from high-resolution DEMs and the visualization of the parameters as raster maps, such as hillshade, slope, aspect, openness and multi-directional hillshade and so on. These parameters enable users to observe topographic features with two-dimensional maps. The characteristics and advantages of each topographic parameter will be analyzed through a visual investigation of the maps. The tint scheme and color continuity of adjacent frames will be discussed as well. The combination of two topographic parameters into a map will also be assessed to display topographic information, which meets the requirements of some particular applications. This study is expected to be useful for a variety of applications of high-resolution DEMs.

This study adopts a data mining-based algorithm, the random forests classifier, with geo-spatial data to construct a regional landslide susceptibility model on the basis of Typhoon Morakot in 2009. The developed model also takes account of landslide source, Run-out and non-occurrence signatures from the landslide inventory in order to increase the reliability of the susceptibility modeling. A total of ten causative factors were collected and used in this study, including topographic, vegetative, fault, geology, river, road, and soil data. This study transforms the landslide inventory and vector-based causative factors into the pixel-based format in order to overlay with other raster data for constructing the random forests based model. This study also uses different topographic data, numbers of samples and classifiers in the analysis to understand their impacts on the susceptibility modeling. Experimental results demonstrate that after identifying the Run-out signatures, the overall accuracy and Kappa coefficient have increased to be more than 85 % and 0.8, respectively. In addition, the user's accuracy and producer's accuracy of the Run-out class can reach 0.9 when the number of samples is sufficient to analyze. Correcting unreasonable topographic features of the digital terrain model and using the random forests algorithm also produce more reliable modeling results. According to the modeling results, a preliminary landslide susceptibility map of the study site is produced. Based on this map, future researches may further include other information to achieve landslide hazard and risk assessments as well as to assist land planning and policy marking.

The objective of this study is to develop a novel method of indoor positioning and navigation with the use of spherical panoramic image (SPI).Two steps are planned in the technology roadmap. Firstly, establishing a control SPI database that contains a good number of well-distributed control SPIs pre-acquired in the target space. A control SPI means an SPI with known exterior orientation parameters (EOPs). Having a control SPI database, the target space will be ready to provide the service of positioning and navigation. Secondly, the position and orientation parameters (POPs) of a newly taken SPI can be solved by using overlapped SPIs searched from the control SPI database. For validation, two kinds of corresponding points were applied in the experiment. The result of calculation were shown in this research including the analysis and discussion.

Due to lack of camera information, automatic procedures of photogrammetric triangulation and image registration can't be applied to the historical images. Manual image stitching which is required to register the images is time consuming. Scale invariant feature transform (SIFT) algorithm is widely used to perform feature extraction because the SIFT feature descriptor is invariant to uniform scaling, orientation, and partially invariant to illumination changes. This study employed the SIFT algorithm to automate image matching and then used least-squares adjustment to calculate the six parameters of each image on the basis of affine transformation. Through a further study, the transformation parameters were transformed from the image coordinate system to the ground coordinate system for image registration.

This paper focuses on the comparison of evaluating the volume of gravel by using two different devices: UAV and Laser Scanner. The research result indicates that the difference between the volume of gravel measured by UAV analysis of multi-view modeling technology and Laser Scanner is less than 5%. In addition, our research result also reveals that UAV has the greater advantages than Laser Scanner on surveying the topography when considering the budgets of equipment and data processing time. The two biggest drawbacks of Laser Scanner are the high demand for computer equipment and the longer data collecting and processing time. In contrast, UAV can provide acceptable surveying results in a more efficient, accurate, and economic way. We recommend using three ground control points to obtain good evaluating results for the volume of gravel.