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The main goal of our ongoing research is to design a low-cost continuous monitoring system for landslide investigation using the Reverse RTK (RRTK) technique. The main objectives of this paper are to review the existing Global Positioning System (GPS) tools and techniques used for landslide monitoring, and to propose a novel low-cost landslide monitoring technique using Reverse RTK GPS. A general overview of GPS application in landslide monitoring is presented, followed by a review of GPS deformation monitoring systems and some of the factors used for their categorization. Finally, the concept, principles and advantages of the proposed new landslide monitoring system are discussed.

Various researchers in Digital Image processing have developed keen interest in the automation of object detection, description and extraction process used for various applications and this has led to the development of series of Feature detection and extraction models one of which is the Maximally Stable Extremal Regions Feature Algorithm (MSER).  This paper investigates the robustness of MSER algorithm (a blob-like and affine-invariant feature detector) for the detection and extraction of corresponding features used for the automatic registration of series of overlapping images. The robustness investigation was carried out in three different registration campaigns using overlapping images extracted from google earth and image pair acquired from an Unmanned Aerial Vehicle (UAV). Sum of Square Difference (SSD) and Bilinear interpolation models were used to establish the similarity measure between the images to be registered, resampling of the pixel-values and computation of non-integer coordinates respectively while Random Sampling Consensus (RANSAC) algorithm was used to exclude the outliers and to compute the transformation matrix using affine transformation function. The results obtained from this preliminary investigation shows that the processing speed of MSER is quite high for Automatic Image Registration with a relatively high accuracy. While an accuracy of 61.54% was obtained from the first campaign with a processing time of 11.92 seconds, the second campaign gave an accuracy of 52.02% with a processing time of 11.20 seconds and the third campaign produced an accuracy of 55.62% with a processing time of 3.27 seconds. The obtained speed and accuracy shows that MSER is a very robust model and as such, can be deployed as the feature detection and extraction model in the development of an automatic image registration scheme.