and regional planning and Geo-information Management (itc-pGM), university of twente, enschede, the netherlands;

In the modern era where technology usage is a tradition of the generation, integrating the teaching and learning with mediums that could catch up and satisfy pupils' interest is noteworthy. In line with this, the contributions of GeoGebra in the teaching-learning of mathematics: as a tool to foster students' interest and achievement, and as an environment to flourish different learning styles are explored in this study. Besides, the cautions to consider before implementing a GeoGebra integrated lesson with the challenges, limitations and areas of future development are indicated. Among these: the belief and technology fluency of users and the student class ratio are found to be among the challenges for effective integration of GeoGebra in mathematics lessons. The difficulty of some commands in the input bar especially for students and teachers with no prior programming experience are considered among the limitations of GeoGebra.

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The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deformation identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS.

Abstract. This paper is focused on SAR interferometry for deformation monitoring, based on the use of passive and active reflectors. Such reflectors are needed in all cases where a sufficient response from the ground is not available. In particular, the paper describes the development of a low-cost active reflector. This development was carried out in an EU H2020 project called GIMS. The paper summarizes the key characteristics of the developed active reflector. The reflector was tested in two main experiments: the first one located in the campus of CTTC and the second one in a GIMS test site located in Slovenia. The experiments demonstrate the visibility of the active reflectors and provide the first results concerning the phase stability of such devices.