Online ISSN : 1347-541X
Print ISSN : 0388-502X
ISSN-L : 0388-502X
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Cover (GEOINFORMATICS 2020 Vol31. No.3)
  • Hang T. DO, Go YONEZAWA, Venkatesh RAGHAVAN, Poliyapram VINAYARAJ, Lua ...
    Type: Article
    2020 Volume 31 Issue 3 Pages 67-78
    Published: September 25, 2020
    Released: September 25, 2020

    Remote sensing images has been reported as valuable data to extract the rice terrace. However, most of these studies have been focused on high and very high spatial resolution remotely sensed images. In this paper, we investigate the capability of three medium resolution remote sensing data, namely, RapidEye, Sentinel-2, and Landsat-8 for rice terrace extraction. Moreover, both Pixel Based Image Analysis (PBIA) and Object Based Image Analysis (OBIA) are utilized to classify rice terrace using robust machine learning classifiers, namely, Multilayer Perceptron, Random Forest, and Support Vector Machine algorithms. All three remote sensing data provide high accuracies of rice terrace classification with PBIA, with accuracies ranging from 90.3% to 92%. OBIA does not perform as well as PBIA. In general, the accuracy of OBIA decreases when the threshold of segmentation increases. OBIA applied RapidEye provides accuracy higher than 85%. Sentinel-2 shows lower accuracy at above 80%. Landsat-8 image shows the least accuracy below 75% at higher threshold levels. Although the classification accuracy for OBIA shows dependence on spatial resolution of remote sensing images, the output results for the three classifiers do no show significant difference except in the ability to distinguish smaller patches of rice terrace in images of higher resolution. Based on the results, PBIA is considered to offer a simple and more accurate method for rice terrace extraction in the study area.

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Development of System and Software
  • KITAO Kaoru
    Type: Development of System and Software
    2020 Volume 31 Issue 3 Pages 79-85
    Published: September 25, 2020
    Released: September 25, 2020

    Scientists engaged in geology often come in contact with point data indicating the location like outcrops, borings and so on, and expand them on a web based 2D-map to show the location in general. However, increasing the number of point data causes deterioration of the response speed of web application. In this study, I tried to make a web application for mapping a huge number of points with new approach by using WebGL, attempted to improve response speed to operations of application. I adopted "Point Cloud PNG (PCPNG)" as a format for delivering point data instead of a well-known format such as JSON, GeoJSON or binary vector tiles. As a result, I have successfully developed a web application with good responsiveness, therefore, my developed mechanism as well as its comparison with already existed web applications for mapping point cloud are herewith introduced.

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