• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.541-550
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• 2014

This paper describes a multi-scale user interaction based tiled display visualization system using multiple input devices for monitoring and analyzing Geostationary Ocean Color Imager (GOCI) observation satellite imagery. This system provides multi-touch screen, Kinect motion sensing, and moblie interface for multiple users to control the satellite imagery either in front of the tiled display screen or far away from a distance to view marine environmental or climate changes around Korean peninsular more effectively. Due to a large amount of memory required for loading high-resolution GOCI satellite images, we employed the multi-level image load technique where the image was divided into small tiled images in order to reduce the load on the system and to be operated smoothly by user manipulation. This system performs the abstraction of common input information from multi-user Kinect motion and gestures, multi-touch points and mobile interaction information to enable a variety of user interactions for any tiled display application. In addition, the unit of time corresponding to the selected date of the satellite images are sequentially displayed on the screen and multiple users can zoom-in/out, move the imagery and select buttons to trigger functions.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.