The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra platform, which was launched in 1999, has three separate subsystems: a visible and near-infrared (VNIR) radiometer, a shortwave-infrared radiometer, and a thermal-infrared radiometer. The ASTER VNIR bands have been radiometrically corrected for approximately 14 years by the sensor degradation curves estimated from the onboard calibrator according to the original calibration plan. However, this calibration by the onboard calibrator encountered a problem; specifically, it is inconsistent with the results of vicarious calibration and cross calibration. Therefore, the ASTER VNIR processing was applied by the radiometric degradation curves calculated from the results of three calibration approaches, i.e., the onboard calibrator, the vicarious calibration, and the cross calibration since February 2014. Even though the current degradation curves were revised, the inter-band and lunar calibrations show some inconsistencies owing to the different traceability in the bands by different calibration approaches. In this study, the current degradation curves and their problems are explained, and the new curves that are derived from the vicarious calibration with lunar calibration are discussed. The new degradation curves that have the same traceability in the bands will be used for future ASTER VNIR processing.

Augmented Reality (AR) and Internet of Things (IoT) devices can converge, we call it AR + IoT. The integration of AR and IoT has promising opportunities for future product research and development. IoT is a tool for communicating, monitoring, and remotely controlling with connected products, while AR is a means to see and find out what is happening with connected products based on visualization/interface based on additional information. The mapping review method was used to carry out this research. The result is that as many as 43 papers from 2014-2020 are the main targets of review. The largest publication database is in the IEEE Xplore Digital Library, the application dominates in the field of social life, the research objectives are varied, and the recommended future work is to maximize the potential for integration between AR and IoT or even with other promising devices. Then comfort, convenience, and user experience are also recommended in implementing AR + IoT

Indonesian Sign Language System (SIBI) recognition plays a crucial role in improving effective communication for individuals with hearing loss in Indonesia. To support automatic SIBI recognition, this research presents a performance analysis of two main algorithms, namely Decision Tree and C4.5, in the context of the SIBI recognition task. This research utilizes a rich SIBI dataset that includes a variety of SIBI signs used in everyday communication. Data pre-processing, model construction with both algorithms, and model performance evaluation using accuracy, precision, recall, and F1-score metrics are all part of the study. Regarding SIBI recognition accuracy, the experimental results demonstrate that the Decision Tree performs better than Decision Tree. The Decision Tree also makes models that are easier to understand, which is important for making communication systems based on SIBI.

A method for landslide area detection based on EfficientNetV2 with optical image converted from SAR image using pix2pixHD with a spatial attention mechanism in the loss function is proposed. Meteorological landslides such as landslides after heavy rains occur regardless of day or night and weather conditions. Meteorological landslides such as landslides are easier to visually judge using optical images than SAR images, but optical images cannot be observed at night, in the rain, or on cloudy days. Therefore, we devised a method to convert SAR images, which allow all-weather observation regardless of day or night, into optical images using pix2pixHD, and to learn about landslide areas using the converted optical images to build a trained model. We used SAR and optical images derived from Sentinel-1 and -2, which captured landslides caused by the earthquake on 14 April 2016, as training data, and constructed a learning model that classifies landslide areas using EfficientNetV2. We evaluated the superiority of the proposed method by comparing it with a learning model that uses only SAR images. As a result, it was confirmed that the F1-score and AUC were 0.3396 and 0.2697, respectively, when using only SAR images, but were improved by 1.52 to 1.84 times to 0.6250 and 0.4109, respectively, when using the proposed method.