Machine learning-based spatial data development for optimizing astronomical observatory sites in Indonesia.

Astronomical observatory construction plays an essential role in astronomy research, education, and tourism development worldwide. This study develops siting distribution scenarios for astronomical observatory locations in Indonesia using a suitability analysis by integrating the physical and atmospheric observatory suitability indexes, machine learning models, and long-term climate models. Subsequently, potential sites are equalized based on longitude and latitude zonal divisions considering air pollution disturbance risks. The study novelty comes from the integrated model development of physical and socio-economic factors, dynamic spatiotemporal analysis of atmospheric factors, and the consideration of equitable low air-pollution-disturbance-risk distribution in optimal country-level observatory construction scenarios. Generally, Indonesia comprises high suitability index and low multi-source air pollution risk areas, although some area has high astronomical suitability and high-medium air pollution risk. Most of Java, the east coast of Sumatra, and the west and south coasts of Kalimantan demonstrate "low astronomical suitability-high air pollution risk." A total of eighteen locations are recommended for new observatories, of which five, one, three, four, two, and three are on Sumatra, Java, Kalimantan, Nusa Tenggara, Sulawesi, and Papua, respectively. This study provides a comprehensive approach to determine the optimal observatory construction site to optimize the potential of astronomical activities.

Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery.

Plastic waste monitoring technology based on Earth observation satellites is one approach that is currently under development in various studies. The complexity of land cover and the high human activity around rivers necessitate the development of studies that can improve the accuracy of monitoring plastic waste in river areas. This study aims to identify illegal dumping in a river area using the adjusted plastic index (API) and Sentinel-2 satellite imagery data. Rancamanyar River has been selected as the research area; it is one of the tributaries of Citarum Indonesia and is an open lotic-simple form, oxbow lake type river. Our study is the first attempt to construct an API and random forest machine learning using Sentinel-2 to identify the illegal dumping of plastic waste. The algorithm development integrated the plastic index algorithm with the normalized difference vegetation index (NDVI) and normalized buildup indices. For the validation process, the results of plastic waste image classification based on Pleiades satellite imagery and Unmanned Aerial Vehicle (UAV) photogrammetry was used. The validation results show that the API succeeded in improving the accuracy of identifying plastic waste, which gave a better correlation in the r-value and p-value by + 0.287014 and + 3.76 × 10-26 with Pleiades, and + 0.143131 and + 3.17 × 10-10 with UAV.