Abstract
The data obtained with remote sensing sensors are processed to fuel studies related to the Earth's resources and environment. Sensor technologies being developed are increasing the diversity and capabilities of remote sensing data. However, these advancements are also multiplying the volume of information enormously, straining resources for processing the data. Meanwhile, the analysis and interpretation of most satellite data have not yet been fully automated, and human intervention is still necessary at certain stages. In addition, recent deep-learning methods that offer solutions to many remote sensing problems require a large amount of labeled data.
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