Rice is one of the major crops in the world, especially in Asia. The height of rice is an important feature of growth health. Over-fertilization makes plants grow too high so to tend to rice lodging, while less-fertilization makes plants too short so to yield incompletely. In practice, panicle fertilization decision making is judged by experience. Traditionally academic survey of rice height uses wooden rulers to measure the rice height in the field. With the popularization of Unmanned Aerial Vehicles (UAV) and the advancement of technology, experts have been able to use remote sensing methods to investigate agricultural heterogeneous traits on a large scale. This study uses UAV to measure the height variation of rice for various levels of fertilization at different growth stages. This study also formulating UAV flight procedure of rice monitoring. Top 100 height average and K-means clustering (or Lloyd–Forgy algorithm) were executed and compared for the measurement of rice height in a paddy. The variation of rice height can be monitored as a reference for cultivation decision, smart yield, and risk management.

Building model is an essential element in a topographic map. In order to improve the automation of building extraction, this research uses deep learning technology to identify building regions and changed areas from multi-temporal aerial orthoimage. The building polygons from Taiwan e-Map and corresponding aerial orthoimage are combined to generate the training dataset for deep learning. The building regions are automatically predicted by multispectral orthoimage using convolutional neural network. Then, the change detection compares bi-temporal building regions from deep learning in two seasons. The experimental results indicated that the F1-score and recall in building detection were 74% and 90%, respectively. The error is mainly caused by the relief displacement of building. Moreover, the accuracy of change detection is mainly related to the size of building area.

All simulations in the study were conducted in SketchUp, and were demonstrated using SketchUp Dynamic Components. In order to improve design workflow, this study focuses on five basic elements (behaviors): (1) material, (2) scale, (3) hidden, (4) on-Click, and (5) copy. Designers will be benefited by this study in time-saving when applying dynamic 3D animation. In the future, dynamic 3D animation will combine mobile devices and cloud simulation, and in conjunction with virtual reality can interact with users to further carry out the modification on the size and material of 3D models as a digital avatar.

This study focuses on the causes and sources of aeolian dust along the Gaoping River. First, various data, including wind speed, wind direction, and PM10, are collected to draw wind rose charts and isarithmic maps. The illustrations point out that generally, dusts are raised by south winds and later transported north along the river. Next, satellite images between 2018 and 2020 are converted to NDVI and NDWI modes with the aim of determining the sources of aeolian dust. Different colors are used to depict the special distributions of fallow farmlands, sandbars, high and low riverbanks, etc. After that, with the use of QGIS-SCP supervised image classification and its later converted vector mode, overlay analysis over the years is performed. The results suggest that wide fallow farmlands and sandbars of Erzhong River, both lying in the northern section of the research area, comprise the greatest threat of aeolian dust occurrences. Based on the analysis mentioned above as well as field investigation, this study lays out the potential areas of aeolian dust occurrence along the Gaoping River, and it is hoped to assist the future plans for suppressing aeolian dust.