Earticle

초록

영어

This study will concentrate on developing an automated process for creating a 3D environment utilizing satellite imagery, a segmentation algorithm, and geospatial data. Traditional methods for crafting a 3D environment primarily rely on manually sculpting terrain and generating 3D objects, which requires substantial time, effort, and resources from the developer. We aim to introduce a system that combines satellite images, digital terrain models, and building segmentation through Python programming to create 3D environments in Unreal Engine. The implementation includes a Python Tkinter GUI for data collection and preprocessing, Mask-RCNN for building segmentation, and the use of Open Street Map (OSM) data to utilize data availability and visualization of data. The system will be evaluated by generating 3D scene environments using satellite image input and incorporating geospatial datasets to analyze and measure the visual similarities between actual and generated 3D environments.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Satellite Imagery in 3D Environment Generation
B. Building Segmentation using Deep Learning
C. Building Reconstruction
III. METHODOLOGY
A. Data Acquisition and Pre-processing
B. Digital Terrain Model (DTM) and Height Data
C. Texture Mapping
D. Procedural Generation in Unreal Engine
E. Automation Procedure
IV. RESULTS AND DISCUSSION
V. CONCLUSION AND FUTURE WORK
VI. ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Ruben D. Espejo Jr. [ Department of Artificial Intelligence Convergence Engineering Changwon National University Changwon City, South Korea ]
• Beomseok Oh [ Department of Applied Artificial Intelligence Seoul National University of Science and Technology Seoul, Republic of Korea ]
• Joongrock Kim [ Department of Artificial Intelligence Convergence Engineering Changwon National University Changwon City, South Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004