• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.8
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• pp.62-71
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• 2001

This paper presents a design of the real-time digital image enhancement preprocessor for CMOS image sensor. CMOS image sensor offers various advantages while it provides lower-quality images than CCD does. In order to compensate for the physical limitation of CMOS sensor, the spatially adaptive contrast enhancement algorithm was incorporated into the preprocessor with color interpolation, gamma correction, and automatic exposure control. The efficient hardware architecture for the preprocessor is proposed and was simulated in VHDL. It is composed of about 19K logic gates, which is suitable for low-cost one-chip PC camera. The test system was implemented on Altera Flex EPF10KGC503-3 FPGA chip in real-time mode, and performed successfully.

• Journal of the Korean Geographical Society
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• v.45 no.1
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• pp.49-74
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• 2010

Cities are major sources of greenhouse gas emissions but also suitable places for implementing proactive climate mitigation and adaptation strategies. Based on the interdisciplinary review of literature, we categorize the current discussion about urban climate mitigation and adaptation planning, policy and practices into four perspectives - sustainability science, global change science, multilevel governance, and structural engineering. While these four schools of thought have distinct perspectives rooted in different disciplinary lenses, our synthesis of the literature identifies several universal themes that are common to all of the perspectives in the context of combating threats posed by climate change. The Portland case study illustrates that a city can make changes to reduce greenhouse gas emissions and increase adaptive capacity to climate change impacts by implementing smart growth, devising local climate action plans that target emission reductions in various sectors, recognizing the interactions and influences of multiple scales of governance, and supporting the installation of various green infrastructures that contribute to green economy. Furthermore, a university can serve as a hub in this climate mitigation and adaptation arena by connecting various levels of community organizations in both public and private sectors, creating innovative research centers and spatially explicit green infrastructure, designing impact assessments and campus carbon inventories, and engaging students and the larger community through service learning.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.3
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• pp.393-400
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• 2009

Recently demand for the 3D modeling technology to reconstruct real world is getting increasing. However, existing geospatial data are mainly based on the 2D space. In addition, most of the geospatial data provide geometric information only. In consequence, there are limits in various applications to utilize information from those data and to reconstruct the real world in 3D space. Therefore, it is required to develop efficient 3D mapping methodology and data for- mat to establish geospatial database. Especially digital elevation model(DEM) is one of the essential geospatial data, however, DEM provides only spatially distributed 3D coordinates of the natural and artificial surfaces. Moreover, most of DEMs are generated without considering terrain properties such as surface roughness, terrain type, spatial resolution, feature and so on. This paper suggests adaptive and flexible geospatial data format that has possibility to include various information such as terrain characteristics, multiple resolutions, interpolation methods, break line information, model keypoints, and other physical property. The study area was categorized into mountainous area, gently rolling area, and flat area by taking the terrain characteristics into account with respect to terrain roughness. Different resolutions and interpolation methods were applied to each area. Finally, a 3D digital map derived from aerial photographs was integrated with the geospatial data and visualized.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.