• Korean Journal of Remote Sensing
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• v.21 no.6
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• pp.457-468
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• 2005

Increasing of impervious surface resulting from urban development has negative impacts on urban environment. Therefore, it is absolutely necessary to estimate and quantify the temporal and spatial aspects of impervious area for study of urban environment. In many cases, conventional image classification methods have been used for analysis of impervious surface fraction. However, the conventional classification methods have shortcoming in estimating impervious surface. The DN value of the each pixel in imagery is mixed result of spectral character of various objects which exist in surface. But conventional image classification methods force each pixel to be allocated only one class. And also after land cover classification, it is requisite to additional work of calculating impervious percentage value in each class item. This study used the spectral mixture analysis to overcome this weakness of the conventional classification methods. Four endmembers, vegetation, soil, low albedo and high albedo were selected to compose pure land cover objects. Impervious surface fraction was estimated by adding low albedo and high albedo. The study area is the Tanchon watershed which has been rapidly changed by the intensive development of housing. Landsat imagery from 1988, 1994 to 2001 was used to estimate impervious surface fraction. The results of this study show that impervious surface fraction increased from $15.6\%$ in 1988, $20.1\%$ in 1994 to $24\%$ in 2001. Results indicate that impervious surface fraction can be estimated by spectral mixture analysis with promising accuracy.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.16 no.2
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• pp.93-104
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• 2013

The purpose of this study was to develop a list of plants that adapted to the aquatic environment in urban areas based on the list of plants surveyed through literature review and field surveys, and to classify the types of vegetation according to the five categories of plant distributions set by the U.S. Fish and Wildlife Service (1988) in the aspect of the adaptability of plants to the aquatic environment. Results of the classification by category according to the adaptability to the aquatic environment for the plant species surveyed through literature review and field surveys showed that there are 45 species of OBL, 96 species of FACW, 66 species of FAC, and 94 species of FACU, totaling 650 species. In addition, a total of 50 species excluding exotic species, endangered species, and naturally introduced plants are proposed as appropriate plants for the urban aquatic environment that will be artificially constructed. The results of the study can be utilized as the basic information for maintaining diversity and stability of the ecosystem during the restoration of water ecology; they can serve as useful data for the development of an optimum vegetation model when planting in water spaces in the future and preparing proper planting plans for each space. In addition, it is believed that the information will be useful in wetland identification and evaluation by observing plant species that appear only in wetlands.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.533-542
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• 2020

This study proposes the logical data model design of a spatial data model that complies with international standards for the waterworks of underground facilities. We conduct a preliminary study related to underground spatial data standards and data models, and review the status of the existing systems. Then, we defined the conceptual design direction of underground spatial data model based on the problems and issues. Next, we defined the terminology, classification, semantic relationships of waterworks. Next, for the conceptual design of the underground spatial data model, we defined the naming criteria for all data according to the waterworks classification. In addition, a logical model is drawn and described using UML (Unified Modeling Language) diagrams. Based on the results, it is expected that the accuracy related to underground facilities data will be improved.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.111-121
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• 2007

Diagnosis and Prediction of urban industrial structure is a key subject for establishment of sustainable urban development plan. By this time, studies of industry-related urban spatial structure have been concentrated on measurement of space distribution by industry type mainly using data about urban industries or total worker numbers. Now, status of workers become an important issue so this study analyzed qualitative change of urban industrial structure in the view of space using work status classification system. For that, data for work status in 1994 and 2004 were collected in towns and villages, and space analysis units were coincided based on change data between 1994 and 2004. Then, it analyzed spatial distribution pattern of employment through qualitative standard called work status using GIS. The analysis results by work status type of Busan industrial structure in GIS circumstance were as below. First, traditional labor intensive industries met a limit and service and wholesale/retail sale industries went to be poor livelihood. Therefore, Busan's employment rate should be decreased and worker numbers were statistically increased, however, irregular and non-wage workers were suddenly increased. So, it was determined that the quality of employment in Busan area came down. Second, a traditional downtown area has dwindled; on the other hand, employment has been increased in new town or new industrial complex and in the area developed services rather than the manufacturing industry. It is expected that the result of this study may be meaningful as data to prepare for longterm industrial development plan through qualitative evaluation called work status as well as to make behavior pattern of industrial structure which is basis of urban development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3384-3398
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• 2020

In recent years, as the environment has become an important issue in dealing with food, energy, and urban development, diverse environment-related applications such as environmental monitoring and ecosystem management have emerged. In such applications, automatic classification of animals using video or sound is very useful in terms of cost and convenience. So far, many works have been done for animal sounds classification using artificial intelligence techniques such as a convolutional neural network. However, most of them have dealt only with the sound of a specific class of animals such as bird sounds or insect sounds. Due to this, they are not suitable for classifying various types of animal sounds. In this paper, we propose a sound classification scheme based on a multi-feature network for classifying sounds of multiple species of animals. To do that, we first collected multiple animal sound datasets and grouped them into classes. Then, we extracted their audio features by generating mixed records and used those features for training. To evaluate the effectiveness of our scheme, we constructed an animal sound classification model and performed various experiments. We report some of the results.

• The Journal of the Acoustical Society of Korea
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• v.39 no.3
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• pp.143-149
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• 2020

This paper presents the effect of the feature extraction methods used in the audio preprocessing on the classification performance of the Convolutional Neural Networks (CNN). We extract mel spectrogram, log mel spectrogram, Mel Frequency Cepstral Coefficient (MFCC), and delta MFCC from the UrbanSound8K dataset, which is widely used in environmental sound classification studies. Then we scale the data to 3 distributions. Using the data, we test four CNNs, VGG16, and MobileNetV2 networks for performance assessment according to the audio features and scaling. The highest recognition rate is achieved when using the unscaled log mel spectrum as the audio features. Although this result is not appropriate for all audio recognition problems but is useful for classifying the environmental sounds included in the Urbansound8K.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.9 no.4
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• pp.113-124
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• 1989

In this study, we tried to extract the land-use change information of Seoul city using the multiple date images of the same geographic area. Multiple date image set is MSS('72, '79, '81, '93) and TM('85), and we carried out geometric correction, digitizing(due to the administrative boundary) in pre-processing process. In addition, we performed land-use classification with MLC(Maximum Likelihood Classifier) after improving the predictive accuracy of classification by filtering technique. At the stage of classification, ground truth data, topographic maps, aerial photographs were used to select the training field and statistical data of that time were compared with the classification result to prove the accuracy. As a result, urban area in Seoul has been increased('72 : 25.8 %${\rightarrow}$'81 : 43.0 %${\rightarrow}$'85 : 51.9 %) and Forest area decreased ('72 : 39.0 %${\rightarrow}$'85 : 28.4 %) as we estimated. Finally, it is concluded that the utilzation of satellite imagery is very effective, economical and helpful in the urban land-use/land-cover monitoring.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.897-901
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• 2008

The objective of this study is to conduct land cover classification respectively using Landsat TM data collected on Oct., 1985 and KOMPSAT-1 EOC data collected on Jan., 2000 covering Gumi city, Gyeongbuk Province and to detect urban change by comparing between both land cover maps. Multispectral images of Landsat TM have spatial resolution of 30m are well known as useful data for extracting information related to landcover, vegetation classification, urban growth analysis and so forth. In contrast, as KOMPSAT-1 EOC collects panchromatic images with relatively high spatial resolution of 6.6m. We try to analyze how accurate landcover classification result is able to be derived from the panchromatic images. As the results of the study, the KOMPSAT EOC data with high resolution greater than 4 times showed higher classification degree than Landsat TM data. It was ascertained that the built-up region was extended by three to four times in the last 15 years between 1985 and 2000. In the contrast, it was shown that the forest region was decreased by 15% to 27% and the grass region including agricultural region was decreased by 28% to 45%.

• Journal of the Korean Institute of Rural Architecture
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• v.26 no.1
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• pp.1-9
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• 2024

This study was motivated by the need to identify the characteristics of professionals involved in planning exterior spaces in urban development projects and to provide guidelines for maintaining the identity of urban images. Urban development's impact on external spaces can alter the image of the urban structure, especially due to the many boundaries between urban and rural areas. Therefore, there is a need for public standards for external spaces in such projects. This study collects and analyzes experts' opinions to understand the characteristics of each professional, selects the relative importance of each, and uses this as a guideline for step-by-step deliberation in planning external spaces. The research scope includes analyzing each expert's characteristics based on the external space evaluation items from Lee Limjeong's 2023 study, which builds upon existing research, and presenting the importance and priority of each expert. As a methodology, a questionnaire was conducted for each expert group using the indicators established in Lee Lim Jung's 2023 study for external spaces in urban development projects. An in-depth analysis was performed using the Analytic Hierarchy Process (AHP) for each expert. Using AHP analysis, the composite weight for each of the 17 detailed items was first adjusted by the number of item weights to account for the classification level of the large and detailed items. Then, the composite importance was calculated by multiplying the importance of the large, medium, and detailed classifications. The calculated composite importance was finally adjusted by applying the number of item weights again, ensuring the sum of the 17 importance values equals 1. The final importance calculated through this process was then presented by occupation.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.223-232
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• 2023

A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.