• Journal of the Korean Geographical Society
• /
• 제38권2호
• /
• pp.275-291
• /
• 2003

It was essential to develop the DB-type teaching and teaming material for geography instruction using a method of ICT. The DB-type teaching and learning material was considered as a alternative in solving the problems of web-based geography instruction. Accordingly, in this study, the geography image DB program as developed, and based on this program the CD-ROM called GEO-DB, having the function of electronic dictionary of geography image for geography teaching and teaming was made. The GEO-DB was composed of 3,060 geography images collected by teachers and learners. The GEO-DB was made to be used simply by teachers and learners. Especially, the portfolio function was Included in the GEO-DB, and that was focused to the instructional system design of teacher and the self-directed teaming ability development of learner. Teachers and learners using this GEO-DB assessed that because the GEO-DB had the easiness of use, the speed of reference and the unlimitedness of extension, it could enlarge the possibility of using a method of In, and it could contribute to the development of geography teaming ability and the change of geography teaming attitude.

• Journal of the Korean Geophysical Society
• /
• 제9권1호
• /
• pp.21-25
• /
• 2006

Aerial photo process with digital camera has some benefits. It is fast and simple by digital way incomparison with aerial photo based on film. Also it works with GPS/INS device to do direct geo-referencing. Sdata and digital map and GCP is produced. In base on it, ortho images are produced and compared with surveying data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 한국정보통신학회 2019년도 춘계학술대회
• /
• pp.71-73
• /
• 2019

How much and where vehicles exist is an essential element in the implementation of a GeoAI-based urban environment that reflects traffic information. In this paper, we trained vehicle data using Mask R-CNN that deep learning model useful for object detection and extraction, and verified vehicle detection in actual aerial images taken with drones.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• 제34권5호
• /
• pp.1645-1653
• /
• 2014

In general, RGB, NIR, and thermal images are used for obtaining geospatial data. Such multiband images are collected via devices mounted on satellites or manned flights, but do not always meet users' expectations, due to issues associated with temporal resolution, costs, spatial resolution, and effects of clouds. We believe high-resolution, multiband images can be obtained at desired time points and intervals, by developing a payload suitable for a low-altitude, auto-piloted UAV. To achieve this, this study first established a low-cost, high-resolution multiband image collection system through developing a sensor and a payload, and collected geo-referencing data, as well as RGB, NIR and thermal images by using the system. We were able to obtain a 0.181m horizontal deviation and 0.203m vertical deviation, after analyzing the positional accuracy of points based on ortho mosaic images using the collected RGB images. Since this meets the required level of spatial accuracy that allows production of maps at a scale of 1:1,000~5,000 and also remote sensing over small areas, we successfully validated that the payload was highly utilizable.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• 제31권2호
• /
• pp.165-172
• /
• 2013

In this paper, high-quality mosaic image is generated by high-resolution hyperspectral strip images using scale-invariant feature transform (SIFT) algorithm, which is one of the representative image matching methods. The experiments are applied to AISA Eagle images geo-referenced by using GPS/INS information acquired when it was taken on flight. The matching points between three strips of hyperspectral images are extracted using SIFT method, and the transformation models between images are constructed from the points. Mosaic image is, then, generated using the transformation models constructed from corresponding images. Optimal band appropriate for the matching point extraction is determined by selecting representative bands of hyperspectral data and analyzing the matched results based on each band. Mosaic image generated by proposed method is visually compared with the mosaic image generated from initial geo-referenced AISA hyperspectral images. From the comparison, we could estimate geometrical accuracy of generated mosaic image and analyze the efficiency of our methodology.

• Proceedings of the KSRS Conference
• /
• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
• /
• pp.763-765
• /
• 2005

In the area of remote sensing, an immense number of images are continuously generated by various remote sensing systems. These images must then be managed by a database system efficient storage and retrieval. There are many types of image database systems, among which the content-based image retrieval (CBIR) system is the most advanced. CBIR utilizes the metadata of images including the feature data for indexing and searching images. Therefore, the performance of image retrieval is significantly affected by the storage method of the image metadata. There are many features of images such as color, texture, and shape. We mainly consider the shape feature because shape can be identified in any remote sensing while color does not always necessarily appear in some remote sensing. In this paper, we propose a metadata representation and storage method for image search based on shape features. First, we extend MPEG-7 to describe the shape features which are not defined in the MPEG-7 standard. Second, we design a storage schema for storing images and their metadata in a relational database system. Then, we propose an efficient storage method for managing the shape feature data using a Wavelet technique. Finally, we provide the performance results of our proposed storage method.

• Korean Journal of Remote Sensing
• /
• 제22권5호
• /
• pp.351-356
• /
• 2006

Based on current practical needs for geo-spatial information on mobile platform, the main theme of this study is a design and implementation of dynamic 3D terrain rendering system using spaceborne imagery, as a kind of texture image for photo-realistic 3D scene generation on mobile environment. Image processing and 3D graphic techniques and algorithms, such as TIN-based vertex generation with regular spacing elevation data for generating 3D terrain surface, image tiling and image-vertex texturing in order to resolve limited resource of mobile devices, were applied and implemented by using graphic pipeline of OpenGL|ES (Embedded System) API. Through this implementation and its tested results with actual data sets of DEM and satellite imagery, we demonstrated the realizable possibility and adaptation of complex typed and large sized 3D geo-spatial information in mobile devices. This prototype system can be used to mobile 3D applications with DEM and satellite imagery in near future.

• Proceedings of the KSRS Conference
• /
• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
• /
• pp.166-169
• /
• 2006

Our country's coast is vulnerable area to natural disaster which the repetitive damages occur every year including a loss of lives, the damage of facilities and erosion mostly except for the east coast because of a typhoon, tidal waves, sea water overflowing by topographic structure of low-lying gentle slope and shallow sea. However, as for prevention of natural disaster occurring every year, the situation is that it's centered on the restorationcentered measures and the general disaster prevention research to minimize damages at the time of disaster occurrence is insufficient. This study intendedlop t to devehe techniques possible for real time sampling of damage prediction areas on Web in order to support decision making for damage prevention and establishment of disaster prevention policy. For this, the thematic map was produced related to disaster based on high-resolution satellite picture, and the environmental DB similar to real world was constructed through topographic construction of three-dimension integrating the parts of land and the sea. In addition, the system was developed possible for the expression of damageable regions by real time grasp of dangerous regions at the time of disaster occurrence through over flowing simulation of three-dimension, and it's intended to prepare a basis to minimize damages to disaster situations through it.

• Journal of Information Processing Systems
• /
• 제19권5호
• /
• pp.614-630
• /
• 2023

Dead trees significantly impact forest production and the ecological environment and pose constraints to the sustainable development of forests. A lightweight YOLOv4 dead tree detection algorithm based on unmanned aerial vehicle images is proposed to address current limitations in dead tree detection that rely mainly on inefficient, unsafe and easy-to-miss manual inspections. An improved logarithmic transformation method was developed in data pre-processing to display tree features in the shadows. For the model structure, the original CSPDarkNet-53 backbone feature extraction network was replaced by MobileNetV3. Some of the standard convolutional blocks in the original extraction network were replaced by depthwise separable convolution blocks. The new ReLU6 activation function replaced the original LeakyReLU activation function to make the network more robust for low-precision computations. The K-means++ clustering method was also integrated to generate anchor boxes that are more suitable for the dataset. The experimental results show that the improved algorithm achieved an accuracy of 97.33%, higher than other methods. The detection speed of the proposed approach is higher than that of YOLOv4, improving the efficiency and accuracy of the detection process.

• Journal of Information Processing Systems
• /
• 제18권4호
• /
• pp.587-598
• /
• 2022

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.