• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.

• The Korean Journal of Air & Space Law and Policy
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• v.33 no.2
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• pp.419-442
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• 2018

Space Treaty Article 2 stipuates non-appropriation by sovereignty, and in any other means. Interpretative controversies has continued as regards the meaning of any other means. It is not clear whether appropriation by private entity is also prohibited or not. Furthermore, the controverse around the binding force of Article 1 has made worse the controversy regarding such appropriation. U.S. Congress has enacted the law regarding the space resouce mining in 2015. Its main purpose is to alleviate legal unstability which U.S, private companies have faced, and it provides some provisions regarding private rights about space resources. Original bill, H.R. 1508 included the property right. Amendment to the bill is to ensure that an "asteroid resource utilization activity" is inter-preted as on a single asteroid and not on any asteroid. The use of the word "in situ" in defining space resources simply means resources in place in outer space; but any such resource within or on an asteroid would need to be "obtained" in order to confer a property right. The use of the word "in situ" in merely defining a space resource in the bill is not equivalent to claiming sovereignty or control over celestial bodies or portions of space. Further, there is clear Congressional direction in the bill that the President is only to encourage space resources exploration and utilization, including lowering barriers to such activity, "consistent with" and "in accordance with" US international obligations. Federal courts are granted original jurisdiction over entities defined in ${\S}$ 51301(4) and in-situ asteroid resources that have been removed from an asteroid by such entities. Federal courts are not granted jurisdiction over outer space, the Moon, other celestial bodies, or the asteroid from which the in-situ natural resource was removed. It is said that the Space Resource Utilization Exploration Act of 2015, talked about the rights of private players to own-kind of a "finders keepers" law.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.403-417
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• 2023

In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.