KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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A Deep-Learning Based Automatic Detection of Craters on Lunar Surface for Lunar Construction

달기지 건설을 위한 딥러닝 기반 달표면 크레이터 자동 탐지

  • Shin, Hyu Soung (Korea Institute of Civil Engineering and Building Technology) ;
  • Hong, Sung Chul (Korea Institute of Civil Engineering and Building Technology)
  • 신휴성 (한국건설기술연구원, 미래융합연구본부) ;
  • 홍성철 (한국건설기술연구원, 미래융합연구본부)
  • Received : 2018.11.15
  • Accepted : 2018.11.22
  • Published : 2018.12.01
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Abstract

A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.

달 지상 인프라 및 기지 건설은 건설재료나 에너지 확보가 가능한 지역과 연계되어야 하며, 얼음 등의 핵심 자원이 풍부한 영구음영 지역을 형성하는 달 크레이터 지형의 탐지와 정보 수집이 선행되어야 한다. 본 연구에서는 이러한 달 크레이터(crater) 객체 정보를 최신 딥러닝 알고리즘을 이용해 효과적으로 자동 탐지하는 방안에 대해 고찰하였다. 딥러닝 학습을 위해 NASA LRO 달 궤도선의 레이저 고도계 데이터를 기반으로 구축된 9만개의 수치표고모델과 개별 수치표고모델에 존재하는 크레이터들의 위치와 크기를 레이블링한 자료를 활용하였다. 딥러닝 학습은 최신 알고리즘인 Faster RCNN (Regional Convolution Neural Network)을 자체적으로 코드화하여 적용하였다. 이를 통해 학습된 딥러닝 시스템은 학습되지 않은 달표면 이미지 내 크레이터를 자동 인식하는데 적용되었으며, NASA에서 인력에 의해 정의한 크레이터 정보들의 오류를 자동 보정 가능하고, 정의되지 않은 많은 크레이터 까지도 자동 인식 가능함을 보였다. 이를 통해 공학적으로 매우 가치가 있는 각 지역별 크레이터들의 크기 분포 특성 및 발생 빈도 분석 등이 가능하게 되었으며, 향후에는 시간 이력별 변화추이도 분석 가능할 것으로 판단된다.

Keywords

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Fig. 1. A Case Study on Lunar Crater Detection by CNN (Honda et al., 2018)

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Fig. 2. Crater Size Distribution Used in This Study

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Fig. 3. Image Transformation from DEM to Hillshaded Map

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Fig. 4. Crater Labelling Status and Correction

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Fig. 5. Prediction for the Trained Dataset

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Fig. 6. Prediction for the Untrained Dataset

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Fig. 7. Prediction for the Untrained Dataset Without Labels

Table 1. Training Environment for Faster RCNN

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