Lunar features detection for energy discovery via deep learning

Chen, Siyuan; Li, Yu; Tao, Zhang; Zhu, Xingyu; Sun, Shuyu; Gao, Xin

doi:10.1016/j.apenergy.2021.117085

Cited by 13 publications

(11 citation statements)

References 23 publications

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“…This section experiments with different networks, including UNet [26], ResUnet [35], GLNet [24], HRNet [23], and GL-HRNet. There were 30,000 training images, 3000 verification images, and 3000 test images.…”

Section: Analysis Of the Lunar Impact Crater Detection Resultsmentioning

confidence: 99%

“…In this paper, four kinds of different data sets were used for experiments: lunar digital elevation data SLDEM [29], Mars HRSC MOLA Blend DEM Global 200 m v2, Surface crack [30], and an assembled dataset [23]. The SLDEM data have a resolution of 59 m and span ±60 • latitude (maximum longitude range).…”

Section: Datasetmentioning

confidence: 99%

“…The assembled data set, referred to as the data set of [23], was carefully selected from the online library published by NASA and the national astronomical observatories of China, and it screened 44 clear images of impact craters and Moon rilles from 1000 images. The image size was 256 × 256 pixels.…”

Section: Datasetmentioning

confidence: 99%

“…They are designed to probe only craters, not Moon rilles, directly related to lunar energy discoveries. The high-resolution net (HRNet) model proposed by Chen [23] is a depth model for human pose estimation. Still, this model's accuracy rate (83.7%) and recall rate (53.8) are low, resulting in poor recognition performance.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery

et al. 2022

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Lunar craters and rilles are significant topographic features on the lunar surface that will play an essential role in future research on space energy resources and geological evolution. However, previous studies have shown low efficiency in detecting lunar impact craters and poor accuracy in detecting lunar rilles. There is no complete automated identification method for lunar features to explore space energy resources further. In this paper, we propose a new specific deep-learning method called high-resolution global–local networks (HR-GLNet) to explore craters and rilles and to discover space energy simultaneously. Based on the GLNet network, the ResNet structure in the global branch is replaced by HRNet, and the residual network and FPN are the local branches. Principal loss function and auxiliary loss function are used to aggregate global and local branches. In experiments, the model, combined with transfer learning methods, can accurately detect lunar craters, Mars craters, and lunar rilles. Compared with other networks, such as UNet, ERU-Net, HRNet, and GLNet, GL-HRNet has a higher accuracy (88.7 ± 8.9) and recall rate (80.1 ± 2.7) in lunar impact crater detection. In addition, the mean absolute error (MAE) of the GL-HRNet on global and local branches is 0.0612 and 0.0429, which are better than the GLNet in terms of segmentation accuracy and MAE. Finally, by analyzing the density distribution of lunar impact craters with a diameter of less than 5 km, it was found that: (i) small impact craters in a local area of the lunar north pole and highland (5°–85°E, 25°–50°S) show apparent high density, and (ii) the density of impact craters in the Orientale Basin is not significantly different from that in the surrounding areas, which is the direction for future geological research.

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Section: Analysis Of the Lunar Impact Crater Detection Resultsmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery

et al. 2022

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“…Recently, deep learning has demonstrated high prediction accuracy in computer vision tasks [25] as well as in computational biology [26,27,28]. A simple neural network can obtain very complex underlying features, which is especially suitable for large-scale data sets and sparse dimension cases.…”

Section: Introductionmentioning

confidence: 99%

HiC-LDNet: A general and robust deep learning framework for accurate chromatin loop detection in genome-wide contact maps

Chen

Wang

Gao

et al. 2022

Preprint

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Motivation: Identifying chromatin loops from genome-wide interaction matrices like Hi-C data is notoriously difficult. Such kinds of patterns can span through the genome from a hundred kilobases to thousands of kilobases. Most loop patterns are frequently related to biological functions, such as providing contacts between regulatory regions and promoters. They can also affect the cell-specific biological functions of different regulatory regions of DNA, thus leading to disease and tumorigenesis. While most statistical methods failed in the generalization to multiple cell types, recently proposed machine learning-based methods struggled when tested on sparse single-cell Hi-C (scHi-C) contact maps. We notice that there is an urgent need for an algorithm that can handle sparse scHi-C maps, and at the same time, can generate confident loop calls on regular cell lines. Results: Therefore, we propose a novel deep learning-based framework for Hi-C chromatin loop detection (HiC-LDNet) and provide the corresponding downstream analysis. HiC-LDNet can give relatively more accurate predictions in multiple tissue types and contact technologies. Compared to other loop calling algorithms, such as HiCCUPS, Peakachu, and Chromosight, HiC-LDNet recovers a higher number of loop calls in multiple experimental platforms (Hi-C, ChIA-PET, DNA-SPRITE, and HiChIP), and achieves higher confidence scores in multiple cell types (Human GM12878, K562, HAP1, and H1-hESC). For example, in genome-wide loop detection on the human GM12878 cell line, HiC-LDNet successfully recovered 82.5% of loops within only 5 pixels of 10k bp resolution. Furthermore, in the sparse scHi-C ODC tissue, HiC-LDNet achieves superior performance by recovering 93.5% of ground truth loops with high confidence scores, compared with that of Peakachu (31.5%), Chromosight(69.6%), and HiCCUPS(9.5%). Therefore, our method is a robust and general pipeline for genome-wide chromatin loop detection for both bulk Hi-C and scHi-C data.

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Monitoring the green evolution of vernacular buildings based on deep learning and multi-temporal remote sensing images

et al. 2022

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Lunar features detection for energy discovery via deep learning

Cited by 13 publications

References 23 publications

An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery

An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery

HiC-LDNet: A general and robust deep learning framework for accurate chromatin loop detection in genome-wide contact maps

Monitoring the green evolution of vernacular buildings based on deep learning and multi-temporal remote sensing images

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