197 research outputs found

    Subsurface robotic exploration for geomorphology, astrobiology and mining during MINAR6 campaign, Boulby Mine, UK: : part II (Results and Discussion)

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    Acknowledgement. The authors of this paper would like to thank Kempe Foundation for its generous funding support to develop KORE, the workshop at the Teknikens Hus, Luleå, for their invaluable and unconditional support in helping with the fabrication of the KORE components and the organizers of the MINAR campaign comprising the UK Centre of Astrobiology, ICL Boulby Mine and STFC Boulby Underground Laboratory, UK. MPZ has been partially funded by the Spanish State Research Agency (AEI) Project No. MDM-2017-0737 Unidad de Excelencia ‘María de Maeztu’- Centro de Astrobiología (INTA-CSIC)Peer reviewedPostprin

    Computer-generated global map of valley networks on Mars

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    The presence of valley networks (VN) on Mars suggests that early Mars was warmer and wetter than present. However, detailed geomorphic analyses of individual networks have not led to a consensus regarding their origin. An additional line of evidence can be provided by the global pattern of dissection on Mars, but the currently available global map of VN, compiled from Viking images, is incomplete and outdated. We created an updated map of VN by using a computer algorithm that parses topographic data and recognizes valleys by their morphologic signature. This computer-generated map was visually inspected and edited to produce the final updated map of VN. The new map shows an increase in total VN length by a factor of 2.3. A global map of dissection density, D, derived from the new VN map, shows that the most highly dissected region forms a belt located between the equator and mid-southern latitudes. The most prominent regions of high values of D are the northern Terra Cimmeria and the Margaritifer Terra where D reaches the value of 0.12 km−1 over extended areas. The average value of D is 0.062 km−1, only 2.6 times lower than the terrestrial value of D as measured in the same fashion. These relatively high values of dissection density over extensive regions of the planet point toward precipitation-fed runoff erosion as the primary mechanism of valley formation. Assuming a warm and wet early Mars, peculiarity of the global pattern of dissection is interpreted in the terms of climate controlling factors influenced by the topographic dichotomy

    Automated Image Interpretation for Science Autonomy in Robotic Planetary Exploration

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    Advances in the capabilities of robotic planetary exploration missions have increased the wealth of scientific data they produce, presenting challenges for mission science and operations imposed by the limits of interplanetary radio communications. These data budget pressures can be relieved by increased robotic autonomy, both for onboard operations tasks and for decision- making in response to science data. This thesis presents new techniques in automated image interpretation for natural scenes of relevance to planetary science and exploration, and elaborates autonomy scenarios under which they could be used to extend the reach and performance of exploration missions on planetary surfaces. Two computer vision techniques are presented. The first is an algorithm for autonomous classification and segmentation of geological scenes, allowing a photograph of a rock outcrop to be automatically divided into regions by rock type. This important task, currently performed by specialists on Earth, is a prerequisite to decisions about instrument pointing, data triage, and event-driven operations. The approach uses a novel technique to seek distinct visual regions in outcrop photographs. It first generates a feature space by extracting multiple types of visual information from the image. Then, in a training step using labeled exemplar scenes, it applies Mahalanobis distance metric learning (in particular, Multiclass Linear Discriminant Analysis) to discover the linear transformation of the feature space which best separates the geological classes. With the learned representation applied, a vector clustering technique is then used to segment new scenes. The second technique interrogates sequences of images of the sky to extract, from the motion of clouds, the wind vector at the condensation level — a measurement not normally available for Mars. To account for the deformation of clouds and the ephemerality of their fine-scale features, a template-matching technique (normalized cross-correlation) is used to mutually register images and compute the clouds’ motion. Both techniques are tested successfully on imagery from a variety of relevant analogue environments on Earth, and on data returned from missions to the planet Mars. For both, scenarios are elaborated for their use in autonomous science data interpretation, and to thereby automate certain steps in the process of robotic exploration

    Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning

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    In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice blocks (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 68% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream

    Automatic extraction of potential impact structures from geospatial data : examples from Finnmark, Northern Norway

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    Impact cratering is a fundamental process in the Solar System, and on solid planetary bodies like Mars and the Moon, impact cratering may be the most prominent landforming process. On the Earth several processes compete in shaping the surface. Consequently, the impact structures on Earth are often poorly preserved, difficult to spot and found in limited numbers (per 2010, 176). The impact crater formation process results in a circular shape of fresh craters, except for impacts at low angles. This circularity is found in e.g. morphology, the distribution of impact rocks and in geophysical anomalies. The analytical choice is then to use the circular shape as a feature descriptor in search approaches. This thesis describes techniques applied to automatic extract circular features from appropriate geospatial datasets, i.e. to locate potential impact structures. The data cover parts of Finnmark county, Northern Norway, and include digital elevation models, geophysical potential field data and multispectral images. Remote sensing or image analysis methodologies can only detect potential impact structures, the most promising structures for further field studies. Evidence must later come from sampled rocks. An impact structure search should not be based on a single technique or a single dataset because of the diverse impact crater catalog, but rather a combination of several techniques applied on various data. Unlike previous terrestrial search approaches of purely visual analysis of data or the use of automatic techniques relevant to only a limited set of data, the presented methodology offers a framework to search large regions and several types of data to extract promising structures prior to the visual inspection
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