Terrain Texture Research Articles

Intelligent classification and analysis of regional landforms based on automatic feature selection

AbstractTerrain features are an important basis for realizing high‐precision landform classification, and feature selection is a key step of machine learning and knowledge mining. However, the feature selection process is facing challenges due to the multidimensionality and correlation of multisource terrain feature datasets and factors. Traditional feature selection methods lack enough consideration for the interpretability and transparency of feature factors, but the transparent decision‐making process of feature selection precisely determines the modelling effect and the reliability of model application results. Current research urgently needs to work out the black holes of visual representation during feature selection. In the process of intelligent landform classification, multiple and effective terrain feature is an essential factor in enhancing the performance and generalisation ability of the network. Therefore, we initially selected 40 terrain feature parameters, including basic terrain factors and digital elevation model (DEM) terrain textures, to calculate the feature contribution degree and sort the parameter importance based on the SHapley Additive exPlanations (SHAP) method, then reserved 10%, 20%, 30%, 40% and 50% terrain features in turn for constructing the landform classification dataset. Because the traditional UNet network cannot completely capture abrupt landform features, the convolutional block attention module (CBAM) was integrated into the UNet, and a deep learning model was established for the fine‐grained classification of regional landforms. Considering the calculation rate, even though there are large regional spatial differences and genetic mechanisms, it is appropriate to retain 20% of terrain features for intelligent landform classification. The classification accuracy of typical regions, namely, the Hanzhong Basin, North China Plain, Yunnan–Guizhou Plateau and Tibetan Plateau, reached 98.76%, 97.36%, 96.3% and 92.78%, respectively, and what's more, some accuracies went up to a higher level under other feature combinations. Meanwhile, given the different feature combinations corresponding to regional landform types, the combinative stability and spatial orderliness characteristics were explored to explain the accuracy variation trend.

Visual discomfort factor analysis and modeling for worldwide stereoscopic 3D maps

EarthView3D is the first worldwide orthographic stereoscopic 3D (S3D) map. It presents an accurate presentation of the Earth’s surface and can provide an immersive geospatial infrastructure for the upcoming metaverse ecosystems. This study conducted the first comprehensive assessment of the worldwide S3D maps, investigated the factors of visual discomfort, predicted discomfort level, and provided design recommendations for improving visual experience. Participants rated the S3D map images and reported the reasons for their visual discomfort. General Eye Symptom Questionnaires (GESQs) and Simplified Simulator Sickness Questionnaires (SSSQs) were used to record the development of visual fatigue with time. Six categories of measurable variables presumably related to visual discomfort were proposed based on participants’ reports, IEEE standards, and literature on visual experience. Factor analysis extracted four principal factors, disparity, terrain texture, luminance, and amplitude spectrum, whose contributions to variance were 38%, 23%, 16%, and 11%, respectively. The selected variables and subjective mean rating scores were used to construct a regression model for the prediction of visual discomfort. Performance evaluation Root Mean Square Error (RMSE) was lower than that reported in previous studies on different S3D databases. The results indicated that besides vergence-accommodation conflict and depth cue conflict, perception of Earth terrain’s textures and luminance played an important role in visual discomfort of viewing orthographic S3D maps.

Extracting Terrain Texture Features for Landform Classification Using Wavelet Decomposition

Accurate landform classification is a crucial component of geomorphology. Although extensive classification efforts have been exerted based on the terrain factor, the scale analysis to describe the macro and micro landform features still needs standard measurement. To obtain the appropriate analysis scale of landform structure feature, and then carry out landform classification using the terrain texture, the texture feature is introduced for reflecting landform spatial differentiation and homogeneity. First, applying the ALOS World 3D-30m (AW3D30) DEM and selecting typical landforms of the southwest Tibet Plateau, the discrete wavelet transform (DWT), which acts as the texture feature analysis method, is executed to dissect the multiscale structural features of the terrain texture. Second, through the structural indices of reconstructed texture images, the optimum decomposition scale of DWT is confirmed. Under these circumstances, wavelet coefficients and wavelet energy entropy are extracted as texture features. Finally, the random forest (RF) method is utilized to classify the landform. Results indicate that the texture feature of DWT can achieve higher classification accuracy, which increases by approximately 11.8% compared with the gray co-occurrence matrix (GLCM).

A Possible Predictor of Visual Discomfort of Viewing Stereoscopic 3D Maps: The Imbalance of Disparity Distributions

Stereoscopic 3D (S3D) maps provide an accurate 3D representation of terrain texture for the precise perception of Earth’s surface. Visual discomfort on S3D images primarily comes from accommodation-vergence conflict, which is related to disparity (the distance between two corresponding points in the left and right stereo images). Previous studies have identified that disparity characteristics are related to visual discomfort. However, the relation between disparity characteristics and visual discomfort has not been investigated in orthographic S3D maps. It is unknown whether disparity characteristics are good indicators of visual discomfort regarding S3D maps. This study proposed a new visual discomfort predictor and compared it to the disparity characteristics already existing in the IEEE standard 3333.1.1™-2015. The comparisons indicate that the imbalance index can be a good predictor of visual discomfort regarding S3D maps. The predictor will be used in a personalized computational model to predict visual discomfort.

Modeling regolith thickness in iron formations using machine learning techniques

The Quadrilátero Ferrífero (QF) in Brazil is a region of great economic, social and environmental importance, involving conflicts of interest due to land use and heavily pressured by iron mining and urban sprawl. This is an area of environmental importance due to supporting rupestrian vegetation areas on ferruginous substrates and springs from important watersheds. Thus, studies that can bring more knowledge about this region becomes important to support future decisions based on technical information. We used Random Forests algorithm and several databases to model the regolith thickness of the entire QF region and we also created an individual model to predict regolith thickness in the lithostratigraphic unit Minas Supergroup that contains most of the drillhole samples. The regolith thickness modeled for the QF region presented an average of 125.32 m and R2 of 0.38, and the specific model for the Minas Supergroup also predicted the average regolith thickness of 125.39 m and R2 of 0.39. These values are in accordance with the average regolith thickness (124 m) data from the drillhole database obtained from exploratory programs for iron ore in the QF region. The most important predictive covariates included drainage density, east–west direction, terrain texture and vertical distance from drainage. This study is the first attempt to model the regolith thickness in this important region and the analysis of model uncertainty can orient future studies.

Terrain visualization information integration in agent-based military industrial logistics simulation

Agent-based simulation has become a useful tool in describing military industrial logistics organization behavior to implement military industrial information integration. Considering the specific demands of virtual engagement terrain design, this study present a framework for terrain visualization information integration in agent-based military industrial logistics simulation, which consists of perception access layer, data layer, service layer, and application layer. Focusing on its data source and the data analysis, processing, use, and validation, we propose a novel design approach on an engagement terrain database system for agent-based military industrial logistics simulation, and provide resolutions to actual problems, such as real-time efficient accession of the agent-based simulation system to high-resolution terrain data, management and maintenance of large-scale terrain data, and integrated visual simulation of terrain data. Such an initiative is achieved by presenting specific methods on the data and model designs for terrain relation, terrain analysis application, and terrain texture in terrain visualization application. A case study of the agent-based military industrial logistics simulation verifies the feasibility and effectiveness of the approach.

UAV + BIM: Incorporation of Photogrammetric Techniques in Architectural Projects with Building Information Modeling Versus Classical Work Processes

The current computer technology facilitates the processing of large volumes of information in architectural design teams, in parallel with recent advances in-flight automation in unmanned aerial vehicles (UAVs) along with lower costs, facilitates their use to capture aerial photographs and obtain orthophotographs and 3D models of relief and terrain textures. With these technologies, 3D models can be produced that allow different geometric configurations of the distribution of construction elements on the ground to be analyzed. This article presents the process of implementation in a terrain integrated into the early stages of architectural design. A methodology is proposed that covers the detailed capture of terrain, the relationship with the architectural design environment, and its implementation on the plot. As a novelty, an inverse perspective to the remaining disciplines is presented, from the inside of the object to the outside. The proposed methodology for the use of UAVs integrates terrain capture, generation of the 3D mesh, superimposition of environmental realities and architectural design using building information modeling (BIM) technologies. In addition, it represents the beginning of a line of research on the implementation of the plot and the layout of foundations using UAVs. The results obtained in the study carried out in three different projects comparing traditional technologies with the integration of UAVs + BIM show a clear improvement in the second option. The use of new technologies applied to the execution and control of work not only improves accuracy but also reduces errors and saves time, which undoubtedly indicates significant savings in costs and deviations in the project.

Somatosensory, Light-Driven, Thin-Film Robots Capable of Integrated Perception and Motility.

Living organisms are capable of sensing and responding to their environment through reflex-driven pathways. The grand challenge for mimicking such natural intelligence in miniature robots lies in achieving highly integrated body functionality, actuation, and sensing mechanisms. Here, somatosensory light-driven robots (SLiRs) based on a smart thin-film composite tightly integrating actuation and multisensing are presented. The SLiR subsumes pyro/piezoelectric responses and piezoresistive strain sensation under a photoactuator transducer, enabling simultaneous yet non-interfering perception of its body temperature and actuation deformation states. The compact thin film, when combined with kirigami, facilitates rapid customization of low-profile structures for morphable, mobile, and multiple robotic functionality. For example, an SLiR walker can move forward on different surfaces, while providing feedback on its detailed locomotive gaits and subtle terrain textures, and an SLiR anthropomorphic hand shows bodily senses arising from concerted mechanoreception, thermoreception, proprioception, and photoreception. Untethered operation with an SLiR centipede is also demonstrated, which can execute distinct, localized body functions from directional motility, multisensing, to wireless human and environment interactions. This SLiR, which is capable of integrated perception and motility, offers new opportunities for developing diverse intelligent behaviors in soft robots.

Void Filling of Digital Elevation Models with a Terrain Texture Learning Model Based on Generative Adversarial Networks

Digital elevation models (DEMs) are an important information source for spatial modeling. However, data voids, which commonly exist in regions with rugged topography, result in incomplete DEM products, and thus significantly degrade DEM data quality. Interpolation methods are commonly used to fill voids of small sizes. For large-scale voids, multi-source fusion is an effective solution. Nevertheless, high-quality auxiliary source information is always difficult to retrieve in rugged mountainous areas. Thus, the void filling task is still a challenge. In this paper, we proposed a method based on a deep convolutional generative adversarial network (DCGAN) to address the problem of DEM void filling. A terrain texture generation model (TTGM) was constructed based on the DCGAN framework. Elevation, terrain slope, and relief degree composed the samples in the training set to better depict the terrain textural features of the DEM data. Moreover, the resize-convolution was utilized to replace the traditional deconvolution process to overcome the staircase in the generated data. The TTGM was trained on non-void SRTM (Shuttle Radar Topography Mission) 1-arc-second data patches in mountainous regions collected across the globe. Then, information neighboring the voids was involved in order to infer the latent encoding for the missing areas approximated to the distribution of training data. This was implemented with a loss function composed of pixel-wise, contextual, and perceptual constraints during the reconstruction process. The most appropriate fill surface generated by the TTGM was then employed to fill the voids, and Poisson blending was performed as a postprocessing step. Two models with different input sizes (64 × 64 and 128 × 128 pixels) were trained, so the proposed method can efficiently adapt to different sizes of voids. The experimental results indicate that the proposed method can obtain results with good visual perception and reconstruction accuracy, and is superior to classical interpolation methods.

Virtual Reality in Cartography: Immersive 3D Visualization of the Arctic Clyde Inlet (Canada) Using Digital Elevation Models and Bathymetric Data

Due to rapid technological development, virtual reality (VR) is becoming an accessible and important tool for many applications in science, industry, and economy. Being immersed in a 3D environment offers numerous advantages especially for the presentation of geographical data that is usually depicted in 2D maps or pseudo 3D models on the monitor screen. This study investigated advantages, limitations, and possible applications for immersive and intuitive 3D terrain visualizations in VR. Additionally, in view of ever-increasing data volumes, this study developed a workflow to present large scale terrain datasets in VR for current mid-end computers. The developed immersive VR application depicts the Arctic fjord Clyde Inlet in its 160 km × 80 km dimensions at 5 m spatial resolution. Techniques, such as level of detail algorithms, tiling, and level streaming, were applied to run the more than one gigabyte large dataset at an acceptable frame rate. The immersive VR application offered the possibility to explore the terrain with or without water surface by various modes of locomotion. Terrain textures could also be altered and measurements conducted to receive necessary information for further terrain analysis. The potential of VR was assessed in a user survey of persons from six different professions.

Stability analysis unit and spatial distribution pattern of the terrain texture in the northern Shaanxi Loess Plateau

Terrain texture analysis is an important method of digital terrain analysis in quantitative geomorphological research and in the exploration of the spatial heterogeneity and autocorrelation of terrain features. However, a major issue often neglected in previous studies is the calculation unit of the terrain texture, that is, the stability analysis unit. As the test size increases, the derived terrain textures become increasingly similar so that their differences can be ignored. The test size of terrain texture is defined as the stability analysis unit. This study randomly selected 48 areas within the Loess Plateau in northern Shaanxi in China as the study sites and used the gray level co-occurrence matrix to calculate the terrain texture. The stability analysis unit of the terrain texture was then extracted, and its spatial distribution pattern in the Loess Plateau was studied using spatial interpolation method. Four terrain texture metrics, i.e., homogeneity, energy, correlation, and contrast, were extracted on the basis of the stability analysis unit, and the spatial variation patterns of these parameters were studied. Results showed that the spatial distribution pattern and the terrain texture metrics reflected a trend of high–low–high from north to south, which correlated with the spatial distribution of the landforms at the Loess Plateau. In addition, the terrain texture measures was significantly correlated with the terrain factors of gully density and slope, and this relationship showed that terrain texture measures based on the stability analysis unit could reflect the basic characteristics of terrain morphology. The stability analysis unit provided a reasonable analytical scale for terrain texture analysis and could be used as a measure of the regional topography to accurately describe basic terrain characteristics.

Drainage basin object-based method for regional-scale landform classification: a case study of loess area in China

Landform classification is one of the most important procedures in recognizing and dividing earth surface landforms. However, topographical homogeneity and differences in regional-scale landforms are often ignored by traditional pixel- and object-based landform classification methods based on digital elevation models (DEMs). In this work, a drainage basin object-based method for classifying regional-scale landforms is proposed. Drainage basins with least critical areas are first delineated from DEMs. Then, terrain derivatives of mean elevation, mean slope, drainage density, drainage depth, and terrain texture are employed to characterize the morphology of the drainage basins. Finally, a decision tree based on the topographical characteristics of the drainage basins is constructed and employed to determine the landform classification law. The experiment is validated in the landform classification of regional-scale loess areas in China. Results show that clear boundaries exist in different landforms at the regional scale. Landform type in a specific region shows significant topographical homogeneity under its specific regional geomorphological process. Classification accuracies are 87.3 and 86.3% for the field investigation and model validation, respectively. Spatial patterns of classified landforms and their proximity to sediment sources and other factors can be regarded as indicators of the evolutionary process of loess landform formation.

3D geospatial visualizations: Animation and motion effects on spatial objects

Digital Elevation Models (DEMs), in combination with high quality raster graphics provide realistic three-dimensional (3D) representations of the globe (virtual globe) and amazing navigation experience over the terrain through earth browsers. In addition, the adoption of interoperable geospatial mark-up languages (e.g. KML) and open programming libraries (Javascript) makes it also possible to create 3D spatial objects and convey on them the sensation of any type of texture by utilizing open 3D representation models (e.g. Collada). One step beyond, by employing WebGL frameworks (e.g. Cesium.js, three.js) animation and motion effects are attributed on 3D models. However, major GIS-based functionalities in combination with all the above mentioned visualization capabilities such as for example animation effects on selected areas of the terrain texture (e.g. sea waves) as well as motion effects on 3D objects moving in dynamically defined georeferenced terrain paths (e.g. the motion of an animal over a hill, or of a big fish in an ocean etc.) are not widely supported at least by open geospatial applications or development frameworks. Towards this we developed and made available to the research community, an open geospatial software application prototype that provides high level capabilities for dynamically creating user defined virtual geospatial worlds populated by selected animated and moving 3D models on user specified locations, paths and areas. At the same time, the generated code may enhance existing open visualization frameworks and programming libraries dealing with 3D simulations, with the geospatial aspect of a virtual world.

Santa Maria di Leuca Province (Mediterranean Sea): Identification of Suitable Mounds for Cold-Water Coral Settlement Using Geomorphometric Proxies and Maxent Methods

The Santa Maria di Leuca (SML) cold-water coral province (northern Ionian Sea) has the largest occurrence of a living white coral community currently known in the Mediterranean Sea. Madrepora oculata and Lophelia pertusa, identified as marking sensitive habitats of relevance by the General Fisheries Commission for the Mediterranean, have been observed heterogeneously distributed on the summits of several mounds. This particularly patchy and uneven distribution in addition to their importance for regional biodiversity highlights the need to better understand their environmental preferences and predict their distribution. Bathymetric data (40m resolution) was used to derive the seafloor characteristics. A fine scale index quantifying the landscape elevation (Bathymetric Position Index at 120m resolution) was used to select all the elevated features considered as candidate morphologies for potential coral mounds. Statistics on 22 known coral topped mounds were computed. Two statistical methods were then used to identify other potential coral mounds based on predictive variables. The first method, the Geomorphometric proxies method, consists in computing basic statistics of terrain variables, using them for a step-by-step classification in a quantitative approach to select a subset of candidate morphologies. The second method consists in using a predictive Habitat Suitability Model (Maxent model). The Geomorphometric proxies method identified 736 potential coral mounds while the Maxent method predicted 1252 potential coral mounds. A subset of 517 potential coral mounds was common to both methods. The analysis of the contribution of each variable with the Maxent method showed that the variable "Vector Ruggedness Measure" at a resolution of 5 pixels (200 m) contributed to 53% of the final Maxent model, followed by the "Terrain Texture" index (31%) at a resolution of 11 pixels (440 m). The common potential coral mounds are mainly located in an area characterized by a mass transport deposit, also called the mounds area because of the roughness of the seafloor, in accordance with the high proportional contribution of the noticeable first roughness index to the Maxent model. The results highlight the importance of the global conservation of the entire Province, with white coral probably widespread over the entire 600 km² SML area.

Regional topographic classification in the North Shaanxi Loess Plateau based on catchment boundary profiles

Topographic classification and mapping are fundamental topics in geomorphology and have many promising applications. This study aimed to achieve landform classification using the catchment boundary profile (CBP) in the area of the North Shaanxi Province. CBPs were extracted, and seven indices were proposed and calculated to describe the profile features. Spatial maps of these indices were generated by interpolating 81 sample areas in the North Shaanxi Province of China. Finally, a topographic regional map was generated through multi-resolution segmentation after integrating the spatial maps of the seven indices. Results showed that the CBP indices were typical and representative to describe the terrain characteristics in the North Shaanxi Province and revealed their diverse spatial patterns. The investigated area was classified into 14 topographic units. The requirements of maximizing internal homogeneity while minimizing external homogeneity in terms of morphological characteristics were satisfied through the analysis of the terrain texture, topographic features, and land surface parameters. A comparison with the reference map showed that the regional classification differed from the geomorphologic map, given the different classification systems. However, the regional classification provided new knowledge of topographic features and spatial patterns in the macro scale. A comparison with other classification method indicated the advantage of the CBP-based method, which could distinguish the landforms of loess tableland, loess ridge, and loess hill that are the typical characteristics of loess landforms. The sensitivity analysis revealed that the differences in CBP indices, except for roughness and fractal dimension (FD) from different landforms, would increase with the digital elevation model (DEM) resolution becoming coarser, which indicates that the CBP-based method could be applied on a coarser resolution DEM if the differences of roughness and FD from different landforms could be identified at this coarse DEM level.

A robust interpolation method for constructing digital elevation models from remote sensing data

A digital elevation model (DEM) derived from remote sensing data often suffers from outliers due to various reasons such as the physical limitation of sensors and low contrast of terrain textures. In order to reduce the effect of outliers on DEM construction, a robust algorithm of multiquadric (MQ) methodology based on M-estimators (MQ-M) was proposed. MQ-M adopts an adaptive weight function with three-parts. The weight function is null for large errors, one for small errors and quadric for others. A mathematical surface was employed to comparatively analyze the robustness of MQ-M, and its performance was compared with those of the classical MQ and a recently developed robust MQ method based on least absolute deviation (MQ-L). Numerical tests show that MQ-M is comparative to the classical MQ and superior to MQ-L when sample points follow normal and Laplace distributions, and under the presence of outliers the former is more accurate than the latter. A real-world example of DEM construction using stereo images indicates that compared with the classical interpolation methods, such as natural neighbor (NN), ordinary kriging (OK), ANUDEM, MQ-L and MQ, MQ-M has a better ability of preserving subtle terrain features. MQ-M replaces thin plate spline for reference DEM construction to assess the contribution to our recently developed multiresolution hierarchical classification method (MHC). Classifying the 15 groups of benchmark datasets provided by the ISPRS Commission demonstrates that MQ-M-based MHC is more accurate than MQ-L-based and TPS-based MHCs. MQ-M has high potential for DEM construction.

Stable Boundary Layer in Complex Terrain. Part II: Geometrical and Sheltering Effects on Mixing

AbstractThe authors examine how terrain texture and topography influence nocturnal mixing rates. Local topographic curvature and site sheltering exhibit systematic influences on nocturnal heat and momentum fluxes and the near-surface potential temperature distribution. This influence is particularly evident in hilly terrain with patchy forested areas, typical of eastern North America and many other regions. Exposure to local obstacles, quantified using Fujita’s “transmission factor,” has its maximum influence on mixing during strong winds (>5 m s−1), whereas the effects of local terrain curvature dominate under weaker winds. Such complementary dominance conditions currently limit direct comparison of the two effects. Even with a limited network of 10 stations, it is clear that preferred regions for mixing can be identified in advance given knowledge of land cover and topography. When designing a network of surface stations to be deployed in heterogeneous terrain, one should consider site curvature, slope, and exposure in addition to spatial coverage.

웨이블릿 변환으로 압축된 지형 데이터의 효율적인 실시간 렌더링 기법

고해상도의 지형 데이터는 용량이 크기 때문에 GPU메모리에 데이터 전체를 적재할 수 없다. 따라서 out-of-core기반의 방법이 많이 사용된다. 그러나 보조기억장치의 대역폭 한계로 인하여 실시간으로 지형을 렌더링하기 어렵기 때문에 GPU로 웨이블릿 변환을 수행하여 압축된 DEM 데이터를 전송한 후 압축 해제하여 렌더링 하는 방법이 사용된다. 하지만 이 방법은 텍스처로부터 주기적으로 값을 읽어와 정점을 변환하고 메쉬를 생성해야하므로 비효율적이다. 이 논문에서는 웨이블릿 압축된 근사 계수 값을 정점의 속성으로 저장하고 기하 쉐이더에서 압축을 해제해 지형을 효율적으로 렌더링 하는 기법을 제안한다. 제안하는 방법은 근사 계수 값을 정점의 속성으로 주어 지형 텍스처의 전송량을 줄일 수 있다. 또한 지형 텍스처로부터 별도의 업로드 과정 없이 메쉬의 생성이 가능하므로 오버헤드가 발생하지 않아 효율적인 렌더링이 가능하다.

Image Overlapping Technique without Model for Large LoD Terrain Based on GPU

There are many difficulties in image overlapping in 3D terrain of dynamic LoD quad tree model, it will cause gaps between terrain tiles and images if both of them are expressed independently. This paper breaks through the LoD mesh difficulties, and realizes image overlapping based on GPU programming without modeling. It establishes different texture coordinate transformational relationships in GPU to realize seamless expression between terrain texture and overlapping image based on setting terrain vertexes with global unified texture coordinate. The affine transformation is adopted to solve the difficulty of multiple spatial nonlinear transformations aiming to the interaction of image overlapping, and also to solve the non-synchronous problem of texture coordinate in interaction. At last, the conclusion may be achieved. Image overlapping technique in LoD terrain without modeling based on GPU is a typical case of render technology to solve application difficulties, the realization is simple, efficient and steady which only faces to single terrain tile. This research will greatly enhance the ability of Digital Earth, which completely clear an important technical barriers and promote the applications in GeoDesign.

Chang’E-3 scientific payloads and its checkout results

The scientific objectives of ChangE-3 mission were to: examine the texture, mineralogy, and structure of the local lunar terrain; determine the distribution and composition of minerals, rocks, and soils surrounding the landing sites; image the Earths plasmasphere and operate an ultraviolet astronomy camera on the moon. Four kinds of scientific instruments are chosen as the payloads for ChangE-3 lander, which include topography camera, lunar optical telescope, extreme ultraviolet imager and landing camera so on. And other four kinds of scientific instruments are chosen as the payloads for lunar rover, which include panoramic cameras, lunar radar, IR acousto-optic spectrometer, X-ray spectrometer and so on. This paper mainly introduces scientific exploration mission, the scientific payloads design and construction. The checkout results are also described in detail.