Probing Surface Information of Alloy by Time of Flight-Secondary Ion Mass Spectrometer

In recent years, time of flight-secondary ion mass spectrometer (ToF-SIMS) has been widely employed to acquire surface information of materials. Here, we investigated the alloy surface by combining the mass spectra and 2D mapping images of ToF-SIMS. We found by surprise that these two results seem to be inconsistent with each other. Therefore, other surface characteristic tools such as SEM-EDS were further used to provide additional supports. The results indicated that such differences may originate from the variance of secondary ion yields, which might be affected by crystal orientation.

A proof of concept for machine learning-based virtual knapping using neural networks

Prehistoric stone tools are an important source of evidence for the study of human behavioural and cognitive evolution. Archaeologists use insights from the experimental replication of lithics to understand phenomena such as the behaviours and cognitive capacities required to manufacture them. However, such experiments can require large amounts of time and raw materials, and achieving sufficient control of key variables can be difficult. A computer program able to accurately simulate stone tool production would make lithic experimentation faster, more accessible, reproducible, less biased, and may lead to reliable insights into the factors that structure the archaeological record. We present here a proof of concept for a machine learning-based virtual knapping framework capable of quickly and accurately predicting flake removals from 3D cores using a conditional adversarial neural network (CGAN). We programmatically generated a testing dataset of standardised 3D cores with flakes knapped from them. After training, the CGAN accurately predicted the length, volume, width, and shape of these flake removals using the intact core surface information alone. This demonstrates the feasibility of machine learning for investigating lithic production virtually. With a larger training sample and validation against archaeological data, virtual knapping could enable fast, cheap, and highly-reproducible virtual lithic experimentation.

GPR Detection of Fossil Structures in Conductive Media Supported by FDTD Modelling and Attributes Analysis: An Example from Early Pleistocene Marine Clay at Bargiano Site (Central Italy)

The application of Ground Penetrating Radar (GPR) prospecting to the search of fossil structures, particularly using advanced techniques like Finite-difference time-domain (FDTD) modelling and GPR attribute analysis, is currently poorly exploited in paleontology. Here, we promote the use of such a GPR workflow at Bargiano (Umbria, central Italy), a unique paleontological site known for the discovery of cetacean skeletons, dolomitized sperm-whale cololites (Ambergrisichnu salleronae), and layered fossil assemblages. The study site is characterized by a very uneven topography shaping highly conductive clayey deposits, representing not exactly ideal conditions for GPR surveying. After generating models encompassing a real topography and variable electrical properties of media, we simulated buried fossil structures at variable depth with different size and geometry, using different operative frequencies. After obtaining information on the characteristics of reflections, investigation depth, and detectability, we provide a comparison with experimental data, also used to compute instantaneous amplitude and phase attributes. Upon depicting a peculiar GPR signature for our targets, we discuss the results in light of ground-truthing performed through trenching. Our workflow allowed us to restrict the excavation areas, extending the surface information in depth in a non-invasive way, and optimizing the field operations, necessary for the preservation of the study site.

Daytime Cloud Detection Algorithm Based on a Multitemporal Dataset for GK-2A Imagery

Cloud detection is an essential and important process in remote sensing when surface information is required for various fields. For this reason, we developed a daytime cloud detection algorithm for GEOstationary KOrea Multi-Purpose SATellite 2A (GEO-KOMPSAT-2A, GK-2A) imagery. For each pixel, the filtering technique using angular variance, which denotes the change in top of atmosphere (TOA) reflectance over time, was applied, and filtering technique by using the minimum TOA reflectance was used to remove remaining cloud pixels. Furthermore, near-infrared (NIR) and normalized difference vegetation index (NDVI) images were applied with dynamic thresholds to improve the accuracy of the cloud detection results. The quantitative results showed that the overall accuracy of proposed cloud detection was 0.88 and 0.92 with Visible Infrared Imaging Radiometer Suite (VIIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), respectively, and indicated that the proposed algorithm has good performance in detecting clouds.

Feature Matching Combining Radiometric and Geometric Characteristics of Images, Applied to Oblique- and Nadir-Looking Visible and TIR Sensors of UAV Imagery

A large amount of information needs to be identified and produced during the process of promoting projects of interest. Thermal infrared (TIR) images are extensively used because they can provide information that cannot be extracted from visible images. In particular, TIR oblique images facilitate the acquisition of information of a building’s facade that is challenging to obtain from a nadir image. When a TIR oblique image and the 3D information acquired from conventional visible nadir imagery are combined, a great synergy for identifying surface information can be created. However, it is an onerous task to match common points in the images. In this study, a robust matching method of image pairs combined with different wavelengths and geometries (i.e., visible nadir-looking vs. TIR oblique, and visible oblique vs. TIR nadir-looking) is proposed. Three main processes of phase congruency, histogram matching, and Image Matching by Affine Simulation (IMAS) were adjusted to accommodate the radiometric and geometric differences of matched image pairs. The method was applied to Unmanned Aerial Vehicle (UAV) images of building and non-building areas. The results were compared with frequently used matching techniques, such as scale-invariant feature transform (SIFT), speeded-up robust features (SURF), synthetic aperture radar–SIFT (SAR–SIFT), and Affine SIFT (ASIFT). The method outperforms other matching methods in root mean square error (RMSE) and matching performance (matched and not matched). The proposed method is believed to be a reliable solution for pinpointing surface information through image matching with different geometries obtained via TIR and visible sensors.

Surface representation impacts on turbulent heat fluxes in the Weather Research and Forecasting (WRF) model (v.4.1.3)

The water and energy transfers at the interface between the Earth's surface and the atmosphere should be correctly simulated in numerical weather and climate models. This implies the need for a realistic and accurate representation of land cover (LC), including appropriate parameters for each vegetation type. In some cases, the lack of information and crude representation of the surface lead to errors in the simulation of soil and atmospheric variables. This work investigates the ability of the Weather Research and Forecasting (WRF) model to simulate surface heat fluxes in a heterogeneous area of southern France using several possibilities for the surface representation. In the control experiments, we used the default LC database in WRF, which differed significantly from the actual LC. In addition, sub-grid variability was not taken into account since the model uses, by default, only the surface information from the dominant LC category in each pixel (dominant approach). To improve this surface simplification, we designed three new interconnected numerical experiments with three widely used land surface models (LSMs) in WRF. The first one consisted of using a more realistic and higher-resolution LC dataset over the area of analysis. The second experiment aimed at investigating the effect of using a mosaic approach; 30 m sub-grid surface information was used to calculate the final grid fluxes based on weighted averages from values obtained for each LC category. Finally, in the third experiment, we increased the model stomatal conductance for conifer forests due to the large flux errors associated with this vegetation type in some LSMs. The simulations were evaluated with gridded area-averaged fluxes calculated from five tower measurements obtained during the Boundary-Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign. The results from the experiments differed depending on the LSM and displayed a high dependency of the simulated fluxes on the specific LC definition within the grid cell, an effect that was enhanced with the dominant approach. The simulation of the fluxes improved using the more realistic LC dataset except for the LSMs that included extreme surface parameters for coniferous forest. The mosaic approach produced fluxes more similar to reality and served to particularly improve the latent heat flux simulation of each grid cell. Therefore, our findings stress the need to include an accurate surface representation in the model, including soil and vegetation sub-grid information with updated surface parameters for some vegetation types, as well as seasonal and man-made changes. This will improve the modelled heat fluxes and ultimately yield more realistic atmospheric processes in the model.

Sensitivity of biomass burning emissions estimates to land surface information

Emissions from biomass burning (BB) are a key source of atmospheric tracer gases that affect the atmospheric carbon cycle. We estimated four types of global BB emissions using a bottom-up approach and by combining the remote sensing products related to fire distribution with two aboveground biomass (AGB) and two land cover classification (LCC) distributions. The sensitivity of the estimates of BB emissions to the AGB and LCC data was evaluated using the carbon monoxide (CO) emissions associated with each BB estimate. We found a substantial spatial difference in CO emissions for both the AGB and LCC data, which resulted in a large (factor of approximately three) spread of estimates for the mean annual CO emissions. We simulated atmospheric CO variability using an atmospheric tracer transport model and the BB emissions estimates and compared it with ground-based and satellite observations. At ground-based observation sites during fire seasons, statistical comparisons indicated that the impact of differences in the BB emissions estimates on atmospheric CO variability was poorly defined in our simulations. However, when compared at the regional and global scales, the distribution of atmospheric CO concentrations in the simulations show substantial differences among the estimates of BB emissions. These results indicate that the estimates of BB emissions are highly sensitive to the AGB and LCC data.

A Review of Remote Sensing Image Dehazing

Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated.

A Combined Unmixing Framework for Impervious Surface Mapping on Medium-Resolution Images with Visible Shadows

Spectral unmixing methods with medium-resolution remote sensing images have become the main approach to mapping urban impervious-surface information. However, as more tall buildings appear, numerous visible shadows exist in medium-resolution images; these have usually been ignored in previous research, but they seriously affect accuracy. To solve this problem, we propose a combined unmixing framework to extract impervious surface in nonshadow and shadow areas, using linear and nonlinear unmixing models, respectively. First shadow is separated from nonshadow. Then a nonlinear unmixing method is selected to map impervious surface in shadow, which is more suitable to the complex imaging environment in shadow, and a classic linear unmixing model in nonshadow. Through experimental tests, the proposed combined unmixing framework is shown to effectively reduce error in two study areas compared with classical unmixing methods.

Evaluation Model of Parking Equipment Planning and Design Based on Object-Oriented Technology

Stereo parking equipment has become an important means to solve the problem of parking difficulties, so it is necessary to study the planning of stereo parking equipment. This paper proposes an evaluation model for parking equipment planning and design, and verifies the feasibility of the model through an example. First, obtain the surface information of the planned area through object-oriented technology, and then complete the design layout of the area that can accommodate the most parking spaces according to the plan information map of the study area. Next, calculate the number of parking spaces required for each building in the area, and the number of available parking spaces within the maximum acceptable time for each building. Finally, compare the two to design the number and location of parking equipment. This method can quickly and accurately obtain the ground plane information map of the study area, while ensuring the capacity of parking spaces to meet the needs of users, it also improves the rationality and suitability of the planning and layout of stereo parking equipment, which can effectively guide the planning and construction of urban parking equipment.