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NASA NTRS1995

SAVS: A Space and Atmospheric Visualization Science system

The research environment faced by space and atmospheric scientists in the 1990s is characterized by unprecedented volumes of new data, by ever-increasing repositories of unexploited mission files, and by the widespread use of empirical and large-scale computational models needed for the synthesis of understanding across data sets and discipline boundaries. The effective analysis and interpretation of such massive amounts of information have become the subjects of legitimate concern. With SAVS (a Space and Atmospheric Visualization Science System), we address these issues by creating a 'push-button' software environment that mimics the logical scientific processes in data acquisition, reduction, and analysis without requiring a detailed understanding of the methods, networks, and modules that link the tools and effectively execute the functions. SAVS provides (1) a customizable framework for accessing a powerful set of visualization tools based on the popular AVS visualization software with hooks to PV-Wave and access to Khoros modules, (2) a set of mathematical and statistical tools, (3) an extensible library of discipline-specific functions and models (e.g., MSIS, IRI, Feldstein Oval, IGRF, satellite tracking with CADRE-3, etc.), and (4) capabilities for local and remote data base access. The system treats scalar, vector, and image data, and runs on most common Unix workstations. We present a description of SAVS and its components, followed by several applications based on generic research interests in interplanetary and magnetospheric physics (IMP/ISTP), active experiments in space (CRRES), and mission planning focused on the Earth's thermospheric, ionospheric, and mesospheric domains (TIMED).

Szuszczewicz, E. P.↗

NASA NTRS2014

Stereoscopic Retrieval of Smoke Plume Heights and Motion from Space-Based Multi-Angle Imaging, Using the MISR INteractive eXplorer(MINX)

Airborne particles desert dust, wildfire smoke, volcanic effluent, urban pollution affect Earth's climate as well as air quality and health. They are found in the atmosphere all over the planet, but vary immensely in amount and properties with season and location. Most aerosol particles are injected into the near-surface boundary layer, but some, especially wildfire smoke, desert dust and volcanic ash, can be injected higher into the atmosphere, where they can stay aloft longer, travel farther, produce larger climate effects, and possibly affect human and ecosystem health far downwind. So monitoring aerosol injection height globally can make important contributions to climate science and air quality studies. The Multi-angle Imaging Spectro-Radiometer (MISR) is a space borne instrument designed to study Earths clouds, aerosols, and surface. Since late February 2000 it has been retrieving aerosol particle amount and properties, as well as cloud height and wind data, globally, about once per week. The MINX visualization and analysis tool complements the operational MISR data products, enabling users to retrieve heights and winds locally for detailed studies of smoke plumes, at higher spatial resolution and with greater precision than the operational product and other space-based, passive remote sensing techniques. MINX software is being used to provide plume height statistics for climatological studies as well as to investigate the dynamics of individual plumes, and to provide parameterizations for climate modeling.

aerosols↗

NASA NTRS1982

A statistical model for radar images of agricultural scenes

The presently derived and validated statistical model for radar images containing many different homogeneous fields predicts the probability density functions of radar images of entire agricultural scenes, thereby allowing histograms of large scenes composed of a variety of crops to be described. Seasat-A SAR images of agricultural scenes are accurately predicted by the model on the basis of three assumptions: each field has the same SNR, all target classes cover approximately the same area, and the true reflectivity characterizing each individual target class is a uniformly distributed random variable. The model is expected to be useful in the design of data processing algorithms and for scene analysis using radar images.

Frost, V. S.↗

NASA NTRS2001

Boiler Tube Corrosion Characterization with a Scanning Thermal Line

Wall thinning due to corrosion in utility boiler water wall tubing is a significant operational concern for boiler operators. Historically, conventional ultrasonics has been used for inspection of these tubes. Unfortunately, ultrasonic inspection is very manpower intense and slow. Therefore, thickness measurements are typically taken over a relatively small percentage of the total boiler wall and statistical analysis is used to determine the overall condition of the boiler tubing. Other inspection techniques, such as electromagnetic acoustic transducer (EMAT), have recently been evaluated, however they provide only a qualitative evaluation - identifying areas or spots where corrosion has significantly reduced the wall thickness. NASA Langley Research Center, in cooperation with ThermTech Services, has developed a thermal NDE technique designed to quantitatively measure the wall thickness and thus determine the amount of material thinning present in steel boiler tubing. The technique involves the movement of a thermal line source across the outer surface of the tubing followed by an infrared imager at a fixed distance behind the line source. Quantitative images of the material loss due to corrosion are reconstructed from measurements of the induced surface temperature variations. This paper will present a discussion of the development of the thermal imaging system as well as the techniques used to reconstruct images of flaws. The application of the thermal line source coupled with the analysis technique represents a significant improvement in the inspection speed and accuracy for large structures such as boiler water walls. A theoretical basis for the technique will be presented to establish the quantitative nature of the technique. Further, a dynamic calibration system will be presented for the technique that allows the extraction of thickness information from the temperature data. Additionally, the results of the application of this technology to actual water wall tubing samples and in-situ inspections will be presented.

Cramer, K. Elliott↗

NASA NTRS2011

Monitoring Areal Snow Cover Using NASA Satellite Imagery

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

Harshburger, Brian J.↗

NASA NTRS2014

Use of MODIS Cloud Top Pressure to Improve Assimilation Yields of AIRS Radiances in GSI

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA's Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Previously, it has been shown that cloud top designation associated with quality control procedures within the Gridpoint Statistical Interpolation (GSI) system used operationally by a number of Joint Center for Satellite Data Assimilation (JCSDA) partners may not provide the best representation of cloud top pressure (CTP). Because this designated CTP determines which channels are cloud-free and, thus, available for assimilation, ensuring the most accurate representation of this value is imperative to obtaining the greatest impact from satellite radiances. This paper examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing analysis increments and numerical forecasts generated using operational techniques with a research technique that swaps CTP from the Moderate-resolution Imaging Spectroradiometer (MODIS) for the value of CTP calculated from the radiances within GSI.

Zavodsky, Bradley↗

NASA NTRS2019

Impact of GMI All-Sky Radiance Assimilation in the NASA GEOS Forecast System

The assimilation of cloud- and precipitation-affected ("all-sky") radiances has become an important focus of development at most numerical weather prediction centers. Efforts at the Global Modeling and Assimilation Office (GMAO) have focused on all-sky assimilation of GPM Microwave Imager (GMI) radiances, which became operational in the GEOS real-time production system in July 2018. Implementation of the all-sky capability required several upgrades to the GEOS hybrid 4D-EnVar assimilation infrastructure including the addition of control variables for cloud liquid, cloud ice, rain and snow, enhancements to the radiative transfer model, new hybrid background and observational error models, and modified quality control and bias correction procedures. This talk describes the impact of GMI all-sky radiance assimilation on GEOS analyses and forecasts as determined from examination of various metrics including statistics of background departures and analysis increments, forecast skill scores, and forecast sensitivity observation impact (FSOI) calculations. It is shown that in addition to the hydrometeors themselves, the initial wind, temperature and pressure fields all undergo significant dynamic adjustment in response to the analyzed cloud and precipitation features. Assimilation of GMI radiances leads to improved forecasts of lower tropospheric wind, temperature and humidity, especially in the tropics. The largest forecast improvements occur during the first 48 hours, with diminishing impact thereafter. However, combining GMI all-sky assimilation with improvements to the GEOS model physics as in the recent implementation of the real-time production system, extends these forecast improvements well in to the medium range. FSOI results based on a 24-hr moist global energy norm show that GMI radiances provide nearly uniform beneficial impact throughout the tropics, with more mixed impacts in the subtropics. While the overall impact of GMI is smaller than that of other, much more numerous microwave and hyperspectral infrared radiance types, its impact is among the largest of all radiance types on a per-observation basis.

Gelaro, Ron↗

NASA NTRS2022

Cross‐Instrument Comparison of MapCam and OVIRS on OSIRIS‐REx

Two of the instruments onboard the OSIRIS-REx spacecraft, the MapCam color imager and the OVIRS visible and 20 infrared spectrometer, observed the surface of asteroid (101955) Bennu in partially overlapping wavelengths. 21 Significant scientific advances have been enabled by using data from these two instruments in tandem, but a robust 22 statistical understanding of their relationship is needed for future analyses to cross-compare their data as accurately 23 and sensitively as possible. Here we present a cross-instrument comparison of data acquired by MapCam and 24 OVIRS, including methods and results for all global and site-specific observation campaigns in which both 25 instruments were active. In our analysis, we consider both the absolute radiometric offset and the relative 26 (normalized) variation between the two instruments; we find that both depend strongly on the photometric and 27 instrumental conditions during the observation. The two instruments have a large absolute offset (>15%) due to their 28 independent radiometric calibrations. However, they are very consistent (relative offset as low as 1%) when each 29 instrument’s response is normalized at a single wavelength, particularly at low phase angles where shadows on 30 Bennu’s rough surface are minimized. We recommend using the global datasets acquired at 12:30 pm local solar 31 time for cross-comparisons; data acquired at higher phase angles have larger uncertainties.

D R Golish↗

DOE OSTI2023

Quantifying Pore Morphology in Spray-Formed Tantalum Using X-ray Micro-computed Tomography

In order to establish quantitative process–structure–property relationships in thermal spray coatings, a robust framework for defining (micro)structural characteristics is needed. Here, we present a quantitative characterization of the three-dimensional morphology of porosity in spray-formed tantalum samples based on high-resolution X-ray micro-computed tomography. Using synchrotron facilities, we acquired dozens of high-resolution scans, enabling a statistically meaningful comparison across multiple samples, different regions within samples, and spray processes. We quantify the spatial distribution, size, and topology of porous inclusions, with a significant focus on variability across samples and different spray processes (plasma and cold sprayed), as well as sensitivity to image segmentation and resolution. Based on a typical segmentation, we report porosities ranging from 0.9 to 1.7 pct for all samples tested, with significant sensitivity due to image segmentation resulting in estimates as low as 0.6 pct and as high as 4.8 pct. For the complex pore space morphology observed in these materials, we argue that a conventional analysis based on identifying individual pores is not well suited, and propose an alternative approach based on morphological metrics with a rich history in porous media literature, such as spatial correlations, local pore thickness, and scale-dependent sub-sampling. Spatial correlations indicate anisotropic splat structures, but only mildly anisotropic pores. Various measures of pore size show a wide distribution of sizes, ranging from sub-micron to 10-micron length scales. Scale-dependent variations in porosity suggest that representative volumes of several hundred microns are required for convergence of morphological metrics, with larger volumes for cold-spray materials. This work provides a robust quantitative basis for describing three-dimensional pore structure in thermal spray coatings.

36 MATERIALS SCIENCE↗

DOE OSTI2017

MALDI Orbitrap Mass Spectrometry Profiling of Dysregulated Sulfoglycosphingolipids in Renal Cell Carcinoma Tissues

Matrix-assisted laser desorption/ionization coupled with Orbitrap mass spectrometry (MALDI-Orbitrap-MS) is used for the clinical study of patients with renal cell carcinoma (RCC), as the most common type of kidney cancer. Significant changes in sulfoglycosphingolipid abundances between tumor and autologous normal kidney tissues are observed. First, sulfoglycosphingolipid species in studied RCC samples are identified using high mass accuracy full scan and tandem mass spectra. Subsequently, optimization, method validation, and statistical evaluation of MALDI-MS data for 158 tissues of 80 patients are discussed. More than 120 sulfoglycosphingolipids containing one to five hexosyl units are identified in human RCC samples based on the systematic study of their fragmentation behavior. Many of them are recorded here for the first time. Multivariate data analysis (MDA) methods, i.e., unsupervised principal component analysis (PCA) and supervised orthogonal partial least square discriminant analysis (OPLS-DA), are used for the visualization of differences between normal and tumor samples to reveal the most up- and downregulated lipids in tumor tissues. Obtained results are closely correlated with MALDI mass spectrometry imaging (MSI) and histologic staining. Important steps of the present MALDI-Orbitrap-MS approach are also discussed, such as the selection of best matrix, correct normalization, validation for semiquantitative study, and problems with possible isobaric interferences on closed masses in full scan mass spectra. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1987

Remote measurement of in-plane diffusivity components in plates

A method of determining thermal diffusivity in thin plates is presented. The method, using infrared images of evolving thermal patterns previously injected with a laser, is noncontacting, one-sided, and remote. It does not require independent estimates of either the emissivity of the sample or the sample thickness. With a line-segment pattern for thermal input, it yields the inplane components of the diffusivity tensor in anisotropic materials and also the rate of heat loss to the environment of the plate. Two methods of data analysis are presented, one corresponding to a heating line of general cross section and the other considering a Gaussian cross section, thereby saving considerable computer time. Both methods produce a statistical evaluation of measurement quality as well as estimates of diffusivity and loss rate. Results are shown for plates of metals and graphite-epoxy composite materials. Principal components and orientation for the diffusivity tensor are obtained in the anisotropic graphite-epoxy sample.

Welch, Christopher S.↗

NASA NTRS1999

Tropical Cirrus Physical and Radiation Characteristics from the NASA Aircraft Observations in Toga/Coare

During the Toga/Coare experiment in the tropical west Pacific, a cloud radiation experiment involving measurement from the NASA ER-2 high altitude aircraft and DC-8 airborne laboratory was performed. Observations include multispectral visible/IR and microwave radiometric imaging, active lidar profiling and radiation flux measurements. On a number of missions coordinated flights were made for simultaneous cloud observations with the ER-2 remote sensing and DC-8 below cloud and in cloud measurements. Since the field experiment there has been systematic analysis of the observations. Intercomparison of retrieved measurements will be presented. These include the relation between visible bi-directional reflectivity and infrared emissivity of tropical cirrus. Upper troposphere microwave water vapor retrievals and cirrus optical parameters have been correlated. The relation observed short wave flux divergence to retrieved cloud thickness and type has been studied. Calculations and statistics for the short and long wave heating profiles of observed clouds have been derived. A summary of key results will be presented.

Spinhirne, James D.↗

NASA NTRSDate not supplied

Anomaly Detection for the Roman Space Telescope Wide Field Instrument’s Science Data Processing Pipeline

The Roman Space Telescope (RST) Wide Field Instrument (WFI) will be utilizing a preliminary Science Data Processing (SDP) pipeline during its Integration and Test, and to some extent during Operations, to track basic statistics and identify known features such as cosmic rays, snowballs as well as possible anomalies in raw detector data. In our detectors, these anomalies appear as jumps in the ramp of a readout and are classified as cosmic rays if they appear as a streak or snowballs if they’re more circular. The WFI employs an array of 18 H4RG-10 detectors that collect image samples. Each set of raw frames within a non-destructive exposure is packaged by the SDP pipeline into image cubes for each detector. Each cube is a time series of 4096 × 4096 accumulating pixel frames. The preliminary analysis pipeline is used to locate anomalies in these time-series accumulation frames and identify the type of anomaly, either natural phenomena or detector characteristic. To compare different methods, we’ve implemented both heuristic-based and data-driven methods to identify anomalies. For the heuristic-based approach, we identify snowballs and cosmic rays by the size and shape of outlier pixel clusters between consecutive frames. For data driven methods, we evaluated a Convolutional Neural Network (CNN) model, and more traditional methods like Principal Component Analysis (PCA). CNN is a supervised learning/classification method. Thus, we used a labeled dataset of anomalies to perform segmentation of the image and identify anomalies. We used previously identified cosmic rays and snowballs to measure the accuracy and efficiency of the mentioned approaches. In evaluating these methods, we aim to pick the best fit for the SDP pipeline’s anomaly detection in terms of both performance and runtime.

Paul Horton↗

DOE OSTI2019

A Prospective, Open-Label, Multicenter Phase 2 Trial of Neoadjuvant Therapy Using Full-Dose Gemcitabine and S-1 Concurrent with Radiation for Resectable Pancreatic Ductal Adenocarcinoma

Background: Neoadjuvant therapy reportedly shows only marginal clinical benefit in pancreatic ductal adenocarcinoma (PDAC), especially in resectable cases. However, with more effective regimens, neoadjuvant therapy may become a standard of care for resectable cases. A prospective, open-label, multicenter phases 1 and 2 trial of neoadjuvant therapy was conducted using full-dose gemcitabine and S-1 concurrently with 50.4 Gy of radiation therapy (GSRT) for resectable PDAC. This report describes the phase 2 results. Methods: The phase 2 part of this study enrolled 57 patients with cytologically or histologically proven PDAC deemed resectable based on imaging before neoadjuvant therapy. These patients received GSRT. After reevaluation by computed tomography scan, surgical exploration was performed, followed by adjuvant therapy. According to the prescribed protocol of the clinical trial, statistical analyses included 57 phase 2 patients and 6 phase 1 patients who received the same dosage as in phase 2. Results: This trial enrolled 63 patients (42 men and 21 women) with a median age of 70 years. Leukopenia or neutropenia of grade 3 or higher occurred for 79% of the patients, but no other severe adverse events were observed. Among the 63 patients, 54 underwent surgical resection. Intention-to-treat analysis of the 63 patients showed an excellent median survival time lasting as long as 55.3 months. The patients who completed neoadjuvant therapy, surgery, and adjuvant therapy had a 5-year survival rate of 56.6%. Conclusions: This regimen showed outstanding clinical efficacy with acceptable tolerability for patients with resectable PDAC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

NASA NTRS2011

Validating LES for Jet Aeroacoustics

Engineers charged with making jet aircraft quieter have long dreamed of being able to see exactly how turbulent eddies produce sound and this dream is now coming true with the advent of large eddy simulation (LES). Two obvious challenges remain: validating the LES codes at the resolution required to see the fluid-acoustic coupling, and the interpretation of the massive datasets that result in having dreams come true. This paper primarily addresses the former, the use of advanced experimental techniques such as particle image velocimetry (PIV) and Raman and Rayleigh scattering, to validate the computer codes and procedures used to create LES solutions. It also addresses the latter problem in discussing what are relevant measures critical for aeroacoustics that should be used in validating LES codes. These new diagnostic techniques deliver measurements and flow statistics of increasing sophistication and capability, but what of their accuracy? And what are the measures to be used in validation? This paper argues that the issue of accuracy be addressed by cross-facility and cross-disciplinary examination of modern datasets along with increased reporting of internal quality checks in PIV analysis. Further, it is argued that the appropriate validation metrics for aeroacoustic applications are increasingly complicated statistics that have been shown in aeroacoustic theory to be critical to flow-generated sound.

Bridges, James↗

DOE OSTI2021

Galaxy–galaxy lensing with the DES-CMASS catalogue: measurement and constraints on the galaxy-matter cross-correlation

ABSTRACT The DMASS sample is a photometric sample from the DES Year 1 data set designed to replicate the properties of the CMASS sample from BOSS, in support of a joint analysis of DES and BOSS beyond the small overlapping area. In this paper, we present the measurement of galaxy–galaxy lensing using the DMASS sample as gravitational lenses in the DES Y1 imaging data. We test a number of potential systematics that can bias the galaxy–galaxy lensing signal, including those from shear estimation, photometric redshifts, and observing conditions. After careful systematic tests, we obtain a highly significant detection of the galaxy–galaxy lensing signal, with total S/N = 25.7. With the measured signal, we assess the feasibility of using DMASS as gravitational lenses equivalent to CMASS, by estimating the galaxy-matter cross-correlation coefficient rcc. By jointly fitting the galaxy–galaxy lensing measurement with the galaxy clustering measurement from CMASS, we obtain $r_{\rm cc}=1.09^{+0.12}_{-0.11}$ for the scale cut of $4 \, h^{-1}{\rm \,\,Mpc}$ and $r_{\rm cc}=1.06^{+0.13}_{-0.12}$ for $12 \, h^{-1}{\rm \,\,Mpc}$ in fixed cosmology. By adding the angular galaxy clustering of DMASS, we obtain rcc = 1.06 ± 0.10 for the scale cut of $4 \, h^{-1}{\rm \,\,Mpc}$ and rcc = 1.03 ± 0.11 for $12 \, h^{-1}{\rm \,\,Mpc}$. The resulting values of rcc indicate that the lensing signal of DMASS is statistically consistent with the one that would have been measured if CMASS had populated the DES region within the given statistical uncertainty. The measurement of galaxy–galaxy lensing presented in this paper will serve as part of the data vector for the forthcoming cosmology analysis in preparation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Realistic galaxy image simulation via score-based generative models

ABSTRACT We show that a denoising diffusion probabilistic model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic Instrument (DESI) grz imaging of galaxies from the Photometry and Rotation curve OBservations from Extragalactic Surveys (PROBES) sample and galaxies selected from the Sloan Digital Sky Survey. Subjectively, the generated galaxies are highly realistic when compared with samples from the real data set. We quantify the similarity by borrowing from the deep generative learning literature, using the ‘Fréchet inception distance’ to test for subjective and morphological similarity. We also introduce the ‘synthetic galaxy distance’ metric to compare the emergent physical properties (such as total magnitude, colour, and half-light radius) of a ground truth parent and synthesized child data set. We argue that the DDPM approach produces sharper and more realistic images than other generative methods such as adversarial networks (with the downside of more costly inference), and could be used to produce large samples of synthetic observations tailored to a specific imaging survey. We demonstrate two potential uses of the DDPM: (1) accurate inpainting of occluded data, such as satellite trails, and (2) domain transfer, where new input images can be processed to mimic the properties of the DDPM training set. Here we ‘DESI-fy’ cartoon images as a proof of concept for domain transfer. Finally, we suggest potential applications for score-based approaches that could motivate further research on this topic within the astronomical community.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models

Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.

artificial intelligence↗