Commercial Satellite Research Articles

Data Fusion in Earth Observation and the Role of Citizen as a Sensor: A Scoping Review of Applications, Methods and Future Trends

Recent advances in Earth Observation (EO) placed Citizen Science (CS) in the highest position, declaring their essential provision of information in every discipline that serves the SDGs, and the 2050 climate neutrality targets. However, so far, none of the published literature reviews has investigated the models and tools that assimilate these data sources. Following this gap of knowledge, we synthesised this scoping systematic literature review (SSLR) with a will to cover this limitation and highlight the benefits and the future directions that remain uncovered. Adopting the SSLR guidelines, a double and two-level screening hybrid process found 66 articles to meet the eligibility criteria, presenting methods, where data were fused and evaluated regarding their performance, scalability level and computational efficiency. Subsequent reference is given on EO-data, their corresponding conversions, the citizens’ participation digital tools, and Data Fusion (DF) models that are predominately exploited. Preliminary results showcased a preference in the multispectral satellite sensors, with the microwave sensors to be used as a supplementary data source. Approaches such as the “brute-force approach” and the super-resolution models indicate an effective way to overcome the spatio-temporal gaps and the so far reliance on commercial satellite sensors. Passive crowdsensing observations are foreseen to gain a greater audience as, described in, most cases as a low-cost and easily applicable solution even in the unprecedented COVID-19 pandemic. Immersive platforms and decentralised systems should have a vital role in citizens’ engagement and training process. Reviewing the DF models, the majority of the selected articles followed a data-driven method with the traditional algorithms to still hold significant attention. An exception is revealed in the smaller-scale studies, which showed a preference for deep learning models. Several studies enhanced their methods with the active-, and transfer-learning approaches, constructing a scalable model. In the end, we strongly support that the interaction with citizens is of paramount importance to achieve a climate-neutral Earth.

뉴 스페이스 시대의 우주무기화에 대한 국제법적 고찰: 국제인도법 원칙을 중심으로

Space development, which started in earnest from the Cold War between the U.S. and the Soviet Union, has now entered the so-called New-Space era. Private entities are playing a more leading role in the development of the outer space than the militaries and governments. This New-Space will bring more convenience and benefits to our lives. Meanwhile, as we has become more dependent on space, our vulnerability to the space environment is getting bigger. Low-Earth orbit is experiencing unprecedented congestion due to the increase in the number of newly launched satellites and junk satellites that do not work any more. This vulnerability will be further exacerbated by the space-weaponization movements where states seek to gain space superiority by using space militarily. Under these circumstances, if an armed conflict take place in the outer space, collateral damage to the civilian sector is highly likely to occur. Therefore, a careful approach is required in selecting the target, means, and method of attack in an armed conflict in the outer space. We should consider the most important norms under international humanitarian law. These are the principle of distinction, the principle of proportionality, the principle of prevention, and the principle of prohibition of indiscriminate attacks. A person planning an armed attack should make a clear distinction between military satellites and commercial satellites in determining the target of the attack and can attack only on military objectives that makes an effective contribution to military activities in nature, location, purpose, and use, and that bring definite advantages to the armed forces. In addition, in case of attacking a dual-use space object which serves as commercial and military purposes, a person planning the attack should consider the damage to the civilian sector. Furthermore, all practicable measures should be taken to avoid and minimize such collateral damage, and indiscriminate attack is also prohibited. In terms of methods of attack, various attack methods such as directed energy weapons, electromagnetic pulse attack weapons, electronic attack weapons, and cyber attacks have been developed to be used in the outer space as well as the kinetic ASAT weapons. Kinetic ASAT attacks that generate a large amount of space debris should be avoided because the damage to civilians and civilian objects by space debris can be huge. Therefore, an attack in the outer space should be conducted in a non-kinetic method. Even among the non-kinetic methods, only those methods that temporarily disrupt or disable the function of a satellite should be permitted in order to minimize the damage to civilians while achieving military objectives.

Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt

Monitoring strategic agricultural crops in terms of crop growth performance, by accurate cost-effective and quick tools is crucially important in site-specific management to avoid crop reductions. The availability of commercial high resolution satellite images with high resolution (spatial and spectral) as well as in situ spectra measurements can help decision takers to have deep insight on crop stress in a certain region. The research attempts to examine remote sensing dataset for forecasting wheat crop (Sakha 61) characteristics including the leaf area index (LAI), plant height (plant-h), above ground biomass (AGB) and Soil Plant Analysis Development (SPAD) value of wheat across non-stress, drought and salinity-induced stress in the Nile Delta region. In this context, the ability of in situ spectroradiometry measurements and QuickBird high resolution images was evaluated in our research. The efficiency of Random Forest (RF) and Artificial Neural Network (ANN), mathematical models was assessed to estimate the four measured wheat characteristics based on vegetation spectral reflectance indices (V-SRIs) extracted from both approaches and their interactions. Field surveys were carried out to collect in situ spectroradiometry measurements concomitant with the acquisition of QuickBird imagery. The results demonstrated that several V-SRIs extracted from in situ spectroradiometry data and the QuickBird image correlated with the LAI, plant-h, AGB, and SPAD value of wheat crop across the study site. The determination coefficient (R2) values of the association between V-SRIs of in situ spectroradiometry data and various determined wheat characteristics varied from 0.26 to 0.85. The ANN-GSIs-3 was found to be the optimum predictive model, demonstrating a greater relationship between the advanced features and LAI. The three features of V-SRIs comprised in this model were strongly significant for the prediction of LAI. The attained results indicated high R2 values of 0.94 and 0.86 for the training and validation phases. The ANN-GSIs-3 model constructed for the determination of chlorophyll in the plant which had higher performance expectations (R2 = 0.96 and 0.92 for training and validation datasets, respectively). In conclusion, the results of our study revealed that high resolution remote sensing images such as QuickBird or similar imagery, and in situ spectroradiometry measurements have the feasibility of providing necessary crop monitoring data across non-stressed and stressed (drought and salinity) conditions when integrating V-SRIs with ANN and RF algorithms.

Spatial Evolution of Energetic Electrons Affecting the Upper Atmosphere during the Last Two Solar Cycles

Future commercial, scientific, and other satellite missions require low-Earth-orbit (LEO) altitudes of 300–400 km for long-term successful space operations. The Earth’s radiation belt (ERB) is an inevitable obstacle for manned and other space missions. Precipitation of >30 keV energetic electrons from the ERB is one of the sources of ionization in LEO, space vehicles, in the ionosphere, and in the upper atmosphere. We show, in this work, that the area of electron precipitation from the outer ERB shifts equator-wards to Siberia. We further show a substantive decrease in the intensity of energetic electrons in the area of the South Atlantic Anomaly (SAA) from the 23rd to the 24th solar cycles. These results can be attributed to, and explained by, variations in geomagnetic activity, with a noticeable change in the configuration of the Earth’s magnetic field during the 24th solar cycle. The diminishing SAA area and electron fluxes should allow elevation of the International Space Station to higher altitudes, thereby making these altitudes accessible to relevant space missions.

Design and Implementation of TDI CCD Imaging Circuits in Space Applications

Imaging circuits of converting light signals to electrical signals is designed for different remote sensing satellites with different sensors in space applications. Based on a TDI CCD sensor, this paper presents the detailed design and implementation of imaging circuits which is more suitable for use on commercial satellites. This paper utilizes industrial-grade electronics to achieve imaging circuits, which becomes effective solutions to reduce the costs of remote sensing satellites. The signal processors receive analog output signals of TDI CCD to convert digital signals. The core processor, FPGA, receives and processes digital signals them from the signal processors and delivers to transceivers. The transceivers receive the image data from FPGA and deliver them to the host computer. Moreover, a schematic of the completed board of TDI CCD imaging circuits is presented. Finally, to verify the performance of the imaging circuit, the HCTF of TDI CCD was measured on this CCD imaging circuit.The measured result is consistent with the product manual, which shows that the imaging circuits perform well.

A commentary of “Using satellites to map trees”: 10 remarkable discoveries from 2020 in Nature

Brandt et al. reported the results of their analysis from high-resolution satellite images, covering more than 1.3 million square kilometers of the Western Sahara and Sahel in West Africa. They mapped the locations and sizes of approximately 1.8 billion trees. Prior to this, scientists had never made such a detailed map of trees in such a large area. Commercial satellites have begun to collect data and can detect small ground objects that are 1 square meter or less in size. Therefore, the field of terrestrial remote sensing may have a significant advance from mainly a comprehensive landscape-scale measurement to mapping the position and canopy size of each tree at a regional or even global scale. This progress will revolutionize how we think, monitor, simulate, and manage the global terrestrial ecosystem.①①Original source in Chinese: Huaguao Huang, Using satellites to map trees, Bulletin of National Natural Science Foundation of China. 35 (2) (2021) 239-240.

Trigger time acquisition system for ground based air shower experiments

For observations of cosmic gamma rays of several TeV or more, many experimental groups conduct ground-based air shower observation experiments. New projects such as Andes Large area PArticle detector for Cosmic ray physics and Astronomy (ALPACA) are also underway. In these experiments, an accurate trigger time measurement system is required when sudden cosmic phenomena are observed. To record such sudden astronomical phenomena, we have developed an event-trigger time recording system that accurately measures the arrival times of air showers. The system consists of a commercial global navigation satellite system module, a high-precision clock, a multi-hit type time-to-digital converter with a reset function for accurate time measurement, and a network-time-protocol server installed on a computer. It was confirmed that the time accuracy compared to UTC was approximately 1μs, and the time deviation was approximately 11.4 ns with one standard deviation. The developed system is versatile and can also be used for the ALPACA experiment. It is expected that the time accuracy of this system can be improved to approximately ±40 ns.

Capabilities of Commercial Satellite Earth Observation Systems and Applications for Nuclear Verification and Monitoring

A growing number of commercial Earth observation satellite systems provide capabilities with significant application in nuclear verification, monitoring, and proliferation analysis. This article provides some relevant examples and a case study describing the importance of spatial, spectral, and temporal resolution on detectability of ground targets and monitoring of activity. The article also provides an overview of 300 operational (as of September 2021) optical and radar systems with a ground resolution of 5 m or better, whose imagery is available to the public. By merging all satellites into one super-constellation, a simulation was performed to describe its potential coverage. The analysis suggests that with current commercial capabilities it would be possible to image newly discovered alleged ICBM fields in China every few hours with a ground resolution sufficient to detect new construction and missile uploading.

Object Tracking in Satellite Videos Based on Siamese Network With Multidimensional Information-Aware and Temporal Motion Compensation

The availability of many commercial satellites has created favorable conditions for tracking typical objects in remote sensing sequences, making them widely useful in numerous applications. However, small objects, multiple similar disruptors, background clutter, and occlusion are significant challenges to this field. This study proposes the novel tracker-temporal motion compensation Siamese network (Siam-TMC) for remote sensing tracking. Our method relies on a multidimensional information-aware module and a temporal motion compensation mechanism. Notably, we propose a dual branch-based Dim-Aware module that brings together foreground and high frequency information to distinguish between critical small objects and interferers. In addition, a TMComp mechanism using temporal motion information was designed to mitigate object trajectory drift through the supervision of occlusion detection. Detailed experimental comparisons on a benchmark dataset show that our method outperforms the state-of-the-art tracking models, particularly in occlusion scenarios.

Стратегическая стабильность и китайский гамбит

During the last three years, the prospects of engaging China in the process of nuclear disarmament has moved to the forefront of the arms control agenda. In 2019–2020, it was made the top issue by the Donald Trump’s administration when it withdrew from the Treaty on the Elimination of Intermediate-Range and Shorter-Range Missiles (INF Treaty) and refused to prolong the Treaty on Measures for the Further Reduction and Limitation of Strategic Offensive Arms (the New START). As for Beijing, it flatly rejected US demands to join arms control and this position was endorsed by Moscow. Thanks to the Joseph Biden’s administration coming to power in January 2021, the New START was extended by five years in February of the same year. In June, the summit of the presidents of Russia and the United States was held in Geneva, and in July, the official dialogue on strategic stability was launched. However, during the same summer, an unexpected event happened. Independent U. S. experts published information from commercial satellites, which revealed an ongoing construction of three large military bases in central areas of China with hundreds of new silo launchers for intercontinental missiles. Afterwards, the Pentagon confirmed this information and predicted that Chinese missiles buildup may reach the level of about 1000 or more nuclear warheads by the year 2030. This news was neither confirmed nor denied by the official Beijing. However, together with continuing Chinese deployment of strategic ballistic missile nuclear submarines, forthcoming introduction of heavy bombers, expanding early warning satellites and tests of antisatellite systems – these developments imply the fundamental shift in the global and regional strategic balances with profound political consequences. American response to China’s strategic buildup and Russian reaction to the U.S. military innovations may entail a next massive cycle of arms race and would cause a new stagnation at the negotiations of the two nuclear superpowers – all highly detrimental to international security. Nonetheless, there is still a chance of reinvigorated diplomatic effort at the U.S. – Russian and U.S. – Chinese tracks, which might lay the foundation of the future multilateral framework of strategic stability.

Regulating Science Fiction: the Regulatory Deficiencies in a Rapidly Growing Commercial Space Industry

a. This Comment explores the deficiencies of the legal framework governing commercial space with the advent of satellite mega-constellations. The scope and size of these so-called constellations are completely unlike anything the space industry has contemplated since the first rocket was launched into orbit. Moreover, these constellations are an extremely new phenomenon—the prime movers in the industry are just beginning to create these massive man-made wonders in space. As such, the legal framework was designed around space operations that are much smaller in scope. That framework has struggled to keep pace with the rapidly growing commercial space industry generally and the constellation industry specifically. This Comment begins by explaining some of the pressing concerns regarding space exploration and specifically addresses the growing concerns roused by the addition of tens of thousands of small satellites primarily within Earth’s lower orbit. It then lays out the relevant regulatory framework. It discusses how that framework has operated in the past, how it has been applied to commercial satellite constellations so far, and how it has changed over the past few years. Finally, this Comment discusses the reality of satellite constellation regulation—that it is being largely self-regulated by the industry. It analyzes the benefits and negative aspects of that reality, and it concludes with a proposal on how to move forward with space regulation such that the continued viability of space exploration and the protection of relevant stakeholders will be assured well into the future.

Design and Implementation of an Encryption Unit to Enhance Satellite Communication Security

Satellite projects consume very high resources in all of their phases (research, design, manufacturing, testing and launching), they also extend for significant amount of time, and hence satellites must be protected against intrusion and data perception. Over the past years, satellite security has been overlooked except for military satellites because satellite intrusions were inconceivable as they far away from earth, but recently commercial satellite manufacturers realized the importance of satellite security because satellite intrusion became not an impossible task. In this paper, we present the design and implementation of encryption unit to secure satellite communication system by ciphering tele-commands using advanced encryption algorithm (AES) with random key generation, Diffie-Hellman key exchange algorithm and Hash-based message authentication code (HMAC) digital signature algorithm.

Deep-learning-based solution for data deficient satellite image segmentation

Satellite images have become more easily accessible due to government programmes and the availability of commercial earth observation satellites. The increased volume of data and the recent advancement in deep learning-based techniques have made possible the development of fast and accurate methods for satellite image segmentation, which aims at automatically giving each pixel in the satellite image a label of the underlying object/ground type. However, despite the abundant supply of raw satellite images, the labelling of these images is a manual task and is therefore time and labour intensive. When a large labelled dataset is absent, resorting to a smaller data source significantly hampers the performance of the machine learning model. Therefore, an approach which mitigates the effects of using small labelled datasets should be investigated. This paper provides a solution to the problem of insufficient data samples for satellite image segmentation. To this end, two methods are developed and compared with the benchmark U-Net model: the use of transfer learning on an Xception architecture, and the use of steerable filters. The first model transfers knowledge from a pre-trained Xception model and predicts with a multi-resolution feature fusion module, specifically designed to recover fine details. The latter further improves the capability of our neural network model in handling data with high rotational variation. Both models are experimentally shown to significantly enhance the segmentation performance despite a small-sized training dataset.

A Novel Method Based on Deep Learning, GIS and Geomatics Software for Building a 3D City Model from VHR Satellite Stereo Imagery

The aim of the paper is to identify a suitable method for the construction of a 3D city model from stereo satellite imagery. In order to reach this goal, it is necessary to build a workflow consisting of three main steps: (1) Increasing the geometric resolution of the color images through the use of pan-sharpening techniques, (2) identification of the buildings’ footprint through deep-learning techniques and, finally, (3) building an algorithm in GIS (Geographic Information System) for the extraction of the elevation of buildings. The developed method was applied to stereo imagery acquired by WorldView-2 (WV-2), a commercial Earth-observation satellite. The comparison of the different pan-sharpening techniques showed that the Gram–Schmidt method provided better-quality color images than the other techniques examined; this result was deduced from both the visual analysis of the orthophotos and the analysis of quality indices (RMSE, RASE and ERGAS). Subsequently, a deep-learning technique was applied for pan sharpening an image in order to extract the footprint of buildings. Performance indices (precision, recall, overall accuracy and the F1measure) showed an elevated accuracy in automatic recognition of the buildings. Finally, starting from the Digital Surface Model (DSM) generated by satellite imagery, an algorithm built in the GIS environment allowed the extraction of the building height from the elevation model. In this way, it was possible to build a 3D city model where the buildings are represented as prismatic solids with flat roofs, in a fast and precise way.

Achieving Breakthroughs in Global Hydrologic Science by Unlocking the Power of Multisensor, Multidisciplinary Earth Observations

AbstractOver the last half century, remote sensing has transformed hydrologic science. Whereas early efforts were devoted to observation of discrete variables, we now consider spaceborne missions dedicated to interlinked global hydrologic processes. Furthermore, cloud computing and computational techniques are accelerating analyses of these data. How will the hydrologic community use these new resources to better understand the world's water and related challenges facing society? In this commentary, we suggest that optimizing the benefits of remote sensing for advancing hydrologic research will happen by integrating multidisciplinary and multisensor data, leveraging commercial satellite measurements, and employing data assimilation, cloud computing, and machine learning. We provide several recommendations to these ends.

Earth observation data for seabirds and their habitats: An introduction

Remote sensing, the science of obtaining information about objects or areas from a distance, has the potential to contribute greatly to conservation, ecology and biodiversity studies. Here, we introduce remote sensing capabilities that could contribute to seabird studies, covering remoting sensing of seabirds directly, and indirectly through remote sensing of seabird habitats. We focus on satellite remote sensing, as these data are the most readily available, and are mostly freely available, and we also present some additional Earth observation (EO) data. This is not an exhaustive list, rather a selection of practical and user-friendly data and tools that are publicly available. At present, the spatial resolution of optical commercial satellite imagery is high enough to identify the largest seabirds such as penguins and albatrosses. Military satellites are capable of higher resolutions that can detect 10 cm objects, but these are not available to the general public. Satellite observation and other EO datasets containing geographic and sea surface condition variables are useful for complementing seabird sightings and tracking data, with their associations justifiable for certain species, breeding status and locations. This paper is aimed at researchers in the fields of seabird conservation, ecology, and biodiversity whose research would benefit from satellite data and from knowing what data sources are available.

The QAA-RGB: A universal three-band absorption and backscattering retrieval algorithm for high resolution satellite sensors. Development and implementation in ACOLITE

We present the “QAA-RGB”, a semianalytical algorithm that retrieves total absorption, particle backscattering, as well as the diffuse attenuation coefficient and the Secchi disk depth, from eighteen metre and decametre satellite sensors, including present and heritage Landsat's data, Sentinel 2 at 10 m and an array of commercial satellites such as PlanetScope, Pléiades and Worldview. The QAA-RGB is a minimal version of the Quasi-Analytical Algorithm (QAA), and therefore keeps its robustness and general validity across different water types. It ingests remote-sensing reflectance at only three bands, centred on red, green and blue wavelengths. Retrievals of non-water absorption at 555 nm (anw(555)) are satisfactory, with a standard deviation of σ ~ 50% up to anw(555) < 2 m−1, based on in situ datasets. Careful calibration results in unbiased retrievals across all sensors. Validation to independent in situ data is also provided. The QAA-RGB has been implemented in the ACOLITE generic processor, and is openly accessible to the community. Consistency of near-coincident observations from different satellite sensors is demonstrated.

A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Resolution Satellite Imagery

BACKGROUND AND AIM: Air Quality (AQ) is currently measured using ground monitoring stations which require funding and infrastructure to support and lack spatial coverage. In contrast, high-resolution commercial satellite imagery (⪯2 m/pixel) can be produced for almost any location on Earth and is readily available. The goal of this project is to develop a pipeline that uses MAXAR satellite imagery to produce meter-scale, continuous maps of AQ for any city around the globe, with greater resolution and coverage than existing methods. METHODS: We developed a Deep Neural Network (DNN) model that can produce AQ estimates for previously unseen urban locations using satellite imagery. The model is based on the VGG-16 pretrained neural network architecture. We used this architecture to extract spatial features from satellite imagery that are passed on to Fully Connected layers to produce an estimate of PM2.5 and NO2 concentrations. We partitioned satellite imagery into 612,248 patches and fed these patches into the model to produce a 100m (200m) continuous grid of PM2.5 (NO2) estimates. RESULTS:We fed the high-resolution (0.5m–2.5m) commercial satellite imagery to a DNN to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous meter-scale AQ estimations. CONCLUSIONS:This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create meter-scale AQ maps in developing cities, where current and historical ground data is limited. KEYWORDS: Air quality. Remote Sensing, Urban environment, Deep learning, Satellite Imagery

Mapping Invasive Phragmites australis Using Unoccupied Aircraft System Imagery, Canopy Height Models, and Synthetic Aperture Radar

Invasive plant species are an increasing worldwide threat both ecologically and financially. Knowing the location of these invasive plant infestations is the first step in their control. Surveying for invasive Phragmites australis is particularly challenging due to limited accessibility in wetland environments. Unoccupied aircraft systems (UAS) are a popular choice for invasive species management due to their ability to survey challenging environments and their high spatial and temporal resolution. This study tested the utility of three-band (i.e., red, green, and blue; RGB) UAS imagery for mapping Phragmites in the St. Louis River Estuary in Minnesota, U.S.A. and Saginaw Bay in Michigan, U.S.A. Iterative object-based image analysis techniques were used to identify two classes, Phragmites and Not Phragmites. Additionally, the effectiveness of canopy height models (CHMs) created from two data types, UAS imagery and commercial satellite stereo retrievals, and the RADARSAT-2 horizontal-horizontal (HH) polarization were tested for Phragmites identification. The highest overall classification accuracy of 90% was achieved when pairing the UAS imagery with a UAS-derived CHM. Producer’s accuracy for the Phragmites class ranged from 3 to 76%, and the user’s accuracies were above 90%. The Not Phragmites class had user’s and producer’s accuracies above 88%. Inclusion of the RADARSAT-2 HH polarization caused a slight reduction in classification accuracy. Commercial satellite stereo retrievals increased commission errors due to decreased spatial resolution and vertical accuracy. The lowest classification accuracy was seen when using only the RGB UAS imagery. UAS are promising for Phragmites identification, but the imagery should be used in conjunction with a CHM.

OPERATIONAL-SCALE GEOAI FOR PAN-ARCTIC PERMAFROST FEATURE DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGERY

Abstract. Regional extent and spatiotemporal dynamics of Arctic permafrost disturbances remain poorly quantified. High spatial resolution commercial satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. The imagery is still largely underutilized, and value-added Arctic science products are rare. Knowledge discovery through artificial intelligence (AI), big imagery, high performance computing (HPC) resources is just starting to be realized in Arctic science. Large-scale deployment of petabyte-scale imagery resources requires sophisticated computational approaches to automated image interpretation coupled with efficient use of HPC resources. In addition to semantic complexities, multitude factors that are inherent to sub-meter resolution satellite imagery, such as file size, dimensions, spectral channels, overlaps, spatial references, and imaging conditions challenge the direct translation of AI-based approaches from computer vision applications. Memory limitations of Graphical Processing Units necessitates the partitioning of an input satellite imagery into manageable sub-arrays, followed by parallel predictions and post-processing to reconstruct the results corresponding to input image dimensions and spatial reference. We have developed a novel high performance image analysis framework –Mapping application for Arctic Permafrost Land Environment (MAPLE) that enables the integration of operational-scale GeoAI capabilities into Arctic science applications. We have designed the MAPLE workflow to become interoperable across HPC architectures while utilizing the optimal use of computing resources.