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High-accuracy optical clocks have garnered increasing attention for their potential application in various fields, including geodesy. According to the gravitational red-shift effect, clocks at lower altitudes on the Earth's surface run slower than those at higher altitudes due to the differential gravitational field. Consequently, the geopotential difference can be determined by simultaneously comparing the frequency of two optical clocks at disparate locations. Here, we report geopotential difference measurements conducted using a pair of transportable 40Ca+ optical clocks with uncertainties at the 10 − 17 level. After calibrating the output frequencies of two optical clocks in the horizontal position, frequency comparison is realized by moving Clock 2 to two different positions using a high-precision optical fiber time–frequency transmission link with Clock 1. The elevation difference of the two different positions, as processed by ensemble empirical mode decomposition (EEMD), is measured as −88.4 cm ± 16.7 cm and 104.5 cm ± 20.1 cm, respectively, which is consistent with the geometric measurement results within the error range. This experimental result validates the credibility of the optical clock time–frequency comparison used in determining geopotential differences, thereby providing a novel measurement model for the establishment of a global unified elevation datum. [ABSTRACT FROM AUTHOR]

Knowing the location of migratory birds throughout their annual cycle is fundamental for many questions in ornithology. Technological advances have provided several approaches, with increasing miniaturization allowing deployment on smaller and smaller birds. Here, we examine the strengths and limitations of barometric pressure geolocators (pressure tags), which have recently been shown to be extremely effective in tracking small birds (<25 g). The newly developed geolocation by pressure workflow (GeoPressureR) uses a global weather analysis dataset and hidden Markov movement model to reveal locations more accurately or continuously than can be achieved by other available light-weight devices (e.g. light-level geolocators, automated radio telemetry, and archival GPS tags). Additionally, pressure data can provide altitude information and document generally intractable flight behavior, as well as exact migratory phenologies. This new technology can be applied to important unresolved problems such as altitudinal migration, stopover site use, molt migration, vertical airspace use, and drivers of migratory flight initiation, duration, and direction. We provide an example of a pressure tag on a Swainson's Warbler (Limnothlypis swainsonii) deployed on its breeding territory in Virginia, USA, revealing the complete story of its migration to and from Cuba, including its refined wintering site, stopover sites, and migration routes, all with precise timing. Studies relying on pressure tags are still subject to biases related to recovery of the devices for data extraction, and application to birds that exhibit extensive aerial movements (e.g. swallows and swifts) remains challenging. Widespread deployment of pressure tags could show unprecedented details of bird migration and other aspects of aerial behavior, which could benefit conservation and greatly enrich our understanding of avian movement ecology. [ABSTRACT FROM AUTHOR]

This manuscript presents a novel geofencing method in behavioral research. Geofencing, built upon geolocation technology, constitutes virtual fences around specific locations. Every time a participant crosses the virtual border around the geofenced area, an event can be triggered on a smartphone, e.g., the participant may be asked to complete a survey. The geofencing method can alleviate the problems of constant location tracking, such as recording sensitive geolocation information and battery drain. In scenarios where locations for geofencing are determined by participants (e.g., home, workplace), no location data need to be transferred to the researcher, so this method can ensure privacy and anonymity. Given the widespread use of smartphones and mobile Internet, geofencing has become a feasible tool in studying human behavior and cognition outside of the laboratory. The method can help advance theoretical and applied psychological science at a new frontier of context-aware research. At the same time, there is a lack of guidance on how and when geofencing can be applied in research. This manuscript aims to fill the gap and ease the adoption of the geofencing method. We describe the current challenges and implementations in geofencing and present three empirical studies in which we evaluated the geofencing method using the Samply application, a tool for mobile experience sampling research. The studies show that sensitivity and precision of geofencing were affected by the type of event, location radius, environment, operating system, and user behavior. Potential implications and recommendations for behavioral research are discussed. [ABSTRACT FROM AUTHOR]

This case study investigates the revitalization of the Korytnica spa settlement in the Low Tatras, known for its rich natural and cultural heritage, including mineral springs. Despite its historical and therapeutic advantages, Korytnica currently experiencing challenges in renovation and development as a thriving spa and tourist resort. The article explores the strategic use of geolocation measurement technology and the potential of mineral springs to enhance the tourist infrastructure and promote the site. The article presents a multidisciplinary approaches, including sustainable development, and the integration of heritage into tourism products. It concludes with a proposal for optimizing transport routes and improving infrastructure in the area, thereby supporting accessibility and minimizing environmental impact. The main output of the article is a proposal for improving the route to the relevant area, improving accessibility by analyzing and improving infrastructure and transport processes. [ABSTRACT FROM AUTHOR]

IP geolocation technology based on network measurement can provide IP geographic location capability without user assistance. In the case that the IP target device or intermediate route device does not respond to the detection message, the existing geolocation methods have reduced the accuracy of city-level geolocation, not to mention that the target cannot be located within the city. To solve this problem, a geolocation method for unreachable IP (called GUI) is proposed. GUI first extracts the target city metropolitan area network (MAN) topology, obtains the path to the IP target and compares it with the MAN topology to achieve the city-level geolocation of the target, and then looks for landmarks in the database that belong to the same /24 subnet and the same city-level location with the target. If the above landmarks exist, GUI uses the coordinate set corresponding to the landmarks to solve the minimum covering circle, so as to judge the location of the target. If the above landmarks do not exist, GUI searches for the intersection nodes from the target detection path and MAN topology, enumerates the connected landmarks, calculates the minimum covering circle according to the coordinates and uses it for the target's urban internal geolocation. The experimental results of 15 cities in China and the USA show that GUI can effectively geolocate IP targets. The city-level geolocation success rate of reachable IP can reach 99.41%, that of unreachable IP can reach 89.37% and the minimum median error of urban internal unreachable IP geolocation can reach 4.99km. All indicators are significantly better than the existing typical geolocation methods, such as PoP-Based Geolocation, Street-Level Geolocation, Ranking Nodes Based Geolocation, and the existing typical IP geographic location databases such as IPIP and MaxMind. [ABSTRACT FROM AUTHOR]

Cross-view geolocation is a valuable yet challenging task. In practical applications, the images targeted by cross-view geolocation technology encompass multi-domain remote sensing images, including those from different platforms (e.g., drone cameras and satellites), different perspectives (e.g., nadir and oblique), and different temporal conditions (e.g., various seasons and weather conditions). Based on the characteristics of these images, we have designed an effective framework, Image Reconstruction and Multi-Unit Mutual Learning Net (IML-Net), for accomplishing cross-view geolocation tasks. By incorporating a deconvolutional network into the architecture to reconstruct images, we can better bridge the differences in remote sensing image features across different domains. This enables the mapping of target images from different platforms and perspectives into a shared latent space representation, obtaining more discriminative feature descriptors. The process enhances the robustness of feature extraction for locating targets across a wide range of perspectives. To improve the network's performance, we introduce attention regions learned from different units as augmented data during the training process. For the current cross-view geolocation datasets, the use of large-scale datasets is limited due to high costs and privacy concerns, leading to the prevalent use of simulated data. However, real data allow the network to learn more generalizable features. To make the model more robust and stable, we collected two groups of multi-domain datasets from the Zurich and Harbin regions, incorporating real data into the cross-view geolocation task to construct the ZHcity750 Dataset. Our framework is evaluated on the cross-domain ZHcity750 Dataset, which shows competitive results compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The surge in remote sensing satellites and diverse imaging modes poses substantial challenges for ground systems. Swift and high-precision geolocation is the foundational requirement for subsequent remote sensing image applications. Breakthroughs in intelligent on-orbit processing now enable on-orbit geometric processing. In the absence of control data on board, a recent trend is to introduce reference data onto satellites. However, the pre-storage of traditional reference images or control point databases presents a significant challenge to the limited on-board data storage capacity. Therefore, oriented to the demand for control information acquisition during on-orbit geometry processing, we propose the construction of lightweight and stable feature databases. Initially, stable feature classes are obtained through iterative matching filtering, followed by re-extracting feature descriptors for each stable feature point location on the training images. Subsequently, the descriptors of each point location are clustered and fused using affinity propagation (AP) to eliminate redundancy. Finally, LDAHash is utilized to quantize floating-point descriptors into binary descriptors, further reducing the storage space. In our experiments, we utilize a variety of feature algorithms to assess the generality of our proposed method, thus extending the scope of the feature database and its applicability to various scenarios. This work plays a crucial role in advancing the technology of on-orbit geometry processing for remote sensing satellites. [ABSTRACT FROM AUTHOR]

The Ziyuan-1 (ZY-1) 02E launched on December 26, 2021 is equipped with a thermal infrared sensor (IRS), which has a ground resolution of better than 16m and a width priority of 115km, balancing the advantages of high resolution and large wide observation. The geometric performance of image data is the premise of remote sensing application, and the difficulty in evaluating the geometric performance of thermal infrared image data lies in the extraction of well-distributed, reliable and accurate GCPs. To extract GCP from high-precision reference images, it is necessary to overcome the feature differences between images caused by different spectral responses. This paper adopts a phase correlation matching method based on frequency domain to realize the fine registration of the data obtained by the emission thermal spectral band with the data from the reflectance spectral band, which can not only solve the GCP extraction of conventional thermal infrared images collected during the day, but also obtain satisfactory GCP data from thermal infrared data acquired at night. In order to test the GCP method proposed in this paper, three typical areas are selected as the experimental areas, including Yiyang City in Hunan, Nagqu City in Xizang and Hami City in Xinjiang, and the internal geometric accuracy and absolute geolocation accuracy of the thermal infrared data spanning one year are evaluated and analyzed by using the reference data composed of the DOM with an accuracy of 2m and the DEM with an accuracy of 10m. The research results indicate that the internal geometric accuracy of ZY-1 02E IRS satellite image data is better than 1.0 pixels, and the performance is satisfactory. However, its absolute geolocation accuracy needs to be continuously improved, especially there are systematic errors in the ascending data at night that require further research. Overall, it meets the design accuracy indicators of satellites and can meet the application requirements of thermal infrared remote sensing. [ABSTRACT FROM AUTHOR]

We use high-resolution mobile phone data with geolocation information and propose a novel technical framework to study how social influence propagates within a phone communication network and affects the offline decision to attend a performance event. Our fine-grained data are based on the universe of phone calls made in a European country between January and July 2016. We isolate social influence from observed and latent homophily by taking advantage of the rich spatial-temporal information and the social interactions available from the longitudinal behavioral data. We find that influence stemming from phone communication is significant and persists up to four degrees of separation in the communication network. Building on this finding, we introduce a new "influence" centrality measure that captures the empirical pattern of influence decay over successive connections. A validation test shows that the average influence centrality of the adopters at the beginning of each observational period can strongly predict the number of eventual adopters and has a stronger predictive power than other prevailing centrality measures. Our centrality measure can be used to improve optimal seeding strategies in contexts with influence over phone calls, such as targeted or viral marketing campaigns. We use high-resolution mobile phone data with geolocation information and propose a novel technical framework to study how social influence propagates within a phone communication network and affects the offline decision to attend a performance event. Our fine-grained data are based on the universe of phone calls made in a European country between January and July 2016. We isolate social influence from observed and latent homophily by taking advantage of the rich spatial-temporal information and the social interactions available from the longitudinal behavioral data. We find that influence stemming from phone communication is significant and persists up to four degrees of separation in the communication network. Building on this finding, we introduce a new "influence" centrality measure that captures the empirical pattern of influence decay over successive connections. A validation test shows that the average influence centrality of the adopters at the beginning of each observational period can strongly predict the number of eventual adopters and has a stronger predictive power than other prevailing centrality measures such as the eigenvector centrality and state-of-the-art measures such as diffusion centrality. Our centrality measure can be used to improve optimal seeding strategies in contexts with influence over phone calls, such as targeted or viral marketing campaigns. Finally, we quantitatively demonstrate how raising the communication probability over each connection, as well as the number of initial seeds, can significantly amplify the expected adoption in the network and raise net revenue after taking into account the cost of these interventions. History: Sam Ransbotham, Senior Editor; Yan Huang, Associate Editor. Funding: Y. Leng acknowledges the support provided by the National Science Foundation [Grant IIS-2153468]. E. Moro acknowledges the support provided by the National Science Foundation [Grant 2218748]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2023.1231. [ABSTRACT FROM AUTHOR]

Estimation of people's home locations using location-based services data from smartphones is a common task in human mobility assessment. However, commonly used home detection algorithms (HDAs) are often arbitrary and unexamined. In this study, we review existing HDAs and examine five HDAs using eight high-quality mobile phone geolocation datasets. These include four commonly used HDAs as well as an HDA proposed in this work. To make quantitative comparisons, we propose three novel metrics to assess the quality of detected home locations and test them on eight datasets across four U.S. cities. We find that all three metrics show a consistent rank of HDAs' performances, with the proposed HDA outperforming the others. We infer that the temporal and spatial continuity of the geolocation data points matters more than the overall size of the data for accurate home detection. We also find that HDAs with high (and similar) performance metrics tend to create results with better consistency and closer to common expectations. Further, the performance deteriorates with decreasing data quality of the devices, though the patterns of relative performance persist. Finally, we show how the differences in home detection can lead to substantial differences in subsequent inferences using two case studies—(i) hurricane evacuation estimation, and (ii) correlation of mobility patterns with socioeconomic status. Our work contributes to improving the transparency of large-scale human mobility assessment applications. [ABSTRACT FROM AUTHOR]

Despite the remarkable progress of deep learning-based object extraction in revealing the number and boundary location of geo-objects for high-resolution satellite imagery, it still faces challenges in accurately extracting blurry dense objects. Unlike general objects, blurry dense objects have limited spatial resolution, leading to inaccurate and connected boundaries. Even with the improved spatial resolution and recent boundary refinement methods for general object extraction, connected boundaries may remain undetected in blurry dense object extraction if the gap between object boundaries is less than the spatial resolution. This paper proposes a blurry dense object extraction method named the buffer parsing network (BPNet) for satellite imagery. To solve the connected boundary problem, a buffer parsing module is designed for dense boundary separation. Its essential component is a buffer parsing architecture that comprises a boundary buffer generator and an interior/boundary parsing step. This architecture is instantiated as a dual-task mutual learning head that co-learns the mutual information between the interior and boundary buffer, which estimates the dependence between the dual-task outputs. Specifically, the boundary buffer head generates a buffer region that overlaps with the interior, enabling the architecture to learn the dual-task bias and assign a reliable semantic in the overlapping region through high-confidence voting. To alleviate the inaccurate boundary location problem, BPNet incorporates a high-frequency refinement module for blurry boundary refinement. This module includes a high-frequency enhancement unit to enhance high-frequency signals at the blurry boundaries and a cascade buffer parsing refinement unit that integrates the buffer parsing architecture coarse-to-fine to recover the boundary details progressively. The proposed BPNet framework is validated on two representative blurry dense object datasets for small vehicle and agricultural greenhouse object extraction. The results indicate the superior performance of the BPNet framework, achieving 25.25% and 73.51% in contrast to the state-of-the-art PointRend method, which scored 21.92% and 63.95% in the A P 50 s e g m metric on two datasets, respectively. Furthermore, the ablation analysis of the super-resolution and building extraction methods demonstrates the significance of high-quality boundary details for subsequent practical applications, such as building vectorization. The code is available at: https://github.com/Dingyuan-Chen/BPNet. [ABSTRACT FROM AUTHOR]

The article analyzes how the tracking of children's clothing by fashion brands implicates the enforcement of the Children's Online Privacy Protection Act (COPPA). Topics discussed include right of parents to control their child's privacy and safety as well as the family's personal information with the rise of smart technology, COPPA enforcement in modern cases, COPPA issues pertinent to the smart apparel industry, and proposed solution to reform COPPA's geolocation data tracking regulations.

Climate change is undeniably one of the most pressing and critical challenges facing humanity in the 21st century. In this context, monitoring the Earth's Energy Imbalance (EEI) is fundamental in conjunction with greenhouse gases (GHGs) in order to comprehensively understand and address climate change. The French Uvsq-Sat NG pathfinder mission addresses this issue through the implementation of a Six-Unit CubeSat, which has dimensions of 111.3 × 36.6 × 38.8 cm in its unstowed configuration. Uvsq-Sat NG is a satellite mission spearheaded by the Laboratoire Atmosphères, Observations Spatiales (LATMOS), and supported by the International Satellite Program in Research and Education (INSPIRE). The launch of this mission is planned for 2025. One of the Uvsq-Sat NG objectives is to ensure the smooth continuity of the Earth Radiation Budget (ERB) initiated via the Uvsq-Sat and Inspire-Sat satellites. Uvsq-Sat NG seeks to achieve broadband ERB measurements using state-of-the-art yet straightforward technologies. Another goal of the Uvsq-Sat NG mission is to conduct precise and comprehensive monitoring of atmospheric gas concentrations (CO 2 and CH 4 ) on a global scale and to investigate its correlation with Earth's Outgoing Longwave Radiation (OLR). Uvsq-Sat NG carries several payloads, including Earth Radiative Sensors (ERSs) for monitoring incoming solar radiation and outgoing terrestrial radiation. A Near-Infrared (NIR) Spectrometer is onboard to assess GHGs' atmospheric concentrations through observations in the wavelength range of 1200 to 2000 nm. Uvsq-Sat NG also includes a high-definition camera (NanoCam) designed to capture images of the Earth in the visible range. The NanoCam will facilitate data post-processing acquired via the spectrometer by ensuring accurate geolocation of the observed scenes. It will also offer the capability of observing the Earth's limb, thus providing the opportunity to roughly estimate the vertical temperature profile of the atmosphere. We present here the scientific objectives of the Uvsq-Sat NG mission, along with a comprehensive overview of the CubeSat platform's concepts and payload properties as well as the mission's current status. Furthermore, we also describe a method for the retrieval of atmospheric gas columns (CO 2 , CH 4 , O 2 , H 2 O) from the Uvsq-Sat NG NIR Spectrometer data. The retrieval is based on spectra simulated for a range of environmental conditions (surface pressure, surface reflectance, vertical temperature profile, mixing ratios of primary gases, water vapor, other trace gases, cloud and aerosol optical depth distributions) as well as spectrometer characteristics (Signal-to-Noise Ratio (SNR) and spectral resolution from 1 to 6 nm). [ABSTRACT FROM AUTHOR]

Conditions will be harsher now for women than before Roe v. Wade for one key reason: We live in a surveillance state. While reproductive health care will continue to be a political hot button, one way to manage some of the fallout from Dobbs v. Jackson Women's Health Organization is by placing over-due limits on state surveillance to protect the politically uncontroversial expectation of privacy for personal data. Specifically, measures are needed to protect the privacy of health care data, and, in particular, reproductive health care data. Currently, law enforcement can obtain such data not only through failings in existing legislation but also via the ample digital breadcrumbs that fall outside any regulatory construct, including data obtainable for "free" by subpoenas, orders, warrants, and geofence warrants; and data "for sale" by data brokers, including sensitive geolocation information and data from fertility apps. Given the perfect storm of readily accessible troves of private digital information alongside a panoply of inconsistent state solutions, this Article urges that federal legislation is needed to provide privacy safeguards for reproductive health care data that provides "three corners" of protection in the digital era. The first corner defines health care data to include a specific carve-out for reproductive health care data. The second corner provides the substantive curb of prohibiting data brokers from selling this reproductive health care data. The third corner adds a necessary procedural protection: Because there is no other kind of health care data with the broad potential to subject a patient to criminalization, reproductive health care data that would not be obtainable without a warrant should not be admissible as evidence to criminalize the individual. Setting such a federal floor to limit law enforcement's ability to mine private data for evidence of abortion, criminalize women, and, disproportionately, criminalize women of color, is more critical than ever in the surveillance state. [ABSTRACT FROM AUTHOR]

Polarimetry, which seeks to measure the vectorial information of light modulated by objects, has facilitated bionic geolocation and navigation applications. It is a novel and promising field that provides humans with a remote sensing tool to exploit polarized skylight in a similar way to polarization-sensitive animals, and yet few in-depth reviews of the field exist. Beginning with biological inspirations, this review mainly focuses on the characterization, measurement, and analysis of vectorial information in polarimetry for bionic geolocation and navigation applications, with an emphasis on Stokes–Mueller formalism. Several recent breakthroughs and development trends are summarized in this paper, and potential prospects in conjunction with some cutting-edge techniques are also presented. The goal of this review is to offer a comprehensive overview of the exploitation of vectorial information for geolocation and navigation applications as well as to stimulate new explorations and breakthroughs in the field. [ABSTRACT FROM AUTHOR]

Recently, metasurfaces based on phase-change materials (PCMs) have attracted increasing attention due to the dramatic optical properties contrast between amorphous and crystalline states. The chalcogenide PCMs can be reversibly switched by electrical or optical pulses, offering tunability and reconfigurability for the metasurfaces. In this Perspective, the latest achievements and ongoing development in reconfigurable metasurfaces based on chalcogenide PCMs are presented, including the applications in nonlinear optics, anapole control, beam steering, perfect absorbers, and polaritons. This Perspective ends with perspectives for the growing demands of PCMs based on metasurfaces. [ABSTRACT FROM AUTHOR]

Mobile devices provide us with an important source of data that capture spatial movements of individuals and allow us to derive general mobility patterns for a population over time. In this article, we present a mathematical foundation that allows us to harmonize mobile geolocation data using differential geometry and graph theory to identify spatial behavior patterns. In particular, we focus on models programmed using Computer Algebra Systems and based on a space-time model that allows for describing the patterns of contagion through spatial movement patterns. In addition, we show how the approach can be used to develop algorithms for finding "patient zero" or, respectively, for identifying the selection of candidates that are most likely to be contagious. The approach can be applied by information systems to evaluate data on complex population movements, such as those captured by mobile geolocation data, in a way that analytically identifies, e.g., critical spatial areas, critical temporal segments, and potentially vulnerable individuals with respect to contact events. [ABSTRACT FROM AUTHOR]

The geolocation accuracy of Synthetic Aperture Radar (SAR) images is crucial for their application in various industries. Five high-resolution SAR satellites, namely ALOS, TerraSAR-X, Cosmo-SkyMed, RadarSat-2, and Chinese YG-3, provide a vast amount of image data for research purposes, although their geometric accuracies differ despite similar resolutions. To evaluate and compare the geometric accuracy of these satellites under the same ground control reference, a validation field was established in Xianning, China. The rational function model (RFM) was used to analyze the geometric performance of the five satellites based on the Xianning validation field. The study showed that each image could achieve sub-pixel positioning accuracy in range and azimuth direction when four ground control points (GCPs) were placed in the corners, resulting in a root mean square error (RMSE) of 1.5 pixels. The study also highlighted the effectiveness of an automated GCP-matching approach to mitigate manual identification of points in SAR images, and results demonstrate that the five SAR satellite images can all achieve sub-pixel positioning accuracy in range and azimuth direction when four GCPs are used. Overall, the verification results provide a reference for SAR satellite systems' designs, calibrations, and various remote sensing activities. [ABSTRACT FROM AUTHOR]

When does the Dormant Commerce Clause preclude states from regulating internet activity--whether through state libel law or invasion of privacy law; through state laws requiring websites to accommodate disabled users (for instance, by providing closed captioning); through state bans on discriminating based on sexual orientation, religion, or criminal record; or through state laws that ban social media platforms from discriminating based on the viewpoint of users' speech? This Article argues that the constitutionality of such state regulation should generally turn on the feasibility of geolocation--the extent to which websites or other internet services can determine, reliably and inexpensively, which states users are coming from so that the sites can then apply the proper state law to each user (or, if need be, choose not to allow access to users from certain states). In recent years, geolocation has become feasible and is routinely used by major websites for ordinary business purposes. There is therefore more constitutional room for state regulation of internet services, including social media platforms, than often believed. [ABSTRACT FROM AUTHOR]

Over the past decades, tracking technologies have become more ubiquitous and helped uncover crucial spatiotemporal relationships in nature. In order to apply these technologies to small animals and reduce any potential adverse impact of devices, geopositioning methodologies compatible with lightweight devices are highly sought after. Measured by lightweight geolocators, atmospheric pressure provides an untapped opportunity for global geopositioning, as its natural temporal variation is unique to each location.In this study, we estimate the position of birds by comparing pressure data recorded by the geolocator with reference data from a global weather reanalysis database. The method produces a likelihood map of the position based on (1) a mask of the locations for which the ground‐level elevation matches the pressure measured by the geolocator and (2) a mismatch between the temporal time series measured by the geolocator and the reanalysis dataset. This new method is introduced step by step and applied to 16 tracks of nine long‐ and short‐distance migrant species.Using known positions of double‐tagged individuals (light and pressure data), we demonstrate that our method is almost three times more accurate than light‐based positioning with an average error of 44 km in our trials. In contrast to the traditional light‐based approach, pressure geolocation can provide useful information for short stationary periods (less than a day) and is not affected by the equinox problem nor by any shading effects due to weather or animal behaviour. To facilitate the application of the method, we developed an R package GeoPressureR, together with a user guide GeopressureManual and starting code GeoPressureTemplate.The use of pressure sensors to position animals has the potential to become widespread thanks to the combination of both affordable lightweight devices (<0.4 g) and this method to estimate position of the device precisely and accurately. In particular, such devices can now be applied to short‐distant migrants (>100 km), forest‐dwelling species, nocturnal animals and altitudinal migrants. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Information Technologies & Communications / Revista de Tecnologías de la Información & Comunicaciones is the property of ECORFAN-Mexico S.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research that audited search algorithms typically deployed queries in one language fielded from within only one country. In contrast, this study scrutinized 8,800 Google results retrieved in November 2020 from 5 countries (Russia, the United States, Germany, Ukraine, and Belarus) in response to queries on COVID-19 conspiracy theories in Russian and English. We found that the pandemic appeared similar to people who googled in Russian independent of their geolocation. The only exception was Ukraine, which had implemented rigorous media policies to limit the reach of websites affiliated with Russia within its national public sphere. Conspiracy narratives varied with input language. In response to Russian-language queries, 35.5% of the conspiratorial results suspected U.S. plotters to be behind the pandemic (English language: 5.8%). All source pages that blamed U.S. plotters showed connections with Russia's elites. These findings raise important theoretical questions for today's multilingual societies, where the practice of searching in nonlocal languages is increasing. [ABSTRACT FROM AUTHOR]

Copyright of SituArte is the property of Universidad del Zulia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobile applications provide marketers with a very specific outlook on their followers as they have the ability to pull direct demographic and geo-location data. This allows marketers to align their audience closely to their marketing and advertising efforts which ultimately helps marketers get the most value out of their ad campaigns. Ultimately, in-app advertising offers countless marketing possibilities for advertisers to explore. However, on the other end, users appear to be adopting techniques to avoid unnecessary target marketing. In this connection, theory of planned behavior(TPB), theory of reasoned action (TRA) and theory of acceptance model (TAM) were employed with the aim to develop a conceptual model of individual attitude towards In-app mobile advertising. Nexus to the phenomenon, four variables (advertising utility, irritation, trust and privacy concerns) as independent variables were selected from the literature. Consequently, permission based advertising has been applied as moderating variable to test the moderating effect of permission in between the relationship of independent variables and dependent variable. Examining the variables that might enhance users' attitudes toward in-app advertisements and lead to increased ad acceptance, this research study seeks to address the problem of ad avoidance. In this research, views toward in-app mobile advertising can be investigated with the help of direct effects of independent variables and moderating effect of permission based advertising in enhancing the advertising utilities, and trust while reducing irritation and privacy worries. Nexus to the adopted methodology for the current research study, an explanatory research study can be conducted to test eight hypotheses. Given the present situation of country, the elections are just around the corner and that the political parties and their followers presumably are the best targeted sample for the current study. [ABSTRACT FROM AUTHOR]

Research Summary: We examine the effect of faceto-face interactions between acquirers and targets before the acquisition announcements on acquisition returns. We argue that frequent interactions increase the target management's trust in the acquirer and benefit the acquirer by mitigating competition in the bidding process. For a sample of U.S. domestic acquisitions, we use smartphone geolocational data to measure the movement of people between merging companies in the months before the announcement. We find that with more frequent interactions, acquirers earn higher stock market returns at the announcement and targets receive fewer later bids from other bidders. Moreover, more frequent interactions are associated with lower returns to public targets vis-à-vis their acquirers. The effect of interactions is weaker when shareholder-manager agency problems in the target are less severe. Managerial Summary: Previous research shows that while acquisitions can create synergistic gains, the presence of potentially competing bidders forces acquirers to pay a high price for their targets, which makes acquisitions generally unprofitable for acquirers. We provide evidence suggesting that frequent social interactions between the acquirer's and the target's management in the pre-acquisition phase increase the target management's trust in the acquirer, making it more willing to cede control to the acquirer and less eager to seek alternative bidders. By mitigating competition in the bidding process, social interactions make acquisitions more profitable for acquirers vis-àvis targets. Social interactions are less effective when the target's management owns a larger share of the target or is better monitored by shareholders (e.g., in companies with concentrated ownership or private companies). [ABSTRACT FROM AUTHOR]

This article approaches aerial images from a media geographical perspective, tracing the development of three modes of seeing from above: historical aerial reconnaissance before and after the introduction of aerial photography; the advent of satellite-based remote sensing during the space age; and finally the development of drone-based imaging practices in the second half of the 20th century. Starting from the assumption that the aerial image is not a geomedium per se, but rather one that emerges as such through various co-operative processes of establishing geographical references with and within it, we draw on Goodwin's notions of "co-operative action" and Farocki's concept of "operative imagery" to theorize useful aerial images as co-operative images. Consequently, our historical analysis looks beyond the aerial image itself, taking into account the wider practices, infrastructures, and intended use cases involved in its operationalization. [ABSTRACT FROM AUTHOR]

The pan‐tilt‐zoom (PTZ) cameras are increasingly popular in urban management and resource protection due to their omnidirectional coverage and reasonable cost. Traditionally, the research of monocular camera geolocation focuses on small‐range ones with a central projection camera under ideal conditions, making it challenging to meet the requirements of omnidirectional coverage and large‐range geolocation for PTZ cameras. In this article, a wild large‐range target omnidirectional geolocation (WLTOG) algorithm was proposed. Firstly, the WLTOG algorithm is used to transform the target in a single image to the camera's optical axis, thereby obtaining the camera posture value. After that, by using a high‐order overdetermined equation, it fits the nonlinear mapping relationship between multiple pairs of PTZ camera postures and the geospatial coordinates. At last, it realizes the bidirectional calculation between pixel coordinates and geospatial coordinates. The experimental PTZ camera is mounted on a communication tower in a wild environment. Therefore, this method can be utilized to construct an omnidirectional geolocation relationship that meets the requirements of omnidirectional geolocation within 945 m based on unknown accurate internal and external orientation elements. The WLTOG algorithm is conducive to the geometric measurement of video and GIS‐augmented video surveillance. [ABSTRACT FROM AUTHOR]

Atmospheric refraction is one of the most significant factors that affect the geolocation accuracy of high-resolution remote sensing images. However, most of the current atmospheric refraction correction methods based on empirical data neglect the spatiotemporal variation of pressure, temperature, and humidity of the atmosphere, inevitably resulting in poor geometric positioning accuracy. Therefore, in terms of the problems mentioned above, this study proposed a spatiotemporal atmospheric refraction correction method (SARCM) based on global measured data to avoid the uncertainty of traditional empirical models. Initially, the atmosphere was stratified into 42 layers according to their pressure property, and each layer was divided into 1,042,560 grid cells with intervals of 0.25 longitude and 0.25 latitude. Then, the atmospheric refractive index of each grid in the imaging region was accurately calculated using the high-precision Ciddor formula, and the result was interpolated using three splines. Subsequently, according to the rigorous geometric positioning model, the line-of-sight of each pixel and the viewing zenith angle outside the atmosphere in WGS84 were derived to provide input for atmospheric refraction correction. Finally, the coordinates of the ground control points were corrected with the calculated atmospheric refractive index and Snell's law. The experimental results showed that the proposed SARCM could effectively improve the positioning accuracy of the image with a large viewing zenith angle, and especially, the improvement percentage for a viewing zenith angle of 34.2426° in the x-direction was 99.5%. Moreover, the atmospheric refraction correction result of the SARCM was better than that of the primary state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objectives: Ultrasound is useful in predicting arteriovenous fistula (AVF) maturation, which is essential for hemodialysis in end‐stage renal disease patients. We developed ultrasound software that measures circumferential vessel wall strain (distensibility) using conventional ultrasound Digital Imaging and Communications in Medicine (DICOM) data. We evaluated user‐induced variability in measurement of arterial wall distensibility and upon finding considerable variation we developed and tested 2 methods for semiautomated measurement. Methods: Ultrasound scanning of arteries of 10 subjects scheduled for AVF surgery were performed. The top and bottom of the vessel wall were tracked using the Kanade‐Lucas‐Tomasi (KLT) feature‐tracking algorithm over the stack of images in the DICOM cine loops. The wall distensibility was calculated from the change of vessel diameter over time. Two semiautomated methods were used for comparison. Results: The location of points selected by users for the cine loops varied significantly, with a maximum spread of up to 120 pixels (7.8 mm) for the top and up to 140 pixels (9.1 mm) for the bottom of the vessel wall. This variation in users' point selection contributed to the variation in distensibility measurements (ranging from 5.63 to 41.04%). Both semiautomated methods substantially reduced variation and were highly correlated with the median distensibility values obtained by the 10 users. Conclusions: Minimizing user‐induced variation by standardizing point selection will increase reproducibility and reliability of distensibility measurements. Our recent semiautomated software may help expand use in clinical studies to better understand the role of vascular wall compliance in predicting the maturation of fistulas. [ABSTRACT FROM AUTHOR]

Geo-parsing, one of the key components of geographical information retrieval, is a process to recognize and geo-locate toponyms mentioned in texts. Such a process can obtain locations contained in toponyms successfully with consistent updating of neural network models and multiple contextual features. The significant offset distance between the geo-parsed locations and the actual occurrence locations still remains. This is because the geo-parsed locations sourced from toponyms in texts always point to the centers of cities, counties, or towns, and cannot directly represent the actual occurrence locations such as factories, farms, and activity areas. Consequently, The significant offset distances between the geo-parsed locations and the actual occurrence locations limit text mining applications in micro-scale geographic discoveries. This research aims at decreasing offset distances of geo-parsed locations by proposing a novel Toponym Correction Method based on satellite Remote Sensing Images (TC-RSI). The TC-RSI method uses satellite remote sensing images to provide extra detailed spatial information that can be associated with the sentence toponym by corresponding attributes. The TC-RSI method was validated in a case study of the forest ecological pattern dataset of An'hui province from visual, statistical, and robustness assessments. The correction results show that the TC-RSI method dramatically decreases the offset distances from about 50 km to about 1 km and promotes geographical discoveries on smaller scales. A series of analyses indicated that the TC-RSI is a valid, effective, and promising method to improve the accuracy of geo-parsed locations, which allows text mining to find more accurate geographical discoveries with lower offset distances. Moreover, toponym correction promotes the use of more diverse spatial data sources, such as Lidar, domain gazetteers, Wikimedia, and streetscapes, which are expected to usher in a new era of geo-parsing with toponym corrections. [ABSTRACT FROM AUTHOR]

BACKGROUND: Most studies focus on the geographically larger production areas in tea traceability. However, famous high‐quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier‐transform near‐infrared (FT‐NIR) spectroscopy. RESULTS: First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over‐sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre‐processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre‐processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set. CONCLUSION: The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The development of the low dislocation density of the Si-based GaAs buffer is considered the key technical route for realizing InAs/GaAs quantum dot lasers for photonic integrated circuits. To prepare the high-quality GaAs layer on the Si substrate, we employed an engineered Ge-buffer on Si, used thermal cycle annealing, and introduced filtering layers, e.g., strained-layer superlattices, to control/reduce the threading dislocation density in the active part of the laser. In this way, a low defect density of 2.9 × 107 cm−2 could be achieved in the GaAs layer with a surface roughness of 1.01 nm. Transmission electron microscopy has been applied to study the effect of cycling, annealing, and filtering layers for blocking or bending threading-dislocation into the InAs QDs active region of the laser. In addition, the dependence of optical properties of InAs QDs on the growth temperature was also investigated. The results show that a density of 3.4 × 1010 InAs quantum dots could be grown at 450 °C, and the photoluminescence exhibits emission wavelengths of 1274 nm with a fullwidth at half-maximum (FWHM) equal to 32 nm at room temperature. The laser structure demonstrates a peak at 1.27 μm with an FWHM equal to 2.6 nm under a continuous-wave operation with a threshold current density of ∼158 A/cm2 for a 4-μm narrow-ridge width InAs QD device. This work, therefore, paves the path for a monolithic solution for photonic integrated circuits when III−V light sources (which is required for Si photonics) are grown on a Ge-platform (engineered Ge-buffer on Si) for the integration of the CMOS part with other photonic devices on the same chip in near future. [ABSTRACT FROM AUTHOR]

Forensic microbiology is a relatively new area of forensic sciences. It considers the potential of microorganisms to be used in criminal investigations. As most studies involve the role of bacteria in fields like post-mortem interval estimation, personal identification or geolocation, the data on the role of fungi is comparatively scarce. Forensic mycology involves the application of fungi and their structures in forensic cases. The aim of this review is the evaluation of the current state of knowledge on fungi associated with human cadavers and their possible role in estimating the time since death. In accordance with the available reports, we focused on the relation between microscopic fungi isolated from human corpses and the cadaver condition e.g., the stage of decomposition. We also emphasised the contrast between the reported methodologies and attempted to standardise research methods in forensic mycology from sample collection to its storage, mycological analysis and identification of the obtained fungal cultures. Moreover, the potential usage of microscopic fungi in criminal cases was discussed based on various case reports. • A proposal for a standardized methodology in forensic mycology. • Comparison case reports on fungi isolated from cadavers. • Analysis of fungi associated with cadavers at different stages of decomposition. • History of forensic mycology. [ABSTRACT FROM AUTHOR]

The Directional Polarimetric Camera (DPC) carried by the Chinese GaoFen-5-02 (GF-5-02) satellite has the ability for multiangle, multispectral, and polarization detection and will play an important role in the inversion of atmospheric aerosol and cloud characteristics. To ensure the validity of the DPC on-orbit multiangle and multispectral polarization data, high-precision image registration and geolocation are vital. High-precision geometric model parameters are a prerequisite for on-orbit image registration and geolocation. Therefore, on the basis of the multiangle imaging characteristics of DPC, an on-orbit autonomous geometric calibration method without ground reference data is proposed. The method includes three steps: (1) preprocessing the original image of the DPC and the satellite attitude and orbit parameters; (2) scale-invariant feature transform (SIFT) algorithm to match homologous points between multiangle images; (3) optimization of geometric model parameters on-orbit using least square theory. To verify the effectiveness of the on-orbit autonomous geometric calibration method, the image registration performance and relative geolocation accuracy before and after DPC on-orbit geometric calibration were evaluated and analyzed using the SIFT algorithm and the coastline crossing method (CCM). The results show that the on-orbit autonomous geometric calibration effectively improves the DPC image registration and relative geolocation accuracy. After on-orbit calibration, the multiangle image registration accuracy is better than 1.530 km, the multispectral image registration accuracy is better than 0.650 km, and the relative geolocation accuracy is better than 1.275 km, all reaching the subpixel level (<1.7 km). [ABSTRACT FROM AUTHOR]

CNN-based hashing methods have greatly boosted the performance of image retrieval, under the strong supervision of large amounts of manually annotated labels. In recent years, a large number of social media images with user tags have been generated on the Internet. These images can be regarded as weakly labeled training data, which can provide rich samples for training hash network, and greatly reduce the cost of obtaining training data. However, there are noise and visual irrelevant tags in user tags, and different tags may describe different objects in the image. In the previous CNN-based hashing method, a training image usually corresponds to a manual label and generates a hash code. These methods are difficult to use the image described by user tags with noise. For solving the above problem, we propose a CNN-based objectness hash learning method using user tags as a guide for training. First of all, the user tags are roughly filtered to remove noise tags that are not related to the visual content of images. Secondly, we quantify user tags into a unified semantic space and extract the highest-frequency words of the semantic space from similar objectness areas as their labels. Then, these objectness areas with their labels are grouped into a series of triple units as training data. So that the generated hash code can inherit the semantic similarity of the objectness areas well, that is, the Hamming distance between hash codes generated by similar objectness areas is closer, the reverse is the farther. Experimental results on NUS-WIDE and Flickr datasets show that our method can effectively extract object-level semantic information from weak user tags, and improve the accuracy of image retrieval. [ABSTRACT FROM AUTHOR]

As personalised immersive display systems have been intensely explored in virtual reality (VR), plausible 3D audio corresponding to the visual content is required to provide more realistic experiences to users. It is well known that spatial audio synchronised with visual information improves a sense of immersion but limited research progress has been achieved in immersive audio-visual content production and reproduction. In this paper, we propose an end-to-end pipeline to simultaneously reconstruct 3D geometry and acoustic properties of the environment from a pair of omnidirectional panoramic images. A semantic scene reconstruction and completion method using a deep convolutional neural network is proposed to estimate the complete semantic scene geometry in order to adapt spatial audio reproduction to the scene. Experiments provide objective and subjective evaluations of the proposed pipeline for plausible audio-visual VR reproduction of real scenes. [ABSTRACT FROM AUTHOR]

The accurate spatial positioning of the target in a fixed camera image is a critical sensing technique. Conventional visual spatial positioning methods rely on tedious camera calibration and face great challenges in selecting the representative feature points to compute the position of the target, especially when existing occlusion or in remote scenes. In order to avoid these deficiencies, this paper proposes a deep learning approach for accurate visual spatial positioning of the targets with the assistance of Global Navigation Satellite System (GNSS). It contains two stages: the first stage trains a hybrid supervised and unsupervised auto-encoder regression network offline to gain capability of regressing geolocation (longitude and latitude) directly from the fusion of image and GNSS, and learns an error scale factor to evaluate the regression error. The second stage firstly predicts regressed accurate geolocation online from the observed image and GNSS measurement, and then filters the predictive geolocation and the measured GNSS to output the optimal geolocation. The experimental results showed that the proposed approach increased the average positioning accuracy by 56.83%, 37.25%, 41.62% in a simulated scenario and 31.25%, 7.43%, 38.28% in a real-world scenario, compared with GNSS, the Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) and the supervised deep learning approach, respectively. Other improvements were also achieved in positioning stability, robustness, generalization, and performance in GNSS denied environments. [ABSTRACT FROM AUTHOR]

Estimating vehicles' locations is one of the key components in intelligent traffic management systems (ITMSs) for increasing traffic scene awareness. Traditionally, stationary sensors have been employed in this regard. The development of advanced sensing and communication technologies on modern vehicles (MVs) makes it feasible to use such vehicles as mobile sensors to estimate the traffic data of observed vehicles. This study aims to explore the capabilities of a monocular camera mounted on an MV in order to estimate the geolocation of the observed vehicle in a global positioning system (GPS) coordinate system. We proposed a new methodology by integrating deep learning, image processing, and geometric computation to address the observed-vehicle localization problem. To evaluate our proposed methodology, we developed new algorithms and tested them using real-world traffic data. The results indicated that our proposed methodology and algorithms could effectively estimate the observed vehicle's latitude and longitude dynamically. [ABSTRACT FROM AUTHOR]

Spam annoys users and poses a security threat. This article proposes a spam filter based on geographical location of the sender determined by IP geolocation. This filter was implemented as a plugin to the SpamAssassin anti-spam software. The plugin allows to define a penalty score for specific countries sending spam. The proposed filter was tested on a dataset of 1500 e-mails consisting of 1200 spam and 300 legitimate e-mails. The Matthews correlation coefficient of the filter has a value of 0.222. This indicates that the proposed spam filter contributes to the correct spam filtering. [ABSTRACT FROM AUTHOR]

A two-dimensional visibility estimation model was developed, based on random forest (RF), a machine learning-based technique. A geostatistical method was introduced into the visibility estimation model for the first time to interpolate point measurement data to gridded data spatially with a pixel size of 10 km. The RF-based model was trained using gridded visibility data, as well as meteorological and air pollution input variable data, for each location in South Korea, which were characterized by complex geographical features and high air pollution levels. Generally, relative humidity was the most important input variable for the visibility estimation (average mean decrease accuracy: 35%). However, PM2.5 tended to be the most crucial variable in polluted regions. The spatial interpolation was found to result in an additional visibility estimation error of 500 m in locations where no adjacent visibility observations within 0.2° were available. The performance of the proposed model was preliminarily assessed. Generally, the best detection performance was achieved in good visibility conditions (visibility range: 10 to 20 km). This study is the first to demonstrate a visibility estimation model based on a geostatistical method and machine learning, which can provide visibility information in locations for which no observations exist. [ABSTRACT FROM AUTHOR]

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) was a limb-viewing infrared Fourier transform spectrometer that operated from 2002 to 2012 aboard the Environmental Satellite (ENVISAT). The final re-processing of the full MIPAS mission Level 2 data was performed with the ESA operational version 8 (v8) processor. This MIPAS data set not only includes retrieval results of pressure-temperature and the standard species H2O, O3, HNO3, CH4, N2O, and NO2, but also vertical profiles of volume mixing ratios of the more difficult to retrieve molecules N2O5, ClONO2, CFC-11, CFC-12 (included since v6 processing), HCFC-22, CCl4, CF4, COF2, and HCN (included since v7 processing). Finally, vertical profiles of the species C2H2, C2H6, COCl2, OCS, CH3Cl, and HDO were additionally retrieved by the v8 processor. The balloon-borne limb-emission sounder MIPAS-B was a precursor of the MIPAS satellite instrument. Several flights with MIPAS-B have been carried out during the 10 years operational phase of ENVISAT at different latitudes and seasons, including both operational periods where MIPAS measured with full spectral resolution (FR mode) and with optimized spectral resolution (OR mode). All MIPAS operational products (except HDO) were compared to results inferred from dedicated validation limb sequences of MIPAS-B. To enhance the statistics of vertical profile comparisons, a trajectory match method has been applied to search for MIPAS coincidences along 2-day forward/backward trajectories running from the MIPAS-B measurement geolocations. This study gives an overview of the validation results based on the ESA operational v8 data comprising the MIPAS FR and OR observation periods. This includes an assessment of the data agreement of both sensors taking into account combined errors of the instruments. The difference between retrieved temperature profiles of both MIPAS instruments generally stays within ±2 K in the stratosphere. For most gases, namely H2O, O3, HNO3, CH4, N2O, NO2, N2O5, ClONO2, CFC-11, CFC-12, HCFC-22, CCl4, CF4, COF2, and HCN we find a 5 % to 20 % agreement of the retrieved vertical profiles of both MIPAS instruments in the lower stratosphere. For the species C2H2, C2H6, COCl2, OCS, and CH3Cl, however, larger differences within 20 % and 50 % appear in this altitude range. [ABSTRACT FROM AUTHOR]

Summary: To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near‐infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS‐DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross‐validation of 0.0261. PLS‐DA model of mean centre and first‐order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post‐harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices. [ABSTRACT FROM AUTHOR]

Geolocation is one of the key synthetic aperture radar (SAR) image processing procedures in SAR-based remote-sensing applications. The conventional SAR imagery geolocation model is based on the range-Doppler (RD) equation, which is derived from the Fresnel approximation of SAR. Currently, there is no other rigorous geolocation model for modern high-resolution SAR; however, this approximation may not be fully applicable for various modern SAR imaging modes and algorithms. This study aims to develop a novel SAR image geolocation model based on the SAR range history (RH) equation, which is derived from the basic SAR backscattered signal property. Moreover, a new geometric calibration model is presented for RH-based locations. Experiments based on real spaceborne SAR data are presented as examples, and the location accuracy of RH and RD location models are compared. The results show that the location accuracies of the RH and RD models are consistent under the existing SAR processing framework, but those for the RH model demonstrate better performance in the azimuth direction. In theory, the proposed RH model can be used for SAR image products generated from complex modes and non-Fresnel-approximation-based imaging algorithms, making it suitable for future SAR systems. [ABSTRACT FROM AUTHOR]