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In recent decades, the field of statistical linguistics has made significant strides, which have been fueled by the availability of data. Leveraging Twitter data, this paper explores the English and Spanish languages, investigating their rank diversity across different scales: temporal intervals (ranging from 3 to 96 h), spatial radii (spanning 3 km to over 3000 km), and grammatical word ngrams (ranging from 1-grams to 5-grams). The analysis focuses on word ngrams, examining a time period of 1 year (2014) and eight different countries. Our findings highlight the relevance of all three scales with the most substantial changes observed at the grammatical level. Specifically, at the monogram level, rank diversity curves exhibit remarkable similarity across languages, countries, and temporal or spatial scales. However, as the grammatical scale expands, variations in rank diversity become more pronounced and influenced by temporal, spatial, linguistic, and national factors. Additionally, we investigate the statistical characteristics of Twitter-specific tokens, including emojis, hashtags, and user mentions, revealing a sigmoid pattern in their rank diversity function. These insights contribute to quantifying universal language statistics while also identifying potential sources of variation. [ABSTRACT FROM AUTHOR]

In this paper a comparative study, restricted to one-dimensional stationary case, between several Direction of Arrival (DOA) estimation algorithms of narrowband signals is presented. The informative signals are corrupted by an Additive White Gaussian Noise (AWGN), to show the performance of each method by applying directly the algorithms without pre-processing techniques such as forward-backward averaging or spatial smoothing. [ABSTRACT FROM AUTHOR]

Introduction: Cocaine abuse represents a major public health concern. The social perception of cocaine has been changing over the decades, a phenomenon closely tied to its patterns of use and abuse. Twitter is a valuable tool to understand the status of drug use and abuse globally. However, no specific studies discussing cocaine have been conducted on this platform. Methods: 111,508 English and Spanish tweets containing "cocaine" from 2018 to 2022 were analyzed. 550 were manually studied, and the largest subset underwent automated classification. Then, tweets related to cocaine were analyzed to examine their content, types of Twitter users, usage patterns, health effects, and personal experiences. Geolocation data was also considered to understand regional differences. Results: A total of 71,844 classifiable tweets were obtained. Among these, 15.95% of users discussed the harm of cocaine consumption to health. Media outlets had the highest number of tweets (35.11%) and the most frequent theme was social/ political denunciation (67.88%). Regarding the experience related to consumption, there are more tweets with a negative sentiment. The 9.03% of tweets explicitly mention frequent use of the drug. The continent with the highest number of tweets was America (55.44% of the total). Discussion: The findings underscore the significance of cocaine as a current social and political issue, with a predominant focus on political and social denunciation in the majority of tweets. Notably, the study reveals a concentration of tweets from the United States and South American countries, reflecting the high prevalence of cocaine-related disorders and overdose cases in these regions. Alarmingly, the study highlights the trivialization of cocaine consumption on Twitter, accompanied by a misleading promotion of its health benefits, emphasizing the urgent need for targeted interventions and antidrug content on social media platforms. Finally, the unexpected advocacy for cocaine by healthcare professionals raises concerns about potential drug abuse within this demographic, warranting further investigation. [ABSTRACT FROM AUTHOR]

The geolocalization and navigation technology of unmanned aerial vehicles (UAVs) in denied environments is currently a prominent research area. Prior approaches mainly employed a two-stream network with nonshared weights to extract features from UAV and satellite images separately, followed by related modeling to obtain the response map. However, the two-stream network extracts UAV and satellite features independently. This approach significantly affects the efficiency of feature extraction and increases the computational load. To address these issues, we propose a novel coarse-to-fine one-stream network. Our approach allows information exchange between UAV and satellite features during early image feature extraction. To improve the model's performance, the framework retains feature maps generated at different stages of the feature extraction process for the feature fusion network and establishes additional connections between UAV and satellite feature maps in the feature fusion network. In addition, the framework introduces offset prediction to further refine and optimize the model's prediction results based on the classification tasks. Our proposed model boasts a similar inference speed to FPI while significantly reducing the number of parameters. It can achieve better performance with fewer parameters under the same conditions. Moreover, it achieves state-of-the-art performance on the UL14 dataset. Compared with previous models, our model achieved a significant 10.92-point improvement on the RDS metric, reaching 76.25. Furthermore, its performance in meter-level localization accuracy is impressive, with 82.62% improvement in 3-m accuracy, 64.17% improvement in 5-m accuracy, and 37.43% improvement in 10-m accuracy.

Satellite-Drone Image Cross-View Geolocalization has wide applications. Due to the pronounced variations in the visual features of 3D objects under different angles, Satellite-Drone cross-view image geolocalization remains an unresolved challenge. The key to successful cross-view geolocalization lies in extracting crucial spatial structure information across different scales in the image. Recent studies improve image matching accuracy by introducing an attention mechanism to establish global associations among local features. However, existing methods primarily focus on using single-scale features and employ a single-channel attention mechanism to correlate local convolutional features from different locations. This approach inadequately explores and utilizes multi-scale spatial structure information within the image, particularly lacking in the extraction and utilization of locally valuable information. In this paper, we propose a cross-view image geolocalization method based on multi-scale information and a dual-channel attention mechanism. The multi-scale information includes features extracted from different scales using various convolutional slices, and it extensively utilizes shallow network features. The dual-channel attention mechanism, through successive local and global feature associations, effectively learns depth discriminative features across different scales. Experimental results were conducted using existing satellite and drone image datasets, with additional validation performed on an independent self-made dataset. The findings indicate that our approach exhibits superior performance compared to existing methods. The methodology presented in this paper exhibits enhanced capabilities, especially in the exploitation of multi-scale spatial structure information and the extraction of locally valuable information. [ABSTRACT FROM AUTHOR]

In recent decades, the field of statistical linguistics has made significant strides, which have been fueled by the availability of data. Leveraging Twitter data, this paper explores the English and Spanish languages, investigating their rank diversity across different scales: temporal intervals (ranging from 3 to 96 h), spatial radii (spanning 3 km to over 3000 km), and grammatical word ngrams (ranging from 1-grams to 5-grams). The analysis focuses on word ngrams, examining a time period of 1 year (2014) and eight different countries. Our findings highlight the relevance of all three scales with the most substantial changes observed at the grammatical level. Specifically, at the monogram level, rank diversity curves exhibit remarkable similarity across languages, countries, and temporal or spatial scales. However, as the grammatical scale expands, variations in rank diversity become more pronounced and influenced by temporal, spatial, linguistic, and national factors. Additionally, we investigate the statistical characteristics of Twitter-specific tokens, including emojis, hashtags, and user mentions, revealing a sigmoid pattern in their rank diversity function. These insights contribute to quantifying universal language statistics while also identifying potential sources of variation.

IntroductionCocaine abuse represents a major public health concern. The social perception of cocaine has been changing over the decades, a phenomenon closely tied to its patterns of use and abuse. Twitter is a valuable tool to understand the status of drug use and abuse globally. However, no specific studies discussing cocaine have been conducted on this platform.Methods111,508 English and Spanish tweets containing “cocaine” from 2018 to 2022 were analyzed. 550 were manually studied, and the largest subset underwent automated classification. Then, tweets related to cocaine were analyzed to examine their content, types of Twitter users, usage patterns, health effects, and personal experiences. Geolocation data was also considered to understand regional differences.ResultsA total of 71,844 classifiable tweets were obtained. Among these, 15.95% of users discussed the harm of cocaine consumption to health. Media outlets had the highest number of tweets (35.11%) and the most frequent theme was social/political denunciation (67.88%). Regarding the experience related to consumption, there are more tweets with a negative sentiment. The 9.03% of tweets explicitly mention frequent use of the drug. The continent with the highest number of tweets was America (55.44% of the total).DiscussionThe findings underscore the significance of cocaine as a current social and political issue, with a predominant focus on political and social denunciation in the majority of tweets. Notably, the study reveals a concentration of tweets from the United States and South American countries, reflecting the high prevalence of cocaine-related disorders and overdose cases in these regions. Alarmingly, the study highlights the trivialization of cocaine consumption on Twitter, accompanied by a misleading promotion of its health benefits, emphasizing the urgent need for targeted interventions and antidrug content on social media platforms. Finally, the unexpected advocacy for cocaine by healthcare professionals raises concerns about potential drug abuse within this demographic, warranting further investigation.

This paper reviews some general information on the pomegranate Punica granatum, focusing on its history, geolocalization, symbols in different world cultivations, and improvement techniques of some existing varieties. [ABSTRACT FROM AUTHOR]

One of the challenges of spatial cognition, such as self-localization and navigation, is to develop an efficient learning approach capable of mimicking human ability. This paper proposes a novel approach for topological geolocalization on the map using motion trajectory and graph neural networks. Specifically, our learning method learns an embedding of the motion trajectory encoded as a path subgraph where the node and edge represent turning direction and relative distance information by training a graph neural network. We formulate the subgraph learning as a multi-class classification problem in which the output node IDs are interpreted as the object's location on the map. After training using three map datasets with small, medium, and large sizes, the node localization tests on simulated trajectories generated from the map show 93.61%, 95.33%, and 87.50% accuracy, respectively. We also demonstrate similar accuracy for our approach on actual trajectories generated by visual-inertial odometry. The key benefits of our approach are as follows: (1) we take advantage of the powerful graph-modeling ability of neural graph networks, (2) it only requires a map in the form of a 2D graph, and (3) it only requires an affordable sensor that generates relative motion trajectory. [ABSTRACT FROM AUTHOR]

Satellite-Drone Image Cross-View Geolocalization has wide applications. Due to the pronounced variations in the visual features of 3D objects under different angles, Satellite-Drone cross-view image geolocalization remains an unresolved challenge. The key to successful cross-view geolocalization lies in extracting crucial spatial structure information across different scales in the image. Recent studies improve image matching accuracy by introducing an attention mechanism to establish global associations among local features. However, existing methods primarily focus on using single-scale features and employ a single-channel attention mechanism to correlate local convolutional features from different locations. This approach inadequately explores and utilizes multi-scale spatial structure information within the image, particularly lacking in the extraction and utilization of locally valuable information. In this paper, we propose a cross-view image geolocalization method based on multi-scale information and a dual-channel attention mechanism. The multi-scale information includes features extracted from different scales using various convolutional slices, and it extensively utilizes shallow network features. The dual-channel attention mechanism, through successive local and global feature associations, effectively learns depth discriminative features across different scales. Experimental results were conducted using existing satellite and drone image datasets, with additional validation performed on an independent self-made dataset. The findings indicate that our approach exhibits superior performance compared to existing methods. The methodology presented in this paper exhibits enhanced capabilities, especially in the exploitation of multi-scale spatial structure information and the extraction of locally valuable information.

Challenges connected with neuroscience and the use of machine learning to support analytical processes encompass more and more areas, thus supporting practitioners and managerial decisions. These changes can also be seen in the area of human resource management and support for decisions on key future spending on the remuneration of future employees. The article presents an original spatial data enrichment and spatial data mining methodology used for the analysis of primary data based on a sample of 1149 young candidates from generation Z to measure the effectiveness of data mining learning methods. The studies used data collected directly from surveys that were "enriched" with spatial geolocation. The fact that the spatial context was taken into account in the studies made it possible to develop a model explaining the spatio-temporal differentiation of professional expectations of respondents from generation Z who were studying professions connected with broadly understood IT. The analyzes used modeling with linear polynomial regression, the neural network of a multi-layer perceptron type and the multivariate adaptive regression splines method in the variant with and without spatial data filtration. The use of different spatial data mining methods made it possible to compare the reliability of models of knowledge extraction from the data and to explain the significance of individual factors which affected the respondents' beliefs. The analysis shows that spatial filtering of the data generates twice lower mean squared error while effective application of machine learning methods requires the use of explanatory spatial data. [ABSTRACT FROM AUTHOR]

Recently, the fine‐grained geolocalization of User‐Generated Short Text (UGST) has been increasingly attracting much attention. Accurate geolocation can benefit many applications, especially for the location‐based services. However, since the majority of UGSTs are short, noisy and not geotagged, existing methods still suffer from two issues. One is the heavy reliance on the manual features not fully exploiting the semantic information. Another is the free writing style of social media resulting in extremely few useful geo‐indicative information. To address these issues, we propose a novel Fine‐grained Geolocalization method for UGSTs with Preprocessing, location‐entity consistency Replacing, Filtering, Convolutional neural network (FG‐PRFC), which only relies on UGST itself. Compared to existing methods, FG‐PRFC has four unique characteristics: (1) We present a UGST‐oriented preprocessing method to obtain more semantic information. (2) To tackle the abbreviation issue, we develop a replacing method to allow geo‐indicative words behaving in the same way. (3) Following the idea of TFIDF, we weight the words in UGST and then develop a location‐free UGST filtering method. (4) We employ convolutional neural network to model the relationship between words and locations. Extensive experiments on three ground‐truth datasets demonstrate that our method has a significant improvement compared to state‐of‐art methods. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The overall declining state of the western taxonomy is clearly documented by the present fate of the extremely high valued and huge unidentified caddisfly material collected in India and deposited in the Canadian National Collection. Due to the general lack of both financial and scientific resources in taxonomy, this huge collection remained almost untouched during the last 70 years after its collection. With a tribute to the collector, Fernand Schmid, the great caddisfly collector and taxonomist we have reconstructed his collecting activities of the five years in India by geolocalization and elaborated about ten percent of his undetermined Hydropsychidae in the Diplectroninae and Hydropsychinae subfamilies. Description of 145 new species from this small fragment of the huge unidentified collection demonstrates a pronounced underestimation of the biodiversity in general, routinely measured by gross phenomics. During this study we have reinstated the species status of seven taxa: Diplectrona kirimaduhela Schmid, 1958, stat. restit., Cheumatopsyche rienga Oláh & Johanson, 2008, stat. restit., Cheumatopsyche amurensis Martynov, 1934, stat. restit., Cheumatopsyche banksi Mosely, 1942, stat. restit., Cheumatopsyche processuata (Martynov, 1927), stat. restit., Potamyia klakahana (Ulmer, 1951), stat. restit., Potamyia proboscida Li & Tian, 1996, stat. restit., and raised the subspecies status to species rank of Diplectrona stigmatica Banks, 1937, stat. nov. Furthermore, we have established four new species groups, sixteen new species complexes and described 145 new species.

Time-dependent geolocalized analysis of pollution data allows to better understand their dynamics over time and could suggest strategies to restore a good ecological status of contaminated area. This research analyzes concentrations of pollutants in surface waters and groundwater monitored by the Regional Environment Protection Agency of Lombardy from 2017 to 2020. Lombardy is one of the richest and populous region of Europe, providing an interesting example of the impact of environmental pollutants due to anthropogenic and industrial activities, not only for Italy but also for all Europe. Results show that groundwater displays more sites with heavy metals above the legal limit with respect to surface waters, including As, Ni, Cr and Zn. Furthermore, the spatio-temporal analysis of the data clearly shows that the introduction of more restrictive laws is a proper policy to improve the ecological status of the water.

In recent years, many scientific institutions have digitized their collections, which often include a large variety of topographic raster maps. These raster maps provide accurate (historical) geographical information but cannot be integrated directly into a geographical information system (GIS) due to a lack of metadata. Additionally, the text labels on the map are usually not annotated, making it inefficient to query for specific toponyms. Manually georeferencing and annotating the text labels on these maps is not cost-effective for large collections. This work presents a fully automated georeferencing approach based on text recognition and geocoding pipeline. After recognizing the text on the maps, publicly available geocoders were used to determine a region of interest. The approach was validated on a collection of historical and contemporary topographic maps. We show that this approach can geolocate the topographic maps fairly accurately, resulting in an average georeferencing error of only 316 m (1.67%) and 287 m (0.90%) for 16 historical maps and 9 contemporary maps spanning 19 km and 32 km, respectively (scale 1:25,000 and 1:50,000). Furthermore, this approach allows the maps to be queried based on the recognized visible text and found toponyms, which further improves the accessibility and quality of the collection. [ABSTRACT FROM AUTHOR]

One of the challenges of spatial cognition, such as self-localization and navigation, is to develop an efficient learning approach capable of mimicking human ability. This paper proposes a novel approach for topological geolocalization on the map using motion trajectory and graph neural networks. Specifically, our learning method learns an embedding of the motion trajectory encoded as a path subgraph where the node and edge represent turning direction and relative distance information by training a graph neural network. We formulate the subgraph learning as a multi-class classification problem in which the output node IDs are interpreted as the object’s location on the map. After training using three map datasets with small, medium, and large sizes, the node localization tests on simulated trajectories generated from the map show 93.61%, 95.33%, and 87.50% accuracy, respectively. We also demonstrate similar accuracy for our approach on actual trajectories generated by visual-inertial odometry. The key benefits of our approach are as follows: (1) we take advantage of the powerful graph-modeling ability of neural graph networks, (2) it only requires a map in the form of a 2D graph, and (3) it only requires an affordable sensor that generates relative motion trajectory.

Recently, the fine-grained geolocalization of user-generated short text (UGST), which can benefit many location-based applications, has been attracting the attention of academica. The semantic information in UGST is seldom introduced in most existing work, which reduces the effectiveness of existing methods. To address this issue, we propose an entity-based fine-grained geolocalization of UGST, which consists of following steps. (1) We employ location-based social network to model the coupling between entities and locations, which can introduce much semantic information. (2) We extract entities from non-geotagged UGST, and discards this UGST if it has not location-related entities. Otherwise, (3) we utilize the built coupling model to rank the candidate locations for this UGST, and then select top $n$ locations as the result. The experiments demonstrate that our method shows marked improvement on $Accuracy\text{@}1km$ and average error distance compared to the state-of-the-art FRV, WMV and LW methods.

High-precision global navigation satellite system (GNSS) is a crucial geolocalization feature enabling a wide range of applications, from mobile Internet-of-Things devices to autonomous drones and self-driving vehicles. Real-time kinematic (RTK) is a GNSS technology that attracting increased interest due to the centimeter precision achievable when wireless communication is present on the devices. On the other hand, sending continuously wireless data increases the energy consumption and the cost of the solution, especially when communication is carried over the 4G network. Due to those drawbacks, RTK is not much exploited in the localization of battery-operated devices. This work combines RTK with low-power long-range communication to achieve submeter precision in an energy-efficient RTK-based system. The proposed system exploits a state-of-the-art RTK-GNSS module combined with a long range (LoRa) to achieve geolocalization with minimal wireless radio infrastructure requirements. An energy-efficient algorithm is proposed and implemented in a microcontroller to have a quick startup and high accuracy. We evaluate three different GNSS modules and compare their performance in terms of power and accuracy. Experimental results, with in-field measurements, show that an average geolocalization precision of tens of centimeters is achievable on a battery-operated wireless end node connected to a single base station used as a geostationary reference anchor placed at kilometers of distance. The peak precision measured is below 10 cm. [ABSTRACT FROM AUTHOR]

In this article, a road-network-based geolocalization method is proposed. We match roads in the onboard images to the reference road vector map, and realize successful localization over areas as large as a whole city. The road network matching problem is treated as a point cloud registration problem under the homography transformation and solved under the hypothesize-and-test framework. To tackle the point cloud registration problem, a global projective-invariant feature is proposed, which consists of two road intersections augmented with their tangents. In addition, we propose the necessary conditions for the features to match. This can reduce the candidate matching features, thus accelerating the search to a great extent. These matching candidates are first “filtered” with the model consistency check in parameter space and then tested with similarity metrics to identify the correct transformation. The experiments show that our method can localize an aerial image over an area larger than 1000 km2 within several seconds on a single CPU. Our code can be found at: https://github.com/FlyAlCode/RCLGeolocalization-2.0. [ABSTRACT FROM AUTHOR]

Recently, the fine-grained geolocalization of User-Generated Short Texts (UGST) has become increasingly important. One challenge is that UGST contains relatively little location-indicative information due to such limitations as text length. Therefore, extract and effectively use the location-indicative information is the key issue for improving the effect of geolocalization. The existing works only consider the global weight of the terms and do not distinguish between the importance of identical terms in different locations. In addition, the existing add-one smoothing masks the difference between the features of different locations. In this paper, we propose a fine-grained geolocalization method to predict the PoI-level location of UGSTs based on a weight probability model (FGST-WP). The method mainly includes three parts: 1) Using the reverse maximum match algorithm to filter out UGSTs that do not contain any location-indicative information. 2) Building coupling of terms and locations and adopting a mixed weight strategy to assign weights to terms. 3) Calculating the probability of nongeotagged UGST posted from each location and selecting k locations according to the top-k probabilities. The accuracy of FGST-WP on the three ground-truth datasets reaches 45%, 68%, and 72%, respectively. The results indicate the superior performance of FGST-WP.

In recent years, many scientific institutions have digitized their collections, which often include a large variety of topographic raster maps. These raster maps provide accurate (historical) geographical information but cannot be integrated directly into a geographical information system (GIS) due to a lack of metadata. Additionally, the text labels on the map are usually not annotated, making it inefficient to query for specific toponyms. Manually georeferencing and annotating the text labels on these maps is not cost-effective for large collections. This work presents a fully automated georeferencing approach based on text recognition and geocoding pipeline. After recognizing the text on the maps, publicly available geocoders were used to determine a region of interest. The approach was validated on a collection of historical and contemporary topographic maps. We show that this approach can geolocate the topographic maps fairly accurately, resulting in an average georeferencing error of only 316 m (1.67%) and 287 m (0.90%) for 16 historical maps and 9 contemporary maps spanning 19 km and 32 km, respectively (scale 1:25,000 and 1:50,000). Furthermore, this approach allows the maps to be queried based on the recognized visible text and found toponyms, which further improves the accessibility and quality of the collection.

Aim/purpose - The aim of the research is to study key location factors and changes in motives for business service offshoring. Design/methodology/approach - The empirical research was conducted on a sample of 200 managers employed in business service centers in Poland. Findings - The results of the research confirm human capital as the most important basis for the location selection of business-service offshoring to Poland. The study has revealed that the pursuit of cost reduction is currently the most important motive for the relocation of business services to Poland. It has been shown that the motives for business service offshoring undergo evolution. The importance of salary arbitrage is diminishing, while other motives are becoming increasingly crucial. In the near future, efficiency improvement and service quality are expected to become more important than cost reduction, while innovation improvement is expected to grow at the fastest rate. Research implications/limitations - In the global economy, the location of business services is becoming the subject of increasingly free choice. Transnational corporations transferring their business services implement various location strategies in search of specific advantages that reflect their expectations. The findings of the study could be extended to other transition economics. Originality/value/contribution - It is one of a few empirical studies on the evolution of motives for business service offshoring worldwide and the first performed in Poland, which has become an important host location for business service offshoring in Europe(original abstract)

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The estimation of geolocation for aerial images is significant for tasks like map creating, or automatic navigation for unmanned aerial vehicles (UAVs). We propose a novel geolocalization method for the UAVs using only aerial images and reference road map. The corresponding road maps of the aerial images are firstly merged into a whole mosaic image using our newly-designed aerial image mosaicking algorithm, where the relative homography transformations between road images are firstly estimated using keypoints tracking in RGB aerial images, and then further refined with registration between detected roads. The geolocalization of the aerial mosaic image is then taken as the problem of registering observed roads in the aerial images to the reference road map under the homography transformation. The registration problem is solved with our fast search algorithm based on a novel projective-invariant feature, which consists of two road intersections augmented with their tangents. Experiments demonstrate that the proposed method can localize the aerial image sequence over an area larger than 1000 km 2 within a few seconds. [ABSTRACT FROM AUTHOR]

Introduction: Widespread use of smartphones allows automatic geolocalization (i.e., transmission of location data) in countless apps. Until now, this technology has not been routinely used in connection with an emergency call in which location data play a decisive role This study evaluated a new software automatically providing emergency medical service (EMS) dispatchers with a caller's geolocation. We hypothesized that this technology will provide higher accuracy, faster dispatching of EMS and a faster beginning of thoracic compressions in a cardiac arrest scenario.Material and Methods: Approval from the local Ethics Committee was obtained. 108 simulated emergency calls reporting a patient in cardiac arrest were conducted at 54 metropolitan locations, which were chosen according to a realistic pattern. At each location, a conventional emergency call, with an oral description of the location, was given first; subsequently, another call using an app with automatic geolocation was placed. Accuracy of localization, time to location, time to EMS dispatch and time to first thoracic compression were compared between both groups.Results: The conventional emergency call was always successful (n = 54). Emergency call via app worked successfully in n = 46 cases (85.2%). Automatic geolocation was provided to EMS in all these n = 46 cases (100%). Deviation from estimated position to actual position was 1173.5 ± 4343.1 m for conventional and 65.6 ± 320.5 m for automatic geolocalization (p < 0.001). In addition, time to localization was significantly shorter using automatic geolocalization (34.7 vs. 71.7 s, p < 0.001). Time to first thoracic compression was significantly faster in the geolocalization group (83.0 vs. 122.6 s; p < 0.001).Conclusions: This pilot study showed that automatic geolocalization leads to a significantly shorter duration of the emergency call, significantly shorter times until the beginning of thoracic compressions, and a higher precision in determining the location of an emergency. [ABSTRACT FROM AUTHOR]

Cross-view geolocalization matches the same target in different images from various views, such as views of unmanned aerial vehicles (UAVs) and satellites, which is a key technology for UAVs to autonomously locate and navigate without a positioning system (e.g., GPS and GNSS). The most challenging aspect in this area is the shifting of targets and nonuniform scales among different views. Published methods focus on extracting coarse features from parts of images, but neglect the relationship between different views, and the influence of scale and shifting. To bridge this gap, an effective network is proposed with well-designed structures, referred to as multiscale block attention (MSBA), based on a local pattern network. MSBA cuts images into several parts with different scales, among which self-attention is applied to make feature extraction more efficient. The features of different views are extracted by a multibranch structure, which was designed to make different branches learn from each other, leading to a more subtle relationship between views. The method was implemented with the newest UAV-based geolocalization dataset. Compared with the existing state-of-the-art (SOTA) method, MSBA accuracy improved by almost 10% when the inference time was equal to that of the SOTA method; when the accuracy of MSBA was the same as that of the SOTA method, inference time was shortened by 30%.

Unmanned aerial vehicles (UAVs) must keep track of their location in order to maintain flight plans. Currently, this task is almost entirely performed by a combination of Inertial Measurement Units (IMUs) and reference to GNSS (Global Navigation Satellite System). Navigation by GNSS, however, is not always reliable, due to various causes both natural (reflection and blockage from objects, technical fault, inclement weather) and artificial (GPS spoofing and denial). In such GPS-denied situations, it is desirable to have additional methods for aerial geolocalization. One such method is visual geolocalization, where aircraft use their ground facing cameras to localize and navigate. The state of the art in many ground-level image processing tasks involve the use of Convolutional Neural Networks (CNNs). We present here a study of how effectively a modern CNN designed for visual classification can be applied to the problem of Absolute Visual Geolocalization (AVL, localization without a prior location estimate). An Xception based architecture is trained from scratch over a >1000 km2 section of Washington County, Arkansas to directly regress latitude and longitude from images from different orthorectified high-altitude survey flights. It achieves average localization accuracy on unseen image sets over the same region from different years and seasons with as low as 115 m average error, which localizes to 0.004% of the training area, or about 8% of the width of the 1.5 × 1.5 km input image. This demonstrates that CNNs are expressive enough to encode robust landscape information for geolocalization over large geographic areas. Furthermore, discussed are methods of providing uncertainty for CNN regression outputs, and future areas of potential improvement for use of deep neural networks in visual geolocalization.

To correctly interpret the estimated displacements in InSAR point clouds, especially in the built environment, these need to be linked to real-world structures. This requires the accurate and precise 3D positioning of each point. Artificial ground control points (GCPs), such as corner reflectors, serve this purpose, but since they require efforts and resources, there is a need for criteria to assess their usefulness. Here we evaluate the value and necessity of using GCPs for different scenarios, concerning the required efforts, and compare this to alternatives such as digital surface models (DSM) and advanced (geo) physical corrections. We consider single-epoch as well as multi-epoch GCP deployment, reflect on the number of GCPs required in relation to the number of SAR data acquisitions, and compare this with digital surface models of different quality levels. Analyzing the geolocation performance using TerraSAR-X and Sentinel-1 data, we evaluate the pros and cons of various deployment options and show that the multi-epoch deployment of a GCP yields optimal geolocalization results in terms of precision, accuracy, and reliability. [ABSTRACT FROM AUTHOR]

Highlights • Groupwise image registration by sequentially registering pairs of images is error-prone in case of highly unreliable pairwise registrations. • Hough voting spaces enable probabilistic pairwise registration estimations in order to identify the joint max-likelihood registration of all images. • Framework for probabilistic groupwise registration process based on feature correspondences. • Unlike previous groupwise registration work, the proposed method is able to jointly register images with arbitrary translational and rotational differences. • Outperforms state-of-the-art for highly challenging registration of aerial WW2 images to present-day ortho-photo maps. Abstract In this paper, we address the registration of historical WWII images to present-day ortho-photo maps for the purpose of geolocalization. Due to the challenging nature of this problem, we propose to register the images jointly as a group rather than in a step-by-step manner. To this end, we exploit Hough voting spaces as pairwise registration estimators and show how they can be integrated into a probabilistic groupwise registration framework that can be efficiently optimized. The feature-based nature of our registration framework allows to register images with a-priori unknown translational and rotational relations, and is also able to handle scale changes of up to 30% in our test data due to a final geometrically guided matching step. The superiority of the proposed method over existing pairwise and groupwise registration methods is demonstrated on eight highly challenging sets of historical images with corresponding ortho-photo maps. [ABSTRACT FROM AUTHOR]

We address the problem of aerial image geolocalization over an area as large as a whole city through road network matching, which is modeled as a 2D point set registration problem under the 2D projective transformation and solved in a two-stage manner. In the first stage, all the potential transformations aligning the query road point set to the reference road point set are found by local point feature matching. A local geometric feature, called the Projective-Invariant Contour Feature (PICF), which consists of a road intersection and the closest points to it in each direction, is specifically designed. We prove that the proposed PICF is equivariant under the 2D projective transformation group. We then encode the PICF with a projective-invariant descriptor to enable the fast search of potential correspondences. The bad correspondences are then removed by a geometric consistency check with the graph-cut algorithm effectively. In the second stage, a flexible strategy is developed to recover the homography transformation with all the PICF correspondences with the Random Sample Consensus (RANSAC) method or to recover the transformation with only one correspondence and then refine it with the local-to-global Iterative Closest Point (ICP) algorithm when only a few correspondences exist. The strategy makes our method efficient to deal with both scenes where roads are sparse and scenes where roads are dense. The refined transformations are then verified with alignment accuracy to determine whether they are accepted as correct. Experimental results show that our method runs faster and greatly improves the recall compared with the state-of-the-art methods.

One of the main drawbacks of Global Navigation Satellite Sytems (GNSS) is that they do not work indoors. When inside, there is often no direct line from the satellite signals to the device and the ultra high frequency (UHF) used is blocked by thick, solid materials such as brick, metal, stone or wood. In this paper, we describe a solution based on the Long Range Wide Area Network (LoRaWAN) technology to geolocalise vehicles indoors. Through estimation of the behaviour of a LoRaWAN channel and using trilateration, the localisation of a vehicle can be obtained within a 20−30 m range. Indoor geolocation for Intelligent Transporation Systems (ITS) can be used to locate vehicles of any type in underground parkings, keep a platoon of trucks in formation or create geo-fences, that is, sending an alert if an object moves outside a defined area, like a bicycle being stolen. Routing of heavy vehicles within an industrial setting is another possibility.

With fast development of information engineering and social network, people’s locations can be conveniently sensed by spatial technology, such as global positioning systems (GPS), base stations, Wi-Fi access points and even from the appearances of the photos they have taken. The social networks and the online shopping platforms have been gathering billions of users, who share a large amount of images taken in places they live in and visit. We can leverage the social networks to express our opinions about the services and places of interest (POIs). The interactions among users, and user and POIs or services generate big social media data, which have rich information for user, location, and service cognition. Many real-time network applications rely heavily on the accurate social users’ locations. How to sense the locations from multisource social media data is very important and challenging. Thus, in this paper, we give a systematic review of the works that combine social media and spatial technology for POI cognition and image localization. [ABSTRACT FROM PUBLISHER]

Many enchanting cultural heritage locations are hidden from tourists, especially when considering countries full of historic attractions. Tourists tend to consider only mainstream monuments and towns, neglecting wonderful little jewels along their travel itinerary. However, this is generally not their fault, as travelers cannot be aware of all the surrounding beauties when visiting a new region. To this aim, we discuss and analyze here PhotoTrip, an interactive tool able to autonomously recommend charming, even if not mainstream, cultural heritage locations along travel itineraries. PhotoTrip is able to identify these points of interest by gathering pictures and related information from Flickr and Wikipedia and then provide the user with suggestions and recommendations. An important technical challenge for this kind of services is the ability to provide only the most relevant pictures among the many available for any considered itinerary. To this aim, we have exploited social networks, crowdsourcing and gamification to involve users in the process of improving the response quality of our system. [ABSTRACT FROM AUTHOR]