Geolocation Method Research Articles

Twitter User Geolocation Based on Location Feature Enhancement

User location discovery from social media is crucial for location-based services like emergency awareness and event monitoring. Existing approaches generally integrate user-generated text features and social relationships, but insufficiently explore location-specific features and geographically proximate relationships, leading to suboptimal accuracy. In this paper, we propose a Twitter user geolocation method based on location feature enhancement, to better capture the location characteristics in users’ tweets and social relationships. Specifically, a user tweet representation algorithm based on location feature separation (TwLS) is designed. By leveraging words’ location-aware weight matrix and pre-trained embeddings, TwLS calculates a tweet representation for each user in every location, explicitly indicating the relevance between users and various locations. Additionally, we develop the local celebrity discovery method (LocCel) to construct social networks by identifying and preserving geographically concentrated high-degree nodes while filtering noise. Thereby LocCel enhances local relationships and strengthens location-proximate connections within the user social network. Experiments on two real-world datasets show that our method outperforms seven baselines, improving user geolocation accuracy by 3.1% ∼ 8.1% and 1.8% ∼ 8.8%, while reducing median error by 22.2% ∼ 52.8% and 19.4% ∼ 50.7%, respectively.

A Self-Adaptive Feature Extraction Method for Aerial-View Geo-Localization

A Self-Adaptive Feature Extraction Method for Aerial-View Geo-Localization

CCR: A Counterfactual Causal Reasoning-Based Method for Cross-View Geo-Localization

CCR: A Counterfactual Causal Reasoning-Based Method for Cross-View Geo-Localization

An improved geolocation methodology for spaceborne radar and lidar systems

Abstract. Geolocation and co-registration methodologies are essential for the accurate interpretation of observations from spaceborne remote sensors. In preparations for the Earth Cloud Aerosol and Radiation Explorer (EarthCARE), here, we refine the definition of these techniques and present various examples of geolocation assessments. The geolocation methods build upon earlier work; however, they introduce several improvements that have increased the reliability of the geolocation accuracy. The EarthCARE active-sensor geolocation methods use coastlines and significant elevation gradients in both statistical and numerical ways. The effectiveness of the proposed geolocation methods was tested using the extensive record of CloudSat and CALIPSO observations. The EarthCARE active-sensor geolocation methods were effective in identifying and correcting a short period of CloudSat observations when the star tracker was not operating properly. In addition, the geolocation methods were able to reproduce the excellent geolocation record of the CloudSat and CALIPSO missions.

An efficient cross-view image fusion method based on selected state space and hashing for promoting urban perception

In the field of cross-view image geolocation, traditional convolutional neural network (CNN)-based learning models generate unsatisfactory fusion performance due to their inability to model global correlations. The Transformer-based fusion methods can well compensate for the above problems, however, the Transformer has quadratic computational complexity and huge GPU memory consumption. The recent Mamba model based on the selection state space has a strong ability to model long sequences, lower GPU memory occupancy, and fewer GFLOPs. It is thus attractive and worth studying to apply Mamba to the cross-view image geolocation task. In addition, in the image-matching process (i.e., fusion of satellite/aerial and street view data.), we found that the storage occupancy of similarity measures based on floating-point features is high. Efficiently converting floating-point features into hash codes is a possible solution. In this study, we propose a cross-view image geolocation method (S6HG) based purely on Vision Mamba and hashing. S6HG fully utilizes the advantages of Vision Mamba in global information modeling and explicit location information encoding and the low storage occupancy of hash codes. Our method consists of two stages. In the first stage, we use a Siamese network based purely on vision Mamba to embed features for street view images and satellite images respectively. Our first-stage model is called S6G. In the second stage, we construct a cross-view autoencoder to further refine and compress the embedded features, and then simply map the refined features to hash codes. Comprehensive experiments show that S6G has achieved superior results on the CVACT dataset and comparable results to the most advanced methods on the CVUSA dataset. It is worth noting that other floating-point feature-based methods (4096-dimension) are 170.59 times faster than S6HG (768-bit) in storing 90,618 retrieval gallery data. Furthermore, the inference efficiency of S6G is higher than ViT-based computational methods.

Stanley and Acetolysis: Enhanced Method for Pollen Identification and Geolocation of Cocaine Samples

The concept of palynological assemblage, a set of all identifiable pollen grains and spores, is very useful in geolocation determinations in Forensic Studies. To identify plant genera, palynologists must recognize detailed aspects of apertures, ornamentation and layers on the cell wall. Pollen and spores recovered from higher purity cocaine samples represent an alternative to indicate drug source regions. After following the method established by Stanley in 1992, we added the acetolysis procedure in samples seized by the Civil Police of the State of Sao Paulo, Brazil, in 2017, in order to enhance morphological traits observation and taxonomical identification of different pollen taxa, allowing reliable environmental and geolocation reconstructions.

A Contrastive Learning Based Multiview Scene Matching Method for UAV View Geo-Localization

Multi-view scene matching refers to the establishment of a mapping relationship between images captured from different perspectives, such as those taken by unmanned aerial vehicles (UAVs) and satellites. This technology is crucial for the geo-localization of UAV views. However, the geometric discrepancies between images from different perspectives, combined with the inherent computational constraints of UAVs, present significant challenges for matching UAV and satellite images. Additionally, the imbalance of positive and negative samples between drone and satellite images during model training can lead to instability. To address these challenges, this study proposes a novel and efficient cross-view geo-localization framework called MSM-Transformer. The framework employs the Dual Attention Vision Transformer (DaViT) as the core architecture for feature extraction, which significantly enhances the modeling capacity for global features and the contextual relevance of adjacent regions. The weight-sharing mechanism in MSM-Transformer effectively reduces model complexity, making it highly suitable for deployment on embedded devices such as UAVs and satellites. Furthermore, the framework introduces a contrastive learning-based Symmetric Decoupled Contrastive Learning (DCL) loss function, which effectively mitigates the issue of sample imbalance between satellite and UAV images. Experimental validation on the University-1652 dataset demonstrates that MSM-Transformer achieves outstanding performance, delivering optimal matching results with a minimal number of parameters.

A Visual Navigation Algorithm for UAV Based on Visual-Geography Optimization

The estimation of Unmanned Aerial Vehicle (UAV) poses using visual information is essential in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a UAV visual navigation algorithm based on visual-geography Bundle Adjustment (BA) to address the challenge of missing geolocation information in monocular visual navigation. This algorithm presents an effective approach to UAV navigation and positioning. Initially, Visual Odometry (VO) was employed for tracking the UAV’s motion and extracting keyframes. Subsequently, a geolocation method based on heterogeneous image matching was utilized to calculate the geographic pose of the UAV. Additionally, we introduce a tightly coupled information fusion method based on visual-geography optimization, which provides a geographic initializer and enables real-time estimation of the UAV’s geographical pose. Finally, the algorithm dynamically adjusts the weight of geographic information to improve optimization accuracy. The proposed method is extensively evaluated in both simulated and real-world environments, and the results demonstrate that our proposed approach can accurately and in real-time estimate the geographic pose of the UAV in a GNSS-denied environment. Specifically, our proposed approach achieves a root-mean-square error (RMSE) and mean positioning accuracy of less than 13 m.

USE OF GPR TECHNOLOGIES IN THE ROAD CONSTRUCTION INDUSTRY

The article continues the study on determining the possibility of applying ground penetrating radar (GPR) technologies in the road construction industry. The authors conduct research using an antenna unit that registers the cross-polarisation component of the reflected signal. In this case, processing the obtained radar images comes down to subtracting two mutually orthogonal components of the signal, which allows the determination of areas of the environment with anisotropic properties. Identifying and positioning extraneous inclusions, finding communications, and determining the location of reinforcement in roadway and bridge structures are relevant tasks. Their solution lies in studying the phenomenon of wave diffraction on extraneous inclusions of the investigated environment. Metal inclusions of different diameters are distinctly visible on radar images obtained during laboratory research. The laboratory studies conducted by the authors showed that the shape and nature of the hyperbola of the diffracted wave significantly depend on the object’s depth, diameter, and material. Therefore, further research in this direction should aim at studying the shape of the diffracted wave, which ultimately allows for judging the depth of occurrence and the diameter of a local extraneous inclusion. When finding and identifying reinforcement in structural layers of road surfaces and elements of bridges, difficulties arise due to the spacing of the reinforcing mesh. With a small reinforcing mesh spacing, when the wavelength is longer than the spacing, the reflections from the adjacent reinforcement bars merge into a common continuous boundary and make the lower layers of the structure almost indistinguishable. Solving the mentioned difficulties is possible by improving the equipment base and the algorithms for processing and interpreting the received radar images. Completed theoretical studies and computational experiments allow us to state that the development of road surface thickness measurement and defect detection techniques will become the basis of the road surface monitoring system using the subsurface geolocation method. Keywords: ground penetrating radar, radar image, road surface, dielectric constant, defect detection, thickness measurement, local inclusions.

Improving Target Geolocation Accuracy with Multi-View Aerial Images in Long-Range Oblique Photography

Target geolocation in long-range oblique photography (LOROP) is a challenging study due to the fact that measurement errors become more evident with increasing shooting distance, significantly affecting the calculation results. This paper introduces a novel high-accuracy target geolocation method based on multi-view observations. Unlike the usual target geolocation methods, which heavily depend on the accuracy of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System), the proposed method overcomes these limitations and demonstrates an enhanced effectiveness by utilizing multiple aerial images captured at different locations without any additional supplementary information. In order to achieve this goal, camera optimization is performed to minimize the errors measured by GNSS and INS sensors. We first use feature matching between the images to acquire the matched keypoints, which determines the pixel coordinates of the landmarks in different images. A map-building process is then performed to obtain the spatial positions of these landmarks. With the initial guesses of landmarks, bundle adjustment is used to optimize the camera parameters and the spatial positions of the landmarks. After the camera optimization, a geolocation method based on line-of-sight (LOS) is used to calculate the target geolocation based on the optimized camera parameters. The proposed method is validated through simulation and an experiment utilizing unmanned aerial vehicle (UAV) images, demonstrating its efficiency, robustness, and ability to achieve high-accuracy target geolocation.

A high-precision sliding spotlight and TOPS SAR geo-location method

A high-precision sliding spotlight and TOPS SAR geo-location method

A Satellite-Drone Image Cross-View Geolocalization Method Based on Multi-Scale Information and Dual-Channel Attention Mechanism

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.

MCCG: A ConvNeXt-Based Multiple-Classifier Method for Cross-View Geo-Localization

MCCG: A ConvNeXt-Based Multiple-Classifier Method for Cross-View Geo-Localization

Maximising the value of transmitted data from PSATs tracking marine fish: a case study on Atlantic bluefin tuna

BackgroundThe use of biologging tags to answer questions in animal movement ecology has increased in recent decades. Pop-up satellite archival tags (PSATs) are often used for migratory studies on large fish taxa. For PSATs, movements are normally reconstructed from variable amounts of transmitted data (unless tags are recovered, and full data archives accessed) by coupling geolocation methods with a state-space modelling (SSM) approach. Between 2018 and 2019, we deployed Wildlife Computers PSATs (MiniPATs) from which data recovery varied considerably. This led us to examine the effect of PSAT data volume on SSM performance (i.e., variation in reconstructed locations and their uncertainty). We did this by comparing movements reconstructed using partial (< 100%) and complete (100%) geolocation data sets from PSATs and investigated the variation in Global Position Estimator 3 (GPE3; Wildlife Computers’ proprietary light-based geolocation SSM) reconstructed locations and their certainty in relation to data volume and movement type (maximum dispersal distance).ResultsIn this analysis, PSATs (n = 29) deployed on Atlantic bluefin tuna (Thunnusthynnus) transmitted data after detaching from study animals for between 0.3 and 10.8 days (mean 4.2 ± 3 days), yielding between 2 and 82% (mean 27% ± 22%) of total geolocation data. The volume of geolocation data received was positively related to the amount of time a tag transmitted for and showed a weak negative relationship to the length of the tag deployment. For 12 recovered PSATs (i.e., 100% of geolocation data; mean ± 1 S.D. = 301 ± 90 days of data per fish), (i) if ABT travelled short-distances (< 1000 km), movements reconstructed from partial data sets were more similar to their complete data set counterpart than fish that travelled over longer distances (> 1000 km); (ii) for fish that travelled long distances, mean distance of locations from corresponding complete data set locations were inversely correlated with the volume of data received; (iii) if only 5% of data was used for geolocation, reconstructed locations for long-distance fish differed by 2213 ± 647 km from the locations derived from complete data sets; and, (iv) track reconstructions omitted migrations into the Mediterranean Sea if less than 30% of data was used for geolocation.ConclusionsFor Wildlife Computers MiniPATs in our specific application, movements reconstructed with as little as 30% of the total geolocation data results in plausible outputs from the GPE3. Below this data volume, however, significant differences of more than 2000 km can occur. Whilst for a single species and manufacturer, this highlights the importance of careful study planning and the value of conducting study-specific sensitivity analysis prior to inclusion of modelled locations in research outputs. Based on our findings, we suggest general steps and refinements to maximise the value of light geolocation data from PSATs deployed on aquatic animals and highlight the importance of conducting data sensitivity analyses.

Unified and Real-Time Image Geo-Localization via Fine-Grained Overlap Estimation.

Image geo-localization aims to locate a query image from source platform (e.g., drones, street vehicle) by matching it with Geo-tagged reference images from the target platforms (e.g., different satellites). Achieving cross-modal or cross-view real-time (>30fps) image localization with the guaranteed accuracy in a unified framework remains a challenge due to the huge differences in modalities and views between the two platforms. In order to solve this problem, a novel fine-grained overlap estimation based image geo-localization method is proposed in this paper, the core of which is to estimate the salient and subtle overlapping regions in image pairs to ensure correct matching. Specifically, the high-level semantic features of input images are extracted by a deep convolutional neural network. Then, a novel overlap scanning module (OSM) is presented to mine the long-range spatial and channel dependencies of semantic features in various subspaces, thereby identifying fine-grained overlapping regions. Finally, we adopt the triplet ranking loss to guide the proposed network optimization so that the matching regions are as close as possible and the most mismatched regions are as far away as possible. To demonstrate the effectiveness of our FOENet, comprehensive experiments are conducted on three cross-view benchmarks and one cross-modal benchmark. Our FOENet yields better performance in various metrics and the recall accuracy at top 1 (R@1) is significantly improved, with a maximum improvement of 70.6%. In addition, the proposed model runs fast on a single RTX 6000, reaching real-time inference speed on all datasets, with the fastest being 82.3 FPS.

Switching-based FOA geolocation method with frequency offset by a single moving observation platform

Switching-based FOA geolocation method with frequency offset by a single moving observation platform

GUI: A Geolocation Method for Unreachable IP

Abstract 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.

UAV LARGE OBLIQUE IMAGE GEO-LOCALIZATION USING SATELLITE IMAGES IN THE DENSE BUILDINGS AREA

Abstract. For UAV large oblique image geo-localization in the dense buildings area, there are still two main challenges. One is the presence of obvious occlusion and large viewpoint differences in UAV images, and the other arises from the fact that reference images, particularly orthographic satellite images, lack façade information of man-made structures (such as buildings and roads), which is crucial for UAV large oblique images. Most of existing image-based geo-localization methods only address the first challenge, neglecting the interference brought by the second challenge, especially for UAV large oblique image geo-localization in the dense buildings area. Motivated by both these two challenges, we have proposed a novel method for UAV large oblique image geo-localization in the dense buildings areas, with the segments direction statistics (SDS) features and their histogram descriptors designed. By considering both the local and global features of man-made structures, the proposed method effectively addresses the significant information difference encountered in cross-view image matching. We conducted experiments on both the public UAV images dataset University-1652 and our own collected dataset of UAV large oblique long focal whiskbroom (LO-LF-W) images. Comparative analysis with state-of-the-art (SOTA) methods demonstrated that the proposed method improves the geo-localization accuracy by approximately 10%. Furthermore, the proposed method exhibits greater robustness to noise and changing orientation of reference images, making it particularly well-suited for dense buildings areas that pose challenges for existing methods.

Perancangan Sistem Informasi Presensi Online Karyawan Berbasis Website dengan Face Record dan Geo Location

This research discusses the development of an online employee attendance system in the context of the company. Attendance data includes employee attendance, arrival and return information, as well as absence status such as sickness or clearance. Traditionally, some organizations still use physical methods to collect attendance data, which is considered less efficient and effective. The research uses classical waterfall development method to facilitate the process of system development up to release. With face recognition and geo location methods, an online attendance system is proposed with IT infrastructure using private hosting and virtual machines with Red Hat Enterprise Linux 8.7 Operating System as a web server. With the implementation of this system, it is expected that benefits will be realized in the ease for employees to conduct attendance and management capabilities in managing employee attendance data in a structured manner.

A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention

Geo-localization has been widely applied as an important technique to get the longitude and latitude for unmanned aerial vehicle (UAV) navigation in outdoor flight. Due to the possible interference and blocking of GPS signals, the method based on image retrieval, which is less likely to be interfered with, has received extensive attention in recent years. The geo-localization of UAVs and satellites can be achieved by querying pre-obtained satellite images with GPS-tagged and drone images from different perspectives. In this paper, an image transformation technique is used to extract cross-view geo-localization information from UAVs and satellites. A single-stage training method in UAV and satellite geo-localization is first proposed, which simultaneously realizes cross-view feature extraction and image retrieval, and achieves higher accuracy than existing multi-stage training techniques. A novel piecewise soft-margin triplet loss function is designed to avoid model parameters being trapped in suboptimal sets caused by the lack of constraint on positive and negative samples. The results illustrate that the proposed loss function enhances image retrieval accuracy and realizes a better convergence. Moreover, a data augmentation method for satellite images is proposed to overcome the disproportionate numbers of image samples. On the benchmark University-1652, the proposed method achieves the state-of-the-art result with a 6.67% improvement in recall rate (R@1) and 6.13% in average precision (AP). All codes will be publicized to promote reproducibility.