High-precision Geolocation Research Articles

Lightweight and Stable Multi-Feature Databases for Efficient Geometric Localization of Remote Sensing Images

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.

Image moment extraction based aerial photo selection for UAV high-precision geolocation without GPS

Since GPS is susceptible to environmental interference or cannot be used in some specific situations, it is important to study the UAV geolocation under GPS-free condition. How to ensure that the aerial photos used for positioning have high accuracy is a pressing concern. The conventional approach relies on the number of matching feature points between aerial and satellite photos and verifying whether the aerial photo center projection falls within a designated area of the satellite photo. However, this method often yields suboptimal accuracy. This paper introduces a novel method for selecting aerial photos for geolocation based on an image moment extraction module. The image moment extraction module contains two parts: the relative error of the image invariant moment and the area relative error. The former is used to select the aerial photos with good performance in translation, rotation and scaling before and after projection, and the latter is adopted to reject aerial photos with excessive scaling before and after projection. Through experiments, we demonstrate the effectiveness and superiority of this approach. Results show that the image moment extraction module, when used for positioning with aerial photo center projection, significantly reduces the mean absolute error in UAV geolocation from 63.220 m to 9.454 m. The maximum error is reduced from 486.7 m to 47.194 m, with 70.96% of aerial photos suitable for positioning. This research is pivotal for achieving high-precision UAV geolocation in GPS-free environments.

3-D Stereo Geolocation of Radar Reflectors Using Multiaspect SAR Acquisitions

With well-controlled orbits and a more accurate timing system of the new-generation synthetic aperture radar (SAR) satellites, the ability of high-precision geolocation with multiple SAR acquisitions has been verified based on radar reflectors. In this letter, SAR absolute and differential geolocation methods are introduced and implemented to retrieve the coordinates of two different types of radar reflectors: corner reflectors (CRs) and multidirectional dihedral reflectors (MDRs). The experimental results demonstrate that decimeter- even centimeter-level positioning accuracy can be obtained by SAR stereo geolocation based on multiaspect SAR acquisitions from RadarSAT-2 (RS-2), COSMO-SkyMed (CSK), Sentinel-1 (S1), and TerraSAR-X (TSX), respectively.

Selected microhabitat and surface temperatures of two sympatric lizard species

Many small lizards live in structurally and thermally heterogeneous environments where they can select preferred microhabitats and surface temperatures to optimize their activity. We examined microhabitat selection and analyzed surface temperatures selected by two ecologically similar lacertid species, Iberolacerta horvathi and Podarcis muralis, at three sites and in different ecological contexts: allotopic and syntopic conditions. We examined their microhabitat and spatial thermal selection by using high-precision geolocation techniques to locate the lizards and record surface temperatures with temperature data loggers. Microhabitat and thermal availability differed among sites: the I. horvathi allotopic site had more rock surfaces and was coldest; the P. muralis allotopic site had more ground surfaces and was warmer; and the syntopic site had the greatest surface and thermal heterogeneity. Our results highlight the differences in microhabitat selection between the two species, most notably in surface selection, but also in thermal characteristics. I. horvathi preferentially and almost exclusively selected rocks. P. muralis chose soil, but with a more general use of microhabitats. I. horvathi lizards chose colder temperatures than those available, while P. muralis chose higher temperatures than those available. Both species showed no differences in microhabitat selection between allotopic and syntopic sites, which could not be explained by differences between sites. Our results provided new insights into the difference in surface temperature selection between two lizard species that may be adapted to different environments.

AUTOMATED REAL-TIME TRAFFIC FORECASTING SYSTEM

Based on the analysis of the vehicles total number growth rates, which exceed the rates of expansion and optimization of the transport infrastructure, the need for the introduction of real-time traffic forecasting and control systems is shown. The factors that make it possible to detect the probability of potentially dangerous situations on the road, such as traffic jams, accidents and lack of parking spaces, respectively, in certain urban areas, based on the data of sensor networks and surveillance cameras combined within the global system of the Internet of Things, have been determined. It is proposed to build a sensor network based on magnetic sensors, which allows for high-precision geolocation with refinement of the received data by using ultrasonic sensors and optical monitoring tools, while identification is carried out by reading RFID tags. It is shown that the task of optimal organization of the relay system includes the determination of the features of the city infrastructure and statistical indicators of the city's traffic flows, and for a multi-level communication system, protocols are determined depending on the distance between nodes, requirements for the level of data protection, data transmission speed, minimum radio signal amplitude, as well as restrictions on the power supply of a separate node. The presented topology of the relay network includes the organization of sensor nodes into clusters, transmission from the main node of the cluster to the gateway node, and from the gateway nodes to the base station. On the basis of the specified model, a scheme for building cluster self-organization algorithms can be presented by forming clusters in real time according to the topology of the cluster tree, which allows simplifying the data transfer subsystem and reducing the time of processing input data. The developed scheme for analyzing the traffic flow at the intersection and the availability of parking spaces can be used in the development of methodological recommendations for the implementation of the "Smart City" concept and the creation of software applications that provide drivers with information about the state of traffic and predicted changes within a certain time interval. Keywords: automaticregulation of auto-traffic, controlled intersection, sensor network, Internet of Things, geolocation, identification, self-organization of clusters.

Where Are WeChat Users: A Geolocation Method Based on User Missequence State Analysis

WeChat has earned more than one billion users worldwide. Research on the geolocation of WeChat users can not only discover the location of malicious users but also verify the validity of user privacy protection strategies. However, existing methods are susceptible to WeChat's location confusion strategies, resulting in intolerable geolocating errors. In this article, a WeChat user geolocation method based on user missequence state analysis (MSAG) is proposed. Different from the existing methods which usually rely on the relationship between reported and actual distances of nearby users, MSAG utilizes the relation between user order and actual distances to geolocate the target. By statistical analysis of sequence changes of nearby users under different actual distances, the distance range that causes nearby users missequence is determined. During geolocation, the distance range of the target is delimited by checking the missequence state of the target and a user with a known location. Finally, we discuss trilateration strategies for different abnormal situations. Experimental results show that MASG can achieve high-precision geolocation of WeChat users, the average error is less than 50 m, and 72% of geolocating errors are within 60 m; compared with existing typical algorithms, the average error is reduced by 23.7%-50.7%.

CROWD4EMS: A CROWDSOURCING PLATFORM FOR GATHERING AND GEOLOCATING SOCIAL MEDIA CONTENT IN DISASTER RESPONSE

Abstract. Increase in access to mobile phone devices and social media networks has changed the way people report and respond to disasters. Community-driven initiatives such as Stand By Task Force (SBTF) or GISCorps have shown great potential by crowdsourcing the acquisition, analysis, and geolocation of social media data for disaster responders. These initiatives face two main challenges: (1) most of social media content such as photos and videos are not geolocated, thus preventing the information to be used by emergency responders, and (2) they lack tools to manage volunteers contributions and aggregate them in order to ensure high quality and reliable results. This paper illustrates the use of a crowdsourcing platform that combines automatic methods for gathering information from social media and crowdsourcing techniques, in order to manage and aggregate volunteers contributions. High precision geolocation is achieved by combining data mining techniques for estimating the location of photos and videos from social media, and crowdsourcing for the validation and/or improvement of the estimated location. The evaluation of the proposed approach is carried out using data related to the Amatrice Earthquake in 2016, coming from Flickr, Twitter and Youtube. A common data set is analyzed and geolocated by both the volunteers using the proposed platform and a group of experts. Data quality and data reliability is assessed by comparing volunteers versus experts results. Final results are shown in a web map service providing a global view of the information social media provided about the Amatrice Earthquake event.

Street-Level Geolocation Based on Router Multilevel Partitioning

The high-precision geolocation of Internet hosts plays an important role in many applications, such as online advertising and deception detection. The existing typical high-precision geolocation algorithms usually utilize single-hop or relative delay to geolocate an Internet host at street-level granularity. However, it is difficult to accurately measure the single-hop or relative delay within a city. This challenge sometimes results in large geolocation errors. To solve this problem, a street-level geolocation algorithm based on router multilevel partitioning is proposed. Unlike existing typical algorithms, the proposed algorithm makes a credible hypothesis that each router has a relatively stable service object for a period of time. By analyzing the connection between routers and landmarks, the possible geographic service ranges of routers are inferred from the geographic distribution of landmarks. Then, distance constraints arising from routers' service ranges are formed to estimate the geographic location of the target IP. Theoretical analysis of the geolocation error shows that the maximum and average errors of the proposed algorithm are less than those of existing typical algorithms. The proposed algorithm is evaluated by geolocating a total of 12,152 target IP addresses located in four cities in different regions. The experimental results show that, compared with the existing typical street-level geolocation algorithms SLG and NC-Geo, the average median error of the proposed algorithm decreases from 4.735 km and 3.776 km to 3.25 km, representing error reductions of approximately 31.36% and 13.96%, respectively.

AN IMPROVED METHOD OF AGM FOR HIGH PRECISION GEOLOCATION OF SAR IMAGES

Abstract. In order to take full advantage of SAR images, it is necessary to obtain the high precision location of the image. During the geometric correction process of images, to ensure the accuracy of image geometric correction and extract the effective mapping information from the images, precise image geolocation is important. This paper presents an improved analytical geolocation method (IAGM) that determine the high precision geolocation of each pixel in a digital SAR image. This method is based on analytical geolocation method (AGM) proposed by X. K. Yuan aiming at realizing the solution of RD model. Tests will be conducted using RADARSAT-2 SAR image. Comparing the predicted feature geolocation with the position as determined by high precision orthophoto, results indicate an accuracy of 50m is attainable with this method. Error sources will be analyzed and some recommendations about improving image location accuracy in future spaceborne SAR's will be given.

High-Precision Geolocation of Earth Surface Images from the Resurs-P Spacecraft

This paper deals with the problems of developing models of high-precision geolocation of Earth surface images obtained from the Resurs-P Nos. 1 and 2 spacecraft based on solving two problems. The first problem involves the development of algorithms for the complex processing of measurement information and the second involves the geometric calibration of satellite survey equipment by ground polygons. It is shown that, as a result, RMS measurement errors of the coordinates of objects in the images of the Earth’s surface do not exceed 10 m.