A Web Client Perspective on IP Geolocation Accuracy

The geographical locations of smart devices can help in providing authentication information between multimedia content providers and users in 5G networks. The IP geolocation methods can help in estimating the geographical location of these smart devices. The two key assumptions of existing IP geolocation methods are as follows: (1) the smallest relative delay comes from the nearest host; (2) the distance between hosts which share the closest common routers is smaller than others. However, the two assumptions are not always true in weakly connected networks, which may affect accuracy. We propose a novel street-level IP geolocation algorithm (Corr-SLG), which is based on the delay-distance correlation and multilayered common routers. The first key idea of Corr-SLG is to divide landmarks into different groups based on relative-delay-distance correlation. Different from previous methods, Corr-SLG geolocates the host based on the largest relative delay for the strongly negatively correlated groups. The second key idea is to introduce the landmarks which share multilayered common routers into the geolocation process, instead of only relying on the closest common routers. Besides, to increase the number of landmarks, a new street-level landmark collection method called WiFi landmark is also presented in this paper. The experiments in one province capital city of China, Zhengzhou, show that Corr-SLG can improve the geolocation accuracy remarkably in a real-world network.

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IP Geolocation Accuracy Evaluation Based on Crowdsourcing

Journal of Physics Conference Series ◽

10.1088/1742-6596/1624/3/032028 ◽

2020 ◽

Vol 1624 ◽

pp. 032028

Author(s):

Guangyu Zhu ◽

Guoming Ren ◽

Xiang Li ◽

Xiaoye Li ◽

Yongpeng Ti

Keyword(s):

Accuracy Evaluation ◽

Ip Geolocation ◽

Geolocation Accuracy

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New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

Remote Sensing ◽

10.3390/rs13050860 ◽

2021 ◽

Vol 13 (5) ◽

pp. 860

Author(s):

Yi-Chun Lin ◽

Tian Zhou ◽

Taojun Wang ◽

Melba Crawford ◽

Ayman Habib

Keyword(s):

Remote Sensing ◽

Control Strategies ◽

Target Function ◽

Canopy Cover ◽

Surface Model ◽

Flowering Date ◽

Sensing Platforms ◽

Geolocation Accuracy ◽

New Strategies

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

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On the Accuracy of Country-Level IP Geolocation

Proceedings of the Applied Networking Research Workshop on ZZZ ◽

10.1145/3404868.3406664 ◽

2020 ◽

Author(s):

Ioana Livadariu ◽

Thomas Dreibholz ◽

Anas Saeed Al-Selwi ◽

Haakon Bryhni ◽

Olav Lysne ◽

...

Keyword(s):

Ip Geolocation ◽

Country Level

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IP Geolocation Using Traceroute Location Propagation and IP Range Location Interpolation

Companion Proceedings of the Web Conference 2021 ◽

10.1145/3442442.3451888 ◽

2021 ◽

Author(s):

Ovidiu Dan ◽

Vaibhav Parikh ◽

Brian D. Davison

Keyword(s):

Ip Geolocation

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Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research

Remote Sensing ◽

10.3390/rs13152981 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2981

Author(s):

Jeanné le Roux ◽

Sundar Christopher ◽

Manil Maskey

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Land Cover ◽

Spectral Response ◽

Fine Particulate Matter ◽

Daily Basis ◽

Land Cover Types ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Difference ◽

Geolocation Accuracy

Planet, a commercial company, has achieved a key milestone by launching a large fleet of small satellites (smallsats) that provide high spatial resolution imagery of the entire Earth’s surface on a daily basis with its PlanetScope sensors. Given the potential utility of these data, this study explores the use for fine particulate matter (PM2.5) air quality applications. However, before these data can be utilized for air quality applications, key features of the data, including geolocation accuracy, calibration quality, and consistency in spectral signatures, need to be addressed. In this study, selected Dove-Classic PlanetScope data is screened for geolocation consistency. The spectral response of the Dove-Classic PlanetScope data is then compared to Moderate Resolution Imaging Spectroradiometer (MODIS) data over different land cover types, and under varying PM2.5 and mid visible aerosol optical depth (AOD) conditions. The data selected for this study was found to fall within Planet’s reported geolocation accuracy of 10 m (between 3–4 pixels). In a comparison of top of atmosphere (TOA) reflectance over a sample of different land cover types, the difference in reflectance between PlanetScope and MODIS ranged from near-zero (0.0014) to 0.117, with a mean difference in reflectance of 0.046 ± 0.031 across all bands. The reflectance values from PlanetScope were higher than MODIS 78% of the time, although no significant relationship was found between surface PM2.5 or AOD and TOA reflectance for the cases that were studied. The results indicate that commercial satellite data have the potential to address Earth-environmental issues.

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A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery

Remote Sensing ◽

10.3390/rs13030491 ◽

2021 ◽

Vol 13 (3) ◽

pp. 491

Author(s):

Niangang Jiao ◽

Feng Wang ◽

Hongjian You

Keyword(s):

Remote Sensing Data ◽

Observation Data ◽

Multiple Sources ◽

Accuracy Improvement ◽

Geometric Calibration ◽

Proposed Model ◽

Calibration Methods ◽

Earth Observation Data ◽

Sar Imagery ◽

Geolocation Accuracy

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data.

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