Introduction to Large-Scale Visual Geo-localization

Zamir, Amir; Hakeem, Asaad; Gool, Luc Van; Shah, Mubarak; Szeliski, Richard

doi:10.1007/978-3-319-25781-5_1

Cited by 16 publications

(12 citation statements)

References 36 publications

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“…As described in DECAF [39], it is possible to use a pre-trained ConvNet as feature generator and apply classical machine learning such as Support Vector Machine (SVM) or logistic regression to train a model with good performance. Transfer learning is utilized, such as classification of satellite images [42], vehicles detection based on RGB images or LiDAR data [43,44], visual floor count determination [45] or visual localization [46]. Only recently, this approach was used for retrieving flooding relevant social media photos [27,28].…”

Section: Related Methods For Interpreting Flood Relevant Social Mediamentioning

confidence: 99%

Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos

Feng

Sester

2018

IJGI

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Abstract:In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens' safety. Therefore, real-time information about such events is desirable. With the increasing popularity of social media platforms, such as Twitter or Instagram, information provided by voluntary users becomes a valuable source for emergency response. Many applications have been built for disaster detection and flood mapping using crowdsourcing. Most of the applications so far have merely used keyword filtering or classical language processing methods to identify disaster relevant documents based on user generated texts. As the reliability of social media information is often under criticism, the precision of information retrieval plays a significant role for further analyses. Thus, in this paper, high quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos. Subsequently, events are detected through spatiotemporal clustering and visualized together with these high quality eyewitnesses in a web map application. Analyses and case studies are conducted during flooding events in Paris, London and Berlin.

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Section: Related Methods For Interpreting Flood Relevant Social Mediamentioning

confidence: 99%

Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos

Feng

Sester

2018

IJGI

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“…determining the location where an image was acquired, has been studied, often independently, by both the photogrammetry and the computer vision communities. Despite recent progress, automatic georeferencing of images remains a challenging task (Zamir et al, 2016).…”

Section: Previous Workmentioning

confidence: 99%

Pose Estimation of a Moving Camera With Low-Cost, Multi-GNSS Devices

Lourakis

Pateraki²,

Κάρολος³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Without additional prior information, the pose of a camera estimated with computer vision techniques is expressed in a local coordinate frame attached to the camera’s initial location. Albeit sufficient in many cases, such an arbitrary representation is not convenient for employment in certain applications and has to be transformed to a coordinate system external to the camera before further use. Assuming a camera that is firmly mounted on a moving platform, this paper describes a method for continuously tracking the pose of that camera in a projected coordinate system. By combining exterior orientation from a known target with incremental pose changes inferred from accurate multi-GNSS positioning, the full 6 DoF pose of the camera is updated with low processing overhead and without requiring the continuous visual tracking of ground control points. Experimental results of applying the proposed method to a moving vehicle and a mobile port crane are reported, demonstrating its efficacy and potential.

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“…Place Recognition: One of the most prevailing approaches in self-localization is place recognition [2,3,6,13,20,25,29,38,41]. By recording the appearance of the world and building a database of it in advance, the localization task can be formulated as a retrieval problem.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

Urtasun

Tartavull

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry-and LiDAR intensitybased localizers. This is important as self-driving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0.05m lateral accuracy and 1.12m longitudinal accuracy on average while taking up only 0.3% of the storage required by previous LiDAR intensitybased approaches.

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Introduction to Large-Scale Visual Geo-localization

Cited by 16 publications

References 36 publications

Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos

Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos

Pose Estimation of a Moving Camera With Low-Cost, Multi-GNSS Devices

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

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