Estimating the location where an image was taken based solely on the contents of the image is a challenging task, even for humans, as properly labeling an image in such a fashion relies heavily on contextual information, and is not as simple as identifying  a single object in the image. Thus any methods which attempt to do so must somehow account for these complexities, and no single model to date is completely capable of addressing all challenges. This work contributes to the state of research in image geolocation inferencing by introducing a novel global meshing strategy, outlining a variety of training procedures to overcome the considerable data limitations when training these models, and demonstrating how incorporating additional information can be used to improve the overall performance of a geolocation inference model. In this work, it is shown that Delaunay triangles are an effective type of mesh for geolocation in relatively low volume scenarios when compared to results from state of the art models which use quad trees and an order of magnitude more training data.   In addition, the time of posting, learned user albuming, and other meta data are easily incorporated to improve geolocation by up to 11% for country-level (750 km) locality accuracy to 3% for city-level (25 km) localities.
 Advancements in deep learning over the past several years have pushed the bounds of what is possible with machine learning beyond typical image classification or object localization. Whereas prior work in computer vision and machine learning has focused primarily on determining the contents of the image-"is there a dog in this photo?"-more recent methods and more complicated models have enabled deeper examinations of the contextual information behind an image. This deeper exploration allows a researcher to ask more challenging questions of its models. One such question is, "where in the world was this picture taken?" However, estimating the geographic origin of a ground-level image is a challenging task due to a number of different factors. Firstly, the volume of available images with geographic information associated with them is not evenly distributed across the globe. This uneven distribution increases the complexity of the model chosen and complicates the design of the model itself. Furthermore, there are additional challenges associated with geo-tagged image data, such as the potential for conflicting data (eg. incorrect geolabels and replica landmarks -St Peters Basilica in Nikko, Japan) and the ambiguity of geographic terms (eg. imagery of similar and ambiguous geological features, such as beaches, screenshots of websites, or generally, plates of food).The work presented herein focuses on the task of content-based image geolocation: the process of identify the geographic origin of an image taken at ground level. Given the significant growth in the production of data, and trends in social data to move away from pure text-based platforms to more images, videos, and mixed-media, tackling the question of inferring geographic context behind an image becomes a significant and relevant problem. Whereas social textual data may be enriched with geolocation information, images may or may not contain such information; EXIF data is often stripped from images or may be present but incomplete. Within the context of mixed-media data, using the geolocation information associated with a linked object may be unreliable or not even applicable. For example , a user on Twitter may have geolocation information enabled for their account, but may post a link to an image taken from a location different from the one where they made their post. Conditioning on latent variables can only improve geolocation models. Universally using time of day in the models was observed to increase accuracy and lower bias. Time of day is a weak addition to Inception-like results, but it is useful to be as accurate as possible, and it makes statistically significant improvements to geolocation. Both meshes were improved by using time information (M2). This is a result that is not surprising, and as a research approach, can be applied to any number of meta data variables that might accompany images.Accounting for indoor/outdoor scenes in images explained variation in validation accuracy. Outdoor only results are better than results for all images. We suggest as future work that the probability that an image is outdoor could be concatenated to the input of M2. The accompanying research hypothesis is that in the case that an image is indoors, perhaps the model will learn to weight time or other meta data results more heavily, or otherwise be able to use that information optimally.Increasing the granularity of a grid reduces accuracy at the country and regional level, while improving accuracy at street and city level accuracy. To be clear though, street level geoinferencing is not practical with a course mesh. This is shown by the best possible accuracy in Table 6 , and so it is expected that a fine mesh would do better. On the other hand, there is no reason to assume that a fine mesh has to be better for geolocation at larger resolutions than 25 km, nor is there any explicit way to prove a fine mesh should do no worse than a course mesh. What we observe is that a course mesh is a superior grid for 200 km resolutions. Furthermore, we show that for both the coarse mesh and the fine mesh, using a Delunary triangle-based mesh provides the ability to train accurate models with far fewer training examples than what was previously published.
A global geolocation inferencing strategy with novel meshing strategy and demonstrating incorporating additional information can be used to improve the overall performance of a geolocation inference model.
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