Abstract: Methods and media for determining a list of geographic location candidates from an image of an environment are described. Open-source data indicative of the Earth's surface may be obtained and compared with images obtained from online sources. The images may be automatically analyzed using a plurality of modular convolution neural networks to determined probabilities of interest, environment, and if the image is locatable. Further, the resulting images may be analyzed for skyline and ridgeline depth orders and Region of Interest. A geolocation depicted in the image may be determined by comparing the results of the analysis with global geographic data.

Abstract: Methods and media for determining a list of geographic location candidates from an image of an environment are described. Open-source data indicative of the Earth's surface may be obtained and compared with images obtained from online sources. The images may be automatically analyzed using a plurality of modular convolution neural networks to determined probabilities of interest, environment, and if the image is locatable. Further, the resulting images may be analyzed for skyline and ridgeline depth orders and Region of Interest. A geolocation depicted in the image may be determined by comparing the results of the analysis with global geographic data.

Abstract: Systems and methods for determining a geographic location of an environment from an image including an annotation on a mobile device without GPS, with no network access, and with no access to peripheral devices or media is described. Open source data indicative of the earth's surface may be obtained and combined into grids or regions. Elevation data may be used to create skyline models at grid points on the surface. An image of an environment may be obtained from a camera on a mobile device. The user of the mobile device may trace a skyline of the environment depicted in the image. The annotation may be used to create reduced regions for edge detection analysis. The edge detection analysis may detect the skyline. The detected skyline may be compared to the skyline models to determine a most likely location of the user.

Abstract: Systems and techniques for determining a list of geographic location candidates from an image of an environment are described. Open source data indicative of the earth's surface may be obtained and combined into grids to create region data to gain a representation of the earth's surface within each grid cell. An image of an environment may be obtained from an unknown camera. Image characteristics may be compared to the region data to determine error between the image characteristics and projections of the region data. A list of lowest error geographic location candidates may be determined and provided to the user.