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: 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.