GeoVision: Intelligent Exploration of Image Geolocation Using Convolutional Neural Networks

The GeoVision project is based on Convolutional Neural Network (CNN) technology, exploring the extraction of visual features from images to achieve accurate geographic coordinate prediction, and revealing the application potential of CNNs in geospatial intelligence. This project aims to address the limitations of traditional geolocation that relies on GPS or manual annotation, inferring the shooting location from the visual content itself using deep learning methods.

Traditional geolocation relies on GPS or manual annotation, but a large number of historical, online, or aerial images lack precise geographic tags, and manual annotation is time-consuming and labor-intensive. The complexity of visual geolocation lies in:

• The same location has large appearance differences under different seasons/weather;
• Similar landforms may be distributed in different regions;
• Human-intuitive geographic clues (such as vegetation types, architectural styles) are difficult to convert into algorithmic features.

GeoVision chooses AlexNet as the basic architecture because it is concise and effective, has mature verification in image classification, and has pre-trained resources. The model converts geolocation into a regression task, outputting continuous latitude and longitude coordinates; it retains AlexNet's convolutional layers (extracting multi-scale features), pooling layers (dimensionality reduction to enhance invariance), and fully connected layers, with the output layer adjusted to two neurons to predict latitude and longitude.

The model automatically learns visual patterns related to geographic locations:

• Vegetation features (such as tropical rainforests, temperate deciduous forests) serve as latitude indicators;
• Architectural styles (Mediterranean white houses, East Asian traditional roofs) provide cultural geographic clues;
• Natural landforms (coastlines, mountains, soil colors) and sky lighting conditions (solar altitude angle, atmospheric scattering) also convey geographic signals.

Training uses image datasets with GPS tags; preprocessing includes standardization, size adjustment, and data augmentation; a balanced sampling strategy is adopted to solve the problem of uneven geographic distribution; the loss function considers the characteristics of spherical coordinates, and may use the Haversine distance to measure surface distance, avoiding the shortcomings of Euclidean distance.

Application scenarios are wide-ranging:

• Social media analysis (adding locations to untagged images to support content recommendation);
• News forensics (verifying the shooting location of images);
• Drones/autonomous driving (GPS backup);
• Cultural heritage protection (organizing historical images);
• Tourism exploration (photo location, similar landscape search).

Current limitations: accuracy decreases in areas with indistinct features (repetitive farmland, similar suburbs), and season/weather changes interfere with judgment. Future directions:

• Multi-modal fusion (combining metadata and text);
• Hierarchical modeling (from coarse classification to fine coordinates);
• Transfer learning/domain adaptation to handle uncovered areas.