Geolocalizing Pixels on PTZ Camera Imagery in Natural Environments using Deep Neural Network

Pan-Tilt-Zoom (PTZ) camera systems used in wildfire surveillance offer significant potential for fire detection, tracking, and response, supporting more effective wildfire management. However, accurately geolocalizing objects or phenomenon seen on images remains a complex challenge, particularly in homogeneous environments where distinct landmarks are scarce and camera parameters continuously vary. This research aims to address this problem by leveraging a deep learning approach to map image pixels to real-world geographic coordinates. A neural network is trained on a dataset of manually calibrated pixel-to-coordinate mappings from wildfire monitoring cameras, learning spatial transformations across varying PTZ configurations, and enabling precise geotagging of pixels – even for unseen camera positions. Since these pixels may correspond to fire, smoke, or other environmental cues, improved geolocalization accuracy directly contributes to more reliable wildfire detection, tracking, and response, supporting more effective wildfire management.