Utilizing Very High-resolution Optical RGB Satellite Imagery in Geo-information Extraction for Fine-scale Map-making

Maps are the fundamental elements of any navigation and localization system. With the fast expansion of urban areas and the increasing complexity of modern cities, traditional mapping techniques cannot meet the need for frequent map updates with enriched map details. This work investigates how geo-referenced very high-resolution (VHR) RGB satellite imagery can be used to extract geo-information to support the creation of fine-scale maps and facilitate map updates, focusing on two sub-directions. First, to alleviate the data scarcity issue of large-scale geo-information extraction from satellite imagery-based datasets, (1) GAN-assisted road segmentation proposes a new assisted training scheme to improve the model performance when the training dataset is limited and (2) a context-enhanced satellite-imagery dataset is created for large-scale parking lot detection to improve the type diversity of target objects. Second, to support rich map attribute geo-information extraction and vision-based navigation using street-view imagery, new methods are proposed to improve location and orientation extraction of the street-view imagery via cross-view matching with satellite imagery.