The Survey Hole Inpainting Problem: A Machine Learning Approach

Wei-Zhi Lin, Jen-Jee Chen, Yu-Chee Tseng

Abstract: This work considers the inpainting of missing data in an indoor field, such as geomagnetism and WiFi fingerprints. As opposed to typical image/video inpainting problems, this problem poses several new challenges. First, unlike images with rectangular shapes and fixed RGB channels, indoor geographic data are multi-channeled and highly influenced by building structures. Second, unlike natural objects with fixed shapes, each geographic field is distinct and geographic data are environment-sensitive, following complex physical laws. Consequently, learning from data in other fields is difficult. Third, such data may be obtained from manual surveys and crowdsourcing, which often results in weakly-labeled and noisy datasets. We model our field data as (i) manually surveyed labeled data with holes and (ii) crowdsourced weakly-labeled data without holes. We propose a two-level adversarial regularization inpainting model to conquer these challenges and validate our results with real field data.

External IDs:dblp:journals/tmc/LinCT26