Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
Source abstract
Abstract
Images captured by drones are increasingly used in various fields, including geographic information management. This study evaluates a procedure that incorporates active learning semantic segmentation for verifying the building registration ledger. Several semantic segmentation techniques were evaluated to extract building information, with ResNet identified as the most effective method for accurately recognizing building roofs. Using active learning, the training data were refined by removing instances with low similarity, leading to improved network performance of the model. The procedure was demonstrated to identify discrepancies between the building information system and the inferred label images, as well as to detect labeling errors on a training dataset. Through this research, the geographic information system dataset is enhanced with minimal human oversight, offering significant potential for urban planning and building detection advancements.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Remote Sensing and LiDAR Applications · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information & Communication Engineering, Wonkwang University, Iksan 54538, Republic of Korea
Department of Computer Software Engineering, Wonkwang University, Iksan 54538, Republic of Korea
Funding
Wonkwang University
References (26)
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