This paper introduces a three-dimensional positioning platform designed to enhance the quality of Korea Telecom's emergency location service by providing floor-level accuracy. Since 2019, the Korea Communications Commission has publicly announced the test results for the positioning quality of mobile operators’ emergency location services. These results are classified by three positioning methods—GNSS, Wi-Fi, and cellular network—in terms of accuracy and time. The main guideline for positioning accuracy is as follows 50 metres horizontal positioning accuracy and 30 seconds total positioning time limit. More recently, the Federal Communications Commission's guideline for vertical positioning requirements, specifying 3 metres of Z-axis accuracy with 90% confidence, has been a subject of discussion among many stakeholders. In order to achieve these quality requirements for emergency location services, Korea Telecom is setting a new challenging accuracy target and working to provide enhanced emergency location services from the perspective of providing end-to-end location information using a floor-level accuracy technique of three-dimensional positioning platform.

In recent interactive segmentation algorithms, previous probability maps are used as network input to help predictions in the current segmentation round. However, despite the utilization of previous masks, useful information contained in the probability maps is not well propagated to the current predictions. In this paper, to overcome this limitation, we propose a novel and effective algorithm for click-based interactive image segmentation, called MFP, which attempts to make full use of probability maps. We first modulate previous probability maps to enhance their represen-tations of user-specified objects. Then, we feed the modulated probability maps as additional input to the segmentation network. We implement the proposed MFP algorithm based on the ResNet-34, HRNet-18, and ViT-B backbones and assess the performance extensively on various datasets. It is demonstrated that MFP meaningfully outperforms the existing algorithms using identical backbones. The source codes are available at hups.//github.com/cwleetul/Ml-P.