PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training

Road network and building footprint extraction is essential for many applications such as updating maps, traffic regulations, city planning, ride-hailing, disaster response \textit{etc}. Mapping road networks is currently both expensive and labor-intensive. Recently, improvements in image segmentation through the application of deep neural networks has shown promising results in extracting road segments from large scale, high resolution satellite imagery. However, significant challenges remain due to lack of enough labeled training data needed to build models for industry grade applications. In this paper, we propose a two-stage transfer learning technique to improve robustness of semantic segmentation for satellite images that leverages noisy pseudo ground truth masks obtained automatically (without human labor) from crowd-sourced OpenStreetMap (OSM) data. We further propose Pyramid Pooling-LinkNet (PP-LinkNet), an improved deep neural network for segmentation that uses focal loss, poly learning rate, and context module. We demonstrate the strengths of our approach through evaluations done on three popular datasets over two tasks, namely, road extraction and building foot-print detection. Specifically, we obtain 78.19\% meanIoU on SpaceNet building footprint dataset, 67.03\% and 77.11\% on the road topology metric on SpaceNet and DeepGlobe road extraction dataset, respectively.

Understanding the Dynamics of Drivers' Locations for Passengers Pickup Performance: A Case Study

With the emergence of e-hailing taxi services, a growing number of scholars have attempted to analyze the taxi trips data to gain insights from drivers' and passengers' flow patterns and understand different dynamics of urban public transportation. Existing studies are limited to passengers' location analysis e.g., pick-up and drop-off points, in the context of maximizing the profits or better managing the resources for service providers. Moreover, taxi drivers' locations at the time of pick-up requests and their pickup performance in the spatial-temporal domain have not been explored. In this paper, we analyze drivers' and passengers' locations at the time of booking request in the context of drivers' pick-up performances. To facilitate our analysis, we implement a modified and extended version of a co-clustering technique, called sco-iVAT, to obtain useful clusters and co-clusters from big relational data, derived from booking records of Grab ride-hailing service in Singapore. We also explored the possibility of predicting timely pickup for a given booking request, without using entire trajectories data. Finally, we devised two scoring mechanisms to compute pickup performance score for all driver candidates for a booking request. These scores could be integrated into a booking assignment model to prioritize top-performing drivers for passenger pickups.