Spatiotemporal Dynamics of Surface Ozone and Its Relationship with Meteorological Factors over the Beijing-Tianjin-Tangshan Region, China, from 2016 to 2019.

In recent years, ozone pollution has been increasing in some parts of the world. In this study, we used the Beijing-Tianjin-Tangshan (BJ-TJ-TS) urban agglomeration region as a case study and used satellite remotely sensed inversion data and hourly ground monitoring observations of surface ozone concentrations, meteorological data, and other factors from 2016 to 2019 to explore the spatiotemporal dynamic characteristics of surface ozone concentration and its pollution levels. We also investigated their coupling relationships with meteorological factors, including temperature, pressure, relative humidity, wind velocity, and sunshine duration, in order to support the development of effective control measures for regional ozone pollution.

Road Extraction from High Resolution Remote Sensing Images Based on Vector Field Learning.

Accurate and up-to-date road network information is very important for the Geographic Information System (GIS) database, traffic management and planning, automatic vehicle navigation, emergency response and urban pollution sources investigation. In this paper, we use vector field learning to extract roads from high resolution remote sensing imaging. This method is usually used for skeleton extraction in nature image, but seldom used in road extraction.

Chimney Detection Based on Faster R-CNN and Spatial Analysis Methods in High Resolution Remote Sensing Images.

Spatially location and working status of pollution sources are very important pieces of information for environment protection. Waste gas produced by fossil fuel consumption in the industry is mainly discharged to the atmosphere through a chimney. Therefore, detecting the distribution of chimneys and their working status is of great significance to urban environment monitoring and environmental governance.