相关论文: Deep Transfer Learning on Satellite Imagery Improv…
Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to…
Air pollution is a common and serious problem nowadays and it cannot be ignored as it has harmful impacts on human health. To address this issue proactively, people should be aware of their surroundings, which means the environment where…
Calculating an Air Quality Index (AQI) typically uses data streams from air quality sensors deployed at fixed locations and the calculation is a real time process. If one or a number of sensors are broken or offline, then the real time AQI…
Building coverage statistics provide crucial insights into the urbanization, infrastructure, and poverty level of a region, facilitating efforts towards alleviating poverty, building sustainable cities, and allocating infrastructure…
The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citizens exposed to it. In this chapter we provide a review of…
Air pollution is a major risk factor for global health, with both ambient and household air pollution contributing substantial components of the overall global disease burden. One of the key drivers of adverse health effects is fine…
Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its…
Air pollutants, such as particulate matter, negatively impact human health. Most existing pollution monitoring techniques use stationary sensors, which are typically sparsely deployed. However, real-world pollution distributions vary…
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring. However, the accuracy…
Air contamination in urban areas has risen consistently over the past few years. Due to expanding industrialization and increasing concentration of toxic gases in the climate, the air is getting more poisonous step by step at an alarming…
MR images scanned at low magnetic field ($<1$T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those from high field (typically 1.5T and 3T). We adapt the recent…
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy making. Due to data collection mechanism, it is common to see…
Poor air quality can have a significant impact on human health. The National Oceanic and Atmospheric Administration (NOAA) air quality forecasting guidance is challenged by the increasing presence of extreme air quality events due to…
Recent advances in deep learning have enabled the inference of urban socioeconomic characteristics from satellite imagery. However, models relying solely on urbanization traits often show weak correlations with poverty indicators, as…
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals,…
The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution…
High-resolution daytime satellite imagery has become a promising source to study economic activities. These images display detailed terrain over large areas and allow zooming into smaller neighborhoods. Existing methods, however, have…
The expansion of the low-altitude economy is contingent on reliable cellular connectivity for unmanned aerial vehicles (UAVs). A key challenge in pre-flight planning is predicting communication link quality along proposed and pre-defined…
In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum Computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficiency of air quality…
The increasingly populated cities of the 21st Century face the challenge of being sustainable and resilient spaces for their inhabitants. However, climate change, among other problems, makes these objectives difficult to achieve. The Urban…