When remote sensing data meet ubiquitous urban data: Fine-grained air quality inference

2016 ◽  
Author(s):  
Yanan Xu ◽  
Yanmin Zhu
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Fine Grained ◽  
Urban Data

scholarly journals Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference Around Mainland China via Attention-Augmented CNN from Daytime Satellite Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2067
Author(s):  
Haoyu Liu ◽  
Xianwen He ◽  
Yanbing Bai ◽  
Xing Liu ◽  
Yilin Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Chinese Mainland ◽  
Economic Research ◽  
Official Method ◽  
County Level ◽  
Socioeconomic Indicators ◽  
Sensing Data ◽  
Fine Grained ◽  
The Chinese Mainland

The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research.

Access, Visualization, and Interoperability of Air Quality Remote Sensing Data Sets via the Giovanni Online Tool

2010 ◽  
Vol 3 (3) ◽  
pp. 359-370 ◽  
Author(s):  
Ana I. Prados ◽  
Gregory Leptoukh ◽  
Chris Lynnes ◽  
James Johnson ◽  
Hualan Rui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Online Tool ◽  
Sensing Data

scholarly journals Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador

Environments ◽  
2019 ◽  
Vol 6 (7) ◽  
pp. 85 ◽  
Author(s):  
Cesar I. Alvarez-Mendoza ◽  
Ana Claudia Teodoro ◽  
Nelly Torres ◽  
Valeria Vivanco
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Pm10 Concentration ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Data ◽  
Landsat 7 ◽  
Terra Modis

The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 µm diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index—NDVI; normalized difference soil index—NDSI, soil-adjusted vegetation index—SAVI; normalized difference water index—NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.

Sign in / Sign up

Export Citation Format

Share Document