Search Results for author:
Found 4 papers, 0 papers with code
PlumeCityNet: Multi-Resolution Air Quality Forecasting
Then, the engine is based on a U-Net architecture built with several of those blocks, giving it the ability to process inputs and to output predictions at different resolutions.
High-Resolution Air Quality Prediction Using Low-Cost Sensors
An other strong conclusion is that in some areas with a high density of low-cost sensors, the engine performs better when fed with low-cost sensors' measurements only than when fed with official monitoring stations' measurements only: this suggests that an air quality monitoring network composed of low-cost sensors is effective in monitoring air quality.
PlumeNet: Large-Scale Air Quality Forecasting Using A Convolutional LSTM Network
This paper presents an engine able to forecast jointly the concentrations of the main pollutants harming people's health: nitrogen dioxyde (NO2), ozone (O3) and particulate matter (PM2. 5 and PM10, which are respectively the particles whose diameters are below 2. 5 um and 10 um respectively).
DeepPlume: Very High Resolution Real-Time Air Quality Mapping
Plume Labs has deployed a similar prediction engine to build several products aiming at providing air quality data to individuals and businesses.
