GeoAI

Cities play an essential role in low-carbon development. However, Estimating CO₂ emissions at the urban scale, including both un-gridded (i.e., administrative unit maps) and gridded maps, cannot avoid the propagation of uncertainties from input to result, which highlights the importance of being aware of uncertainty estimation, especially in gridded maps due to its implications for the precision mitigation of CO₂ emissions. We proposed an analytic workflow to analyze the propagated uncertainties caused by the gridded model and the input for gridded CO₂ emission maps.

Using Tencent location based service data and machine learning mapping produced by multi-sources geospatial data to investigate the health risk of short-term exposure to PM2.5.

To introduce what geography is and the three laws of geography to high school students.

We develop street view imagery-based urban visual intelligence, from SVI coverage generation to GeoAI and LLM visual-semantic sensing, to quantify eye-level built environments and support urban management.

Cities are typical sources of carbon and a focus of climate-change mitigation. Although there is great potential for reducing emissions in cities, constructing low-carbon emission cities under the carbon-emission reduction target of the 2016 Paris …

We present a low-cost method to create high-precision, spatially explicit reference maps of large-scale forest aboveground biomass (AGB) to provide a scientific basis for quantitative assessment of forest management decisions involving, for example, …

To introduce our study on gridded CO₂ emissions at the urban scale and the quantification of the uncertainties.

We want to know how the pattern of grassland and water influence the hydrological process of cities.

To introduce our study on the environmental effect of urban form on PM2.5 using multi-source data.