[HTML][HTML] A review of spatially-explicit GeoAI applications in Urban Geography
P Liu, F Biljecki - International Journal of Applied Earth Observation and …, 2022 - Elsevier
Urban Geography studies forms, social fabrics, and economic structures of cities from a
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …
[HTML][HTML] Artificial intelligence in agricultural map**: A review
R Espinel, G Herrera-Franco, JL Rivadeneira García… - Agriculture, 2024 - mdpi.com
Artificial intelligence (AI) plays an essential role in agricultural map**. It reduces costs and
time and increases efficiency in agricultural management activities, which improves the food …
time and increases efficiency in agricultural management activities, which improves the food …
Neural network guided interpolation for map** canopy height of China's forests by integrating GEDI and ICESat-2 data
Spatially continuous estimates of forest canopy height at national to global scales are critical
for quantifying forest carbon storage, understanding forest ecosystem processes, and …
for quantifying forest carbon storage, understanding forest ecosystem processes, and …
Substantial terrestrial carbon emissions from global expansion of impervious surface area
Global impervious surface area (ISA) has more than doubled over the last three decades,
but the associated carbon emissions resulting from the depletion of pre-existing land carbon …
but the associated carbon emissions resulting from the depletion of pre-existing land carbon …
Understanding place characteristics in geographic contexts through graph convolutional neural networks
Inferring the unknown properties of a place relies on both its observed attributes and the
characteristics of the places to which it is connected. Because place characteristics are …
characteristics of the places to which it is connected. Because place characteristics are …
A hybrid deep learning model for regional O3 and NO2 concentrations prediction based on spatiotemporal dependencies in air quality monitoring network
Short-term prediction of urban air quality is critical to pollution management and public
health. However, existing studies have failed to make full use of the spatiotemporal …
health. However, existing studies have failed to make full use of the spatiotemporal …
Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images
It is important to predict the spatial distribution of SOC accurately for migrating carbon
emission and sustainable soil management. Environmental variables influence the accuracy …
emission and sustainable soil management. Environmental variables influence the accuracy …
Explainable spatially explicit geospatial artificial intelligence in urban analytics
Geospatial artificial intelligence (GeoAI) is proliferating in urban analytics, where graph
neural networks (GNNs) have become one of the most popular methods in recent years …
neural networks (GNNs) have become one of the most popular methods in recent years …
[HTML][HTML] Advances in geocomputation and geospatial artificial intelligence (GeoAI) for map**
Geocomputation and geospatial artificial intelligence (GeoAI) have essential roles in
advancing geographic information science (GIS) and Earth observation to a new stage …
advancing geographic information science (GIS) and Earth observation to a new stage …
Accurate prediction of soil heavy metal pollution using an improved machine learning method: a case study in the Pearl River Delta, China
W Zhao, J Ma, Q Liu, L Dou, Y Qu, H Shi… - Environmental …, 2023 - ACS Publications
In traditional soil heavy metal (HM) pollution assessment, spatial interpolation analysis is
often carried out on the limited sampling points in the study area to get the overall status of …
often carried out on the limited sampling points in the study area to get the overall status of …