Abstract The Urban Climate and Resiliency-Science Working Group (ie, The WG) was
convened in the summer of 2018 to explore the scientific grand challenges related to climate …

The agriculture sector is highly vulnerable to natural disasters and climate change, leading
to severe impacts on food security, economic stability, and rural livelihoods. The use of …

Recently, agricultural remote sensing community has endeavored to utilize the power of
artificial intelligence (AI). One important topic is using AI to make the mapping of crops more …

The remote-sensing based Flood Crop Loss Assessment Service System (RF-CLASS) is a
web service based system developed and managed by the Center for Spatial Information …

Abstract Google Earth Engine (GEE) is an ideal platform for large-scale geospatial
agricultural and environmental modeling based on its diverse geospatial datasets, easy-to …

As an important tool for air quality simulation, the Community Multiscale Air Quality (CMAQ)
model is widely used in the environmental modeling community. However, setting up and …

AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention.
Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate …

Statistics show the volume of Earth Observation (EO) data increases in the exponential level
during the past decade. As the new generation computing platform to meet the big data …

Today environmental scientists heavily rely on geospatial web services (GWS). However,
many online facilities are under-utilized by the environmental modelling community because …

Scientific irrigation scheduling aims to solve the on-farm allocation of water, deciding
whether or not to irrigate, when, and determining the amount. Studies conducted in various …