Properties of spatially explicit data are often ignored or inadequately handled in machine
learning for spatial domains of application. At the same time, resources that would identify …

Scale is a central concept in the geographical sciences and is an intrinsic property of many
spatial systems. It also serves as an essential thread in the fabric of many other physical and …

Geospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that uses
artificial intelligence approaches and machine learning techniques for geographic …

Water quality experiments are difficult, costly, and time-consuming. Therefore, different
modeling methods can be used as an alternative for these experiments. To achieve the …

Downscaling land surface temperatures (LST) from satellite imagery is essential for many
fine-scale applications. However, the accuracy of the downscaling is often limited by …

Analyzing spatially varying effects is pivotal in geographic analysis. However, accurately
capturing and interpreting this variability is challenging due to the increasing complexity and …

Multiscale geographically weighted regression (MGWR) is an important method that is used
across many disciplines for exploring spatial heterogeneity and modeling local spatial …

The rising temperature is one of the key indicators of a warming climate, capable of causing
extensive stress to biological systems as well as built structures. Ambient temperature …

Urban greenspace has been widely recognized for its beneficial role in mitigating the urban
heat island (UHI) effect and enhancing human thermal comfort. However, understanding on …