## 1 _Editorial_  
Geospatial AI in Earth Observation, Remote Sensing, and GIScience  
[PERSON], [PERSON], [PERSON] and [PERSON],*(c)  
Geospatial artificial intelligence (Geo-AI) methods have revolutionarily impacted earth observation and remote sensing. With the advancement of deep-learning algorithms and the availability of high-resolution geospatial data, significant strides have been made in geospatial artificial intelligence. Additionally, complex analysis combined with Geo-AI has significantly advanced spatial analysis, providing a better platform for capturing the dynamic processes of spatial changes.  
Multimodal methods enrich geographic information science by integrating data from multiple angles, spectra, platforms, and scales, allowing for the effective combination and utilization of complementary information. Nonetheless, multimodal artificial intelligence structures require substantial computational demands. Consequently, there is an emphasis on streamlining the algorithm structure and pivoting towards data-driven machine learning to simulate reality within a relatively simple framework.  
This Special Issue spotlights the latest developments in Geo-AI method applications in earth observation and remote sensing. This collection serves as a conduit for scholars in the affiliated disciplines to present their distinct perspectives and most recent accomplishments.  
This Special Issue highlights six seminal papers. While they do not encompass every facet of \"Geospatial artificial intelligence\", they do echo the most recent innovations and developments in this domain. These articles can be broadly categorized into the following topics.  
11  
introducing innovative data sources for multi-source remote sensing data. Currently, deep-learning algorithms used for landslide detection have high computational complexity and memory requirements, and lightweight algorithms can significantly alleviate these problems. In 'A Lightweight and Partitioned CNN Algorithm for Multi Landslide Detection in Remote Sensing Images', [PERSON] et al. [2] proposed a lightweight LP-YOLO algorithm based on YOLOv5 and applied it to automatic landslide monitoring. This algorithm can mine and aggregate remote information in two directions while consuming less computational costs. The algorithm used the SCYLA-IoU (SIoU) boundary box regression loss function to replace the complete IoU (CIoU) loss function and proposed a new feature fusion structure. The experimental results showed that the model proposed by the author has better monitoring performance compared to the YOLOv5 algorithm, providing technical guidance for achieving reliable and real-time automatic landslide detection.  
The systematic evaluation of geological disasters and the comprehensive risk assessment of extreme climate conditions can provide important references for effectively implementing disaster prevention and reduction work. In the study titled 'Analysis and Evaluation of Extreme Rainfall Trends and Geological Hazards Risk in the Lower Jinshajiang River', [PERSON] et al. [3] selected the lower reaches of the Jinsha River as the research area for geological hazard risk assessment. The authors used the time series and trends of extreme precipitation events in the region over the past 60 years, identified nine key indicators for landslide susceptibility evaluation, and conducted a risk assessment on geological disasters such as landslides and debris flows. The geological hazard zoning under extreme rainfall trends was derived using the GIS spatial analysis function. After their analysis, the authors concluded that terrain is the main controlling factor of geological disasters and that human engineering activities and rainfall are the main triggering factors for geological disasters. The research results of this article lay a foundation for informed planning and strategies geared toward geological disaster prevention and control planning in the Yangtze River Basin.