The paper is dedicated to the integration of artificial intelligence, machine learning, and deep learning with geospatial science and technology as a key part of spatial analysis, called GeoAI. The embedded applications of GeoAI will allow us to solve the tasks related to classification, clustering and pattern detection, prediction and forecasting, and information extraction not only from sound and meteorological datasets but also from imagery, radar, videos, and unstructured text data. Nowadays it is a very important task for defence sphere in Ukraine. Hostile Artillery Locating Acoustic System has a low target location accuracy in warfare and low ability to identify the targets (among classes or types). To improve the accuracy of the system when processing the sound waves from combat system fire activity is possible using datasets from different functional systems. We analyzed the HALAS configurations for tthe sound source position prediction.
The paper is dedicated to the integration of artificial intelligence, machine learning, and deep learning with geospatial science and technology as a key part of spatial analysis, called . The embedded applications of GeoAI will allow us to solve the tasks related to classification, clustering and pattern detection, prediction and forecasting, and information extraction not only from sound and meteorological datasets but also from imagery, radar, videos, and unstructured text data. Nowadays it is a very important task for sphere in Ukraine. Hostile artillery locating acoustic system has a low target location accuracy in warfare and low ability to identify the targets (among classes or types). To improve the accuracy of the system when processing the sound waves from combat system fire activity is possible using datasets from different functional systems. We analyzed the HALAS configurations for tthe sound source position prediction.