Abstract
Abstract. For quick and efficient response, as well as for recovery after any natural or artificial catastrophe, one of the most important things are accurate and reliable spatial data in real or near real-time. It is essential to know the location as well as to track and analyse passive and active threats to quickly identify the possible dangers and hazards. As technology evolves and advances, there is a broader spectrum of sensors that provide spatial data, and nowadays, decision-making processes also include nontraditional, informal sources of information. Apart from the offer, demand for new spatial data is increasing as well. For quicker and enhanced integration and analysis of data, artificial intelligence (AI) tools are increasingly used which, in addition to immediate rapid reactions, can help to make better and smarter decisions in the future. Such software algorithms that imitate human intelligence can help in generating conclusions from natural phenomena presented by spatial data. Using AI in the data analysis can identify risk areas and determine future needs. This paper presents an overview of the use of AI in geospatial analysis in disaster management.
Highlights
Natural disasters are not so rare events and happen all around the world causing a lot of material damage and, sometimes loss of human lives
Deep learning (Figure 2) can be defined as a technique for implementing machine learning algorithms inspired by the structure and function of the human brain called artificial neural networks, which are composed of multiple processing layers to use multiple abstraction data (LeCun et al 2015)
Areas are divided into small grids, while Disaster Impact Index (DII) is a normalized pixelwise change over the grid calculated according to number of pixels in the grid which contain detected feature in pre-disaster, but not in the post-disaster mask (Doshi et al 2018)
Summary
Natural disasters are not so rare events and happen all around the world causing a lot of material damage and, sometimes loss of human lives. In dealing with such unwanted events, the most important thing is existence of different kinds of data, which are nowadays available more than ever before. In disaster management, data analysis is often needed in real-time or near real-time, which is not an easy task when performed manually. Such and similar issues can be addressed by applying methods of artificial intelligence. The second part of the paper gives an overview of the use of AI in geospatial analysis in disaster management through the overview of the case studies
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