A Topological Machine Learning Approach with Multichannel Integration for Detecting Geospatial Objects
Meirman Syzdykbayev, Bobak Karimi, Hassan A. Karimi · Big Data · 2024
Geospatial object detection plays a key role in geospatial data analysis and is used in a variety of applications. Geospatial objects can be detected through computer vision and machine learning (ML) models and algorithms by focusing on geometrical and/or contextual information. However, there are challenges with current geospatial object detection models and algorithms, including geospatial data noise and feature representation. In addition to geometrical and contextual information, geospatial datasets contain topological information, which is often not considered for detection. This chapter tests the hypothesis that incorporating information on the shape of geospatial objects, that is, topological information, may address these challenges and improve detection accuracy. One way to incorporate topological information, along with geometrical or contextual information, is by using methods from topological data analysis such as Persistent Homology (PH) and Mapper. The research in this chapter is focused on the development of a method that utilizes topological information to detect geospatial objects. Explored in this research is incorporation of topological information into machine-learning (ML)-based geospatial object detection method, where topological information is transformed into a multichannel image as an additional feature. To test the results of the proposed method, its performance was evaluated by detecting the boundaries of known landslide deposits.