GIS data and AI-driven environmental monitoring using remote sensing

¹ University of Friendship of People’s Academician A. Kuatbekov, Shymkent, Kazakhstan
² Kokshetau University named after Sh. Ualijhanov, Kokshetau, Kazakhstan
³ Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkistan, Kazakhstan

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Amid global climate change and escalating human impact on the environment, the necessity for the development of effective and scalable environmental monitoring systems is intensifying. Conventional observation techniques reliant on terrestrial measurements and expert evaluations exhibit restricted spatial coverage and lack the capacity for swift analysis of dynamic environmental phenomena. The amalgamation of remote sensing data, geographic information systems (GIS), and artificial intelligence techniques signifies a promising domain in contemporary environmental research. This paper introduces an AI-based methodology for environmental monitoring utilizing satellite data and GIS spatial information. The proposed methodology encompasses the preprocessing and integration of multidimensional spatiotemporal data, the extraction of informative features, and the application of machine and deep learning algorithms to analyze environmental conditions. Artificial intelligence techniques facilitate the automation of land cover classification, the identification of environmental changes, and the prediction of potential risks. The study’s findings indicate that the incorporation of AI models with remote sensing and GIS data enhances monitoring precision and resilience relative to conventional methods. Moreover, the proposed system improves the interpretability of outcomes and facilitates decision-making in environmental management and sustainable development. The results validate the practicality of employing intelligent technologies for thorough environmental analysis and underscore their considerable potential for environmental monitoring across diverse spatial scales.