18 AI-Driven and Machine Learning Approaches in Geospatial Modeling for Hydrologic and Geologic Mapping

This session features GIS, remote sensing, and AI-driven/machine learning approaches to geologic and hydrologic mapping, emphasizing predictive modeling, cross-scale analysis, and innovative workflows that extend beyond traditional cartographic methods.

Tuesday, 10 March 2026
8:30 AM - 9:30 AM
RCC, 104

Modeling Urban Expansion and Heat Risk in Northwest Arkansas Using Remote Sensing and Machine Learning: A Multi-Scale Assessment of Land Use Change and Surface Temperature Dynamics

Paper number: 18-1
Program: Technical Sessions
Time: 8:45 AM – 9:00 AM

Authors

Abstract

Arkansas, known as the "Natural State", is experiencing significant urban expansion and land use changes, impacting environmental sustainability. This study investigates the relationship between land surface temperature (LST) and land use/land cover (LULC) changes in Northwest Arkansas (NWA), aiming to inform urban planning and sustainable development strategies. It evaluates the potential of advanced machine learning techniques, specifically convolutional neural networks, to accurately predict future LST and LULC changes, aiding in land use planning and disaster management. Additionally, it examines the presence of urban heat islands in NWA cities and how insights into LST and LULC changes can support long-term land use planning and climate resilience. The study provides empirical data and methodological insights, promoting further research in deep learning, urban sustainability, and climate studies. Utilizing Landsat-5/-8 data, the analysis focuses on seasonal land cover indices and correlations between seasonal LST and LULC changes in NWA from 2001 to 2021, exploring how LULC affects temperature dynamics, especially LST and urban heat islands. Furthermore, it simulates future urbanization conditions using advanced machine learning algorithms, including the cellular automaton (CA) - Markov chain algorithm, to forecast future temperature and land use changes and XGboost to predict LST. This research sets the stage for future studies to enhance understanding and knowledge of urban sustainability and climate resilience.

Geological Society of America Abstracts with Programs. Vol. 58, No. 1

doi: 10.1130/abs/2026SE-411579

© Copyright 2026 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.