Wildfires pose significant threats to ecosystems, economies, and human health. The integration of remote sensing (RS), geospatial information systems (GIS), and artificial intelligence (AI) has emerged as a powerful approach for addressing wildfire-related challenges. However, existing review studies typically focus on specific wildfire tasks and lack a comprehensive synthesis of how geospatial data and supervised AI techniques interact across the full wildfire management cycle. Therefore, this study aims to provide a meta-analysis review of the integration of RS, GIS, and supervised AI methods in wildfire science. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to systematically analyze 449 peer-reviewed journal articles published between 1994 and 2024. The review examines various wildfire-related tasks, data sources, algorithmic approaches, spatial scales, performance metrics, and other aspects used in wildfire geospatial AI (GeoAI) studies. The results reveal a strong concentration of research on tasks such as burned area mapping (BAM), wildfire detection, and susceptibility mapping, while critical areas, such as fuel mapping, wildfire vulnerability, and post-fire recovery, remain underexplored. The analysis also identifies a dominant use of traditional machine learning (ML) algorithms, such as Random Forest (RF), and an increasing adoption of deep learning (DL) models, particularly convolutional neural networks (CNNs). Furthermore, the geographic distribution of studies highlights significant global disparities, with most research conducted in high-income regions, while wildfire-prone areas in developing regions remain underrepresented. The review also reveals limited adoption of advanced AI techniques, including transfer learning, transformer architectures, Geo-foundation AI models, and explainable AI (XAI). These findings provide a comprehensive synthesis of GeoAI applications in wildfire management and highlight critical methodological, geographic, and application-level gaps. Addressing these gaps through improved data accessibility, adoption of advanced AI methods, and increased research focus on underrepresented wildfire tasks and regions will be essential for developing scalable, interpretable, and globally applicable wildfire management systems.

Identifying changes in the Earth’s phenomena is vital for understanding and mitigating the impacts of environmental issues. Monitoring the Earth’s surface phenomena can be carried out effectively using satellite images acquired at different times. In addition to spectral features, spatial features play a significant role in detecting precise changes. However, classical change detection (CD) methods rarely consider spatial information and fail to account for scale variations within images. The present study introduces a novel deep learning-based CD method that hierarchically extracts spatial–spectral features at various scales to address these issues. The proposed deep neural network generates a binary change map by employing a multi-scale approach that integrates the information of patches of varied sizes at the decision level. We conducted experiments using Landsat-8 images from Sahand City, East Azarbaijan, Iran, because of their remarkable capacity to represent the Earth’s surface details. Tabriz’s population growth has led to rapid development in Sahand City to accommodate citizens. Studying these changes can offer valuable insights into urban planning. The performance of the proposed deep model is evaluated in comparison to two classical methods, the change vector analysis (CVA) method and a random forest (RF) algorithm. Based on the change detection results, the proposed deep learning network demonstrates a significant improvement in the kappa coefficient (KC) compared to the RF and CVA methods, with increases of approximately 11.86% and 29.36%, respectively. Furthermore, in terms of overall accuracy (O.A.), the proposed network outperforms both the RF and CVA methods by approximately 17.08% and 29.16%, respectively. The proposed multi-scale deep network performs better at detecting changes across all metrics. As a result, the CVA method fails to identify changes with sufficient accuracy.