Ali Mohammadzadeh

Forest canopy height (FCH) is a critical biophysical parameter that characterizes forest structure and provides fundamental information for estimating above-ground biomass and carbon stocks. The Global Ecosystem Dynamics Investigation (GEDI) Level 2A (L2A) product offers accurate canopy height observations; however, its point-based nature constrains spatial continuity in FCH mapping. This study integrates the multimodal remote sensing datasets for continuous FCH mapping in Tanzania’s West Usambara (WUSA) forest, recognized globally for its rich biodiversity and ecological significance. Hence, remote sensing data, including Sentinel-1 polarizations (VV and VH), Sentinel-2 spectral bands and vegetation indices, and the SRTM digital elevation model (DEM), were integrated and matched with GEDI canopy height data used as reference for FCH modelling. The optimal feature set was derived by evaluating the performance of several feature selection and extraction methods, including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), Recursive Feature Elimination (RFE), Sequential Feature Selection (SFS), and the Selected K-Best approach using F-value and mutual information scoring functions. The feature set derived from RFE, comprising ten features from all data sources, demonstrated the highest accuracy and reliability in FCH modelling. Subsequently, four machine learning algorithms, including Random Forest (RF), Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Ordinary Least Squares (OLS), were evaluated for FCH modelling. Accordingly, RF achieved higher R2 than GBR, SVR, and OLS, with differences of 0.9%, 8.7%, and 16.4%, respectively. Therefore, the RF model, as the most reliable model, was employed for FCH mapping across the WUSA forest.

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.

Earthquakes can cause considerable damage to transportation infrastructure, affecting emergency response. Timely detection of road debris is important in such situations. The purpose of this research is to address the challenges related to the rapid detection of collapsed areas and road blockages. Deep learning has become an essential tool in remote sensing and image processing, offering improved capabilities for classification, and segmentation from high-resolution imagery. Its integration into post-disaster analysis enables more accurate assessments compared to traditional methods. This study examines the performance of three commonly used semantic segmentation models Unet, Attention Unet (AttUnet), and ResUnet++ for road debris detection. Two earthquake events were used as case studies: the 2023 earthquake in Osmaniye, Türkiye (Mw 7.8), captured by the Pleiades satellite, and the 2017 earthquake in Sarpol-e Zahab, Kermanshah, Iran (Mw 7.3), captured by a Phantom 4 Pro drone. The models were evaluated using three metrics: Intersection over Union (IoU), Recall, and Accuracy. The results show that ResUnet++ achieved higher performance compared to Unet and AttUnet in both cases. For the Osmaniye dataset, ResUnet++ reached an IoU of 80.81%, Recall of 78.88%, and Accuracy of 96.12%. On the Sarpol-e Zahab dataset, it obtained an IoU of 81.62%, Recall of 80.28%, and Accuracy of 97.24%. Unet and AttUnet performed at lower levels across all evaluated metrics. This comparison provides a clear assessment of model performance in post-earthquake debris detection tasks and contributes to ongoing work in the application of deep learning and high-resolution imagery for geospatial analysis in disaster response contexts.

Binary change detection (BCD) using heterogeneous optical and SAR imagery faces challenges due to modality-specific noise and the lack of adaptive fusion strategies. Existing methods often fail to suppress SAR speckle noise and accurately localize fine boundaries. This study proposes a novel deep architecture, termed Dual-Modal Mixture-of-Experts Attention U-Net (DMoE-AttU-Net), featuring (i) dual-stream encoders for modality-specific feature extraction, (ii) a mixture-of-experts (MoE) module in the SAR stream with a gating network for dynamic fusion, (iii) Squeeze-and-Excitation (SE) and spatial attention mechanisms in the decoder, and (iv) hierarchical skip connections for multi-scale fusion. Unlike existing multimodal change detection frameworks that apply uniform feature fusion, the proposed architecture introduces a modality-aware design in which the MoE mechanism is selectively applied to the SAR stream, enabling adaptive suppression of speckle noise while preserving complementary optical information. These components collectively enhance change localization and reduce noise-induced artifacts. The proposed model achieved a mean IoU of 0.855 and a kappa coefficient of 0.836 on three optical–SAR datasets, outperforming state-of-the-art methods in both accuracy and spatial consistency.