This study employs a dual methodological approach, integrating Google Earth Engine (GEE) and unsupervised classification (UNSUP) to analyze urban expansion patterns in Chiang Mai province using nighttime light imagery. The research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data from 2014 to 2023 to assess urban growth dynamics. The primary objectives are to (1) evaluate the performance of GEE and UNSUP in nighttime light data processing, (2) validate urban area classification accuracy using multiple assessment metrics, and (3) examine the relationship between nighttime light intensity and electricity consumption through Pearson’s correlation analysis, thereby establishing urban growth patterns. The methodological framework incorporates a dual-threshold classification mechanism in GEE and K-means clustering in traditional geospatial software. Accuracy assessment is conducted using 256 stratified random sampling points, complemented by land use and land cover (LULC) data for ground truth validation. The results indicate that GEE consistently outperforms UNSUP, achieving overall accuracy values between 0.80 and 0.82, compared to 0.73 and 0.76 for UNSUP. The Kappa coefficient for GEE ranges from 0.60 to 0.65, whereas UNSUP demonstrates lower agreement with ground truth data (0.44–0.52). Furthermore, both approaches reveal a significant correlation between electricity consumption and nighttime light intensity, with R² = 0.9744 for GEE and R² = 0.9759 for UNSUP, confirming the efficacy of nocturnal illumination data in urban expansion monitoring. The findings indicate that urban areas in Chiang Mai have expanded by approximately 70% over the study period. This research contributes to the field by demonstrating the effectiveness of integrated geospatial methodologies in urban development analysis. The findings offer urban planners and policymakers critical insights for sustainable urban growth management and decision-making.

Traditional methods for assessing sunflower yield across large agricultural fields are typically labor-intensive and time-consuming. This study explores the integration of unmanned aerial vehicle (UAV) imagery and the YOLOv11 deep learning model for automated sunflower head detection and yield estimation. Aerial imagery was collected from sunflower fields using UAVs, and a YOLOv11-based detection model was developed to identify sunflower heads efficiently. Model performance was optimized by tuning the Confidence Threshold and Intersection over Union (IoU) parameters. A total of 1290 image tiles derived from 215 UAV images were used for model training and evaluation. The dataset was divided into training and testing subsets with an 80:20 ratio. The optimal configuration, achieved at a Confidence Threshold of 0.50 and an IoU Threshold of 0.40, yielded balanced and accurate results, including a Precision of 0.84, Recall of 0.95, mAP@0.5 of 0.95, and an F1-score of 0.90. The findings demonstrate that parameter adjustment directly influences model detection accuracy and reliability. Overall, this study confirms that combining UAV remote sensing with YOLOv11 offers a robust and scalable approach for automated sunflower yield estimation, significantly reducing manual effort and processing time. Moreover, the proposed framework can be adapted for other high-value crops, contributing to the advancement of intelligent and data-driven agricultural management systems.

Wildfires pose serious risks to ecosystems, air quality, and human health. Effective wildfire monitoring requires accurate detection and timely validation, but current approaches are often constrained by fragmented data sources, false alarms, and delays in field verification. This study presents WildfireGO, a multi-source wildfire detection and validation system that integrates crowdsourced observations, satellite hotspot data, and image-based classification in a geospatial monitoring environment. The system combines user-submitted images, Sentinel-2 imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data processed through Google Earth Engine (GEE) to support wildfire detection and verification. Four classification models, namely Convolutional Neural Network (CNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB), were evaluated using 10-fold cross-validation and an independent test dataset of 800 wildfire-related images. The CNN model produced the best result, with an accuracy of 97.5% on the independent test dataset. By combining image-based classification with crowdsourced reporting, the system helps screen user-submitted wildfire information and reduce false detections. Satellite-derived hotspot data provide spatial evidence for cross-checking reported events and improving spatial situational awareness for wildfire monitoring and response planning. WildfireGO supports near real-time data submission, automated processing, and interactive map-based visualization through a web-based interface. The findings indicate that combining crowdsourced reports, satellite observations, and image classification in a single geospatial system has the potential to support more reliable wildfire detection and provide practical support for environmental monitoring, disaster response, and spatial decision-making.