Food security has been an ongoing concern of governments and international organizations. This study aims at ensuring food security using Geospatial technology to find suitable sites for building food industry plants. In this respect, Geospatial technology including remote sensing, Geospatial information system, and global positioning system was implemented to identify suitable sites for the construction of food industry plants in Qaemshahr County, Iran and an up-to-date and efficient geodatabase for this purpose was prepared. Since rice and citrus are the main products of the study area, to ensure food security and income for the people of the region, suitable site selection to build food industry plants to sort, package, store, convert, and process rice and citrus is very important. On the other hand, because Ghaemshahr is the main hub of rice and citrus production in the country as well as export, food security at the national level can also be ensured. To support the achieved result of selected sites for food industry plants, several analyses were carried out. The resulting suitability map was reclassified based on proximity to raw food materials as well as main roads and cities. Consequently, among the obtained suitable sites, the most suitable places based on these factors were identified. In addition, a comparative analysis was performed between the selected sites and the existing industrial zones in the study area. The findings of this study are useful to ensure food security in the region and country. The study also demonstrated the effectiveness of Geospatial technology for this purpose.

Accurate crop classification is crucial for agricultural monitoring and decision-making. Remote sensing's ultimate goal is the precise extraction and classification of crops. Based on a cloud platform, the study area of Guntur district, Andhra Pradesh India, presents a multi-crop classification approach using Sentinel-2 satellite imagery and machine learning techniques. The study area encompasses a diverse agricultural region with three major crop types. After pre-processing, spectral and textural features were extracted. It compares the traditional four machine learning algorithms employed, adding the NDVI, NDBI, MNDWI, and BSI vegetation indices for multi-crop classification enhances accuracy, and offers diverse and complementary information. The overall classification accuracy achieved 95%, with individual crop accuracies ranging from 85 to 96%. The scalable and simple classification method proposed in this research gives full play to the advantages of cloud platforms in data and operation, and the traditional machine learning compared with other algorithms can effectively improve the classification accuracy, and individual areas of crop production are calculated. The results underscored the reliability of GEE-based crop mapping in the region, demonstrating a satisfactory level of classification accuracy, surpassing 97% across distinct time intervals in overall accuracy values, Kappa coefficients, and F1-Score.

Background: The COVID-19 pandemic has shattered the global health system and caused havoc worldwide. Since there is no proper medication, non-pharmaceutical intervention methods are followed to mitigate viral transmission. For its proper implementation, it is crucial to track the spatial pattern of transmission and target those areas which require immediate action to control the spread of the pandemic. The Geospatial technologies have established themselves as powerful tools that have substantial ability to track outbreak patterns in real-time, identify at-risk populations, and plan targeted intervention. Methods: The study provides a robust methodological framework with three Geospatial tools: Spatial Autocorrelation (Global Moran’, s I), Hot Spot Analysis (Getis-Ord Gi*), and Space-time scan statistic. The quantitative study was carried out exclusively for North-East India to track the COVID-19 outbreaks from April 2020 to December 2020. Results: The results obtained indicate a gradual change of spatial distribution of the disease from cluster to random distribution at the global scale. But, eventually, the COVID-19 cases tend to form clusters again. Kamrup Metro, the district with the highest urban population, was reported constantly as a hotspot. Moreover, space-time clusters tend to expand in size over time. Conclusion: The research study’, s findings provide an overview of the spatio-temporal pattern of COVID-19 in the study area and help the health officials and policy-makers in formulating effective management strategies and non-pharmaceutical intervention measures by targeting the high-risk areas. The study is a valuable guide towards implementing Geographic Information Science technologies in monitoring and tracking the pandemic.