Updating and digitizing cadastral maps remains a major challenge in land administration, demanding significant financial and human resources. This study presents a fully automated AI-based system to address this issue, focusing on the extraction and digitization of agricultural cadastral maps using photogrammetric images. The proposed method leverages the Segment Anything Model (SAM) for high-accuracy segmentation, achieving a notable Intersection over Union (IoU) score of 92%, significantly outperforming traditional approaches. Additionally, the system reduces processing time by 40% and eliminates the need for manual intervention, enabling scalable, efficient digitization. These improvements are critical for better land-use planning, resource allocation, and sustainable land management practices. The model, implemented using open-source Python libraries, integrates three stages: image preprocessing, AI-based segmentation, and postprocessing. By automating these processes, the system not only accelerates map production but also reduces environmental impacts associated with traditional mapping techniques. The approach also enhances the accuracy of agricultural boundary delineation, offering benefits for land dispute resolution and optimized agricultural practices. This research contributes to the modernization of land administration systems by providing an accessible, scalable solution for surveyors and policymakers. It bridges the gap between cutting-edge artificial intelligence advancements and practical applications, addressing technical and operational challenges in geospatial data management. The findings underscore the importance of automating cadastral mapping for both economic efficiency and environmental sustainability.

Recent advancements in remote sensing technology have significantly expanded the exploration of natural resources and enabled the detection of materials in inaccessible areas. Hyperspectral images (HSIs) are a valuable data source due to their distinctive properties in various applications. However, several problems, including noise, band correlation, ineffectively extracted features, and most notably, a lack of sufficient labeled samples reduce the accuracy of HSI classification. To improve the performance of such a system, we propose an effective method with the capability of making attention to spectral and spatial features. The raw HSI data is first preprocessed using a Principal Component Analysis (PCA) operation because of the redundancy and correlation between HSI bands. Then, the Entropy Base Informative Module (EBIM) is designed to add entropy information to the selected spectral features by PCA. We also use spectral and spatial attention modules in the proposed model. Moreover, a hybrid neural network that uses both 3D CNNs and 2D CNNs with skip connections is exploited to reduce the complexity of the network compared to 3D CNNs. The spatial attention module called Depth-wise Spatial Attetnion (DSA) block can inherently highlight spatial information. The spectral attention module named Reshape Softmax Attention (RSA) can capture useful spectral regions of feature maps. Meticulous HSI classification tests are conducted over the University of Pavia (PU), Indian Pines (IP), Salinas (SA), and Houston 2013 (HT) to evaluate the effectiveness of our approach. Our experiments show higher accuracy compared to other deep learning methods.

Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as maximizing the coverage. After tuning the parameters of the algorithms using sensitivity analysis, they were used separately to process data for Region 11, Tehran. The results showed that the genetic algorithm was more efficient than simulated annealing, and therefore, the genetic algorithm was used in later steps. Next, we increased the number of stations. Results showed that the model can successfully provide seven optimal locations and allocate high demands (280,000) to stations in a discrete space in a GIS, assuming that the stations’ capacities are known. Following this, we used a weighting program so that in each repetition, we could allot weights to each target randomly. Finally, by repeating the model over 10 independent executions, a set of solutions with the least sum and the highest number of non-dominated solutions was selected from among many non-dominated solutions as the best set of optimal solutions.

Advancement of sustainable agriculture techniques and ensuring food security depend on accurate and reliable crop yield forecasting. Accurate and reliable crop yield forecasting is essential for sustainable agricultural planning and global food security. However, data quality issues—such as missing values and temporal misalignments in remote sensing datasets—often challenge the robustness of machine learning models. This study proposes a robust yield prediction framework that integrates multi-source data, including MODIS-based Gross Primary Production (GPP), vegetation indices (NDVI, EVI), climate variables, and soil properties, to estimate maize yield at the county level across the U.S. Corn Belt. Two ensemble models, Random Forest and Extreme Gradient Boosting (XGBoost), are trained and evaluated under both clean and simulated degraded data conditions. XGBoost achieved the highest accuracy (RMSE = 14.58, R² = 0.84), while Random Forest demonstrated strong robustness (RMSE = 15.10, R² = 0.82), even when early-season NDVI was missing or GPP time series were temporally shifted. Feature importance analysis identified late-season GPP and soil organic matter as the most influential predictors. These findings demonstrate the potential of ensemble learning models to deliver reliable and interpretable yield forecasts, even under imperfect data conditions, making them practical tools for real-world precision agriculture applications. This work provides a practical framework for implementing AI-driven solutions in large-scale, real-world remote sensing-based agricultural monitoring and yield forecasting systems.