Employee Perspective on the Impact of using GIS on Highway and Transportation Construction Project Success

This survey research effort investigated the Geographic Information System (GIS) functions in highway and transportation construction-project success based on employee perspective. The population included engineers and IT professionals in the United States. The sample included members of various organizations who are classified under the construction industry. The high percentage of respondents who reported little knowledge about how to use the GIS functions may cause erroneous conclusions about the use of the GIS. The lack of knowledge was the primary reason that people in the field do not use the GIS appropriately. Moreover, it seems that executives have more skills or knowledge about the GIS than those in lower-level positions

Using Queuing Analysis to Define Appropriate Staffing Levels in a Municipality

This report provides an analysis and evaluation of the current process in the construction departments of the sub-municipalities of Riyadh city in Saudi Arabia. The queue model was used with a G/G/s template to find the current expected wait in the queue. Several mathematical formulas were found and used for the queue analysis. Two different plans were developed to reduce waiting time in the queue (Plans A and B). Results of data analyzed show that there are different waiting times found in each sub-municipality (branch). Plans A and B were then applied to accelerate the waiting time in the queue. The plan with the lowest cost was chosen, if it met the target waiting time. Two additional ways were found to improve the work: (a) shifting engineers from overstaffed offices to branches in need of additional help, without applying either Plan A or Plan B in the branches to which they were shifted; or (b) shifting engineers from overstaffed offices to branches in need of additional help and applying Plans A or B in the branches to which they were shifted. It is recommended that engineers from overstaffed branches be shifted to branches in need of additional help without applying either Plan A or Plan B in branches to which they were shifted. This recommendation will reduce waiting time in the queue as much as possible with the least cost, which is only $16,000/month

Geospatial Analysis and Optimization Techniques to Select Site for New Business: The Case Study of Washtenaw County, Michigan, USA

Geographic information system (GIS) can provide an optimal solution in case of site selection. Therefore, using GIS is a common practice for spatial decision support. The study area and The purpose of this study was to find the best location for a new Wal-Mart store in Washtenaw County, Michigan, USA. population, distance from existing stores, land uses, and slope were factors that considered in this research

Improving Generalization for Hyperspectral Image Classification: The Impact of Disjoint Sampling on Deep Models

Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models e.g., Attention Graph and Vision Transformer. When training, validation, and test sets overlap or share data, it introduces a bias that inflates performance metrics and prevents accurate assessment of a model’s true ability to generalize to new examples. This paper presents an innovative disjoint sampling approach for training SOTA models for the Hyperspectral Image Classification (HSIC). By separating training, validation, and test data without overlap, the proposed method facilitates a fairer evaluation of how well a model can classify pixels it was not exposed to during training or validation. Experiments demonstrate the approach significantly improves a model’s generalization compared to alternatives that include training and validation data in test data (A trivial approach involves testing the model on the entire Hyperspectral dataset to generate the ground truth maps. This approach produces higher accuracy but ultimately results in low generalization performance). Disjoint sampling eliminates data leakage between sets and provides reliable metrics for benchmarking progress in HSIC. Disjoint sampling is critical for advancing SOTA models and their real-world application to large-scale land mapping with Hyperspectral sensors. Overall, with the disjoint test set, the performance of the deep models achieves 96.36% accuracy on Indian Pines data, 99.73% on Pavia University data, 98.29% on University of Houston data, 99.43% on Botswana data, and 99.88% on Salinas data

Spatial Spectral Transformer with Conditional Position Encoding for Hyperspectral Image Classification

In Transformer-based Hyperspectral Image Classification (HSIC), predefined positional encodings (PEs) are crucial for capturing the order of each input token. However, their typical representation as fixed-dimension learnable vectors makes it challenging to adapt to variable-length input sequences, thereby limiting the broader application of Transformers for HSIC. To address this issue, this study introduces an implicit conditional PEs (CPEs) scheme in a Transformer for HSIC, conditioned on the input token’s local neighborhood. The proposed SSFormer integrates spatial-spectral information and enhances classification performance by incorporating a CPE mechanism, thereby increasing the Transformer layers’ capacity to preserve contextual relationships within the HSI data. Moreover, SSFormer ensembles the cross-attention between patches and proposed learnable embeddings. This enables the model to capture global and local features simultaneously while addressing the constraint of limited training samples in a computationally efficient manner. Extensive experiments on publicly available HSI benchmarking datasets were conducted to validate the effectiveness of the proposed SSFormer model. The results demonstrated remarkable performance, achieving classification accuracies of 97.7% on the Indian Pines dataset and 96.08% on the University of Houston dataset

Adopting a new approach for finding missing people using GIS techniques: A case study in Saudi Arabia’s desert area

Every year, hundreds of people go missing in the wilderness of Saudi Arabia. There is an urgent need to examine modern geographic techniques for finding such people. Geographical information systems, for example, play a crucial role in wilderness search and rescue (WiSAR), not only in mapping probability areas but also in applying further analysis and modeling methods to reduce time and effort and to guide life-saving task forces in the right direction. In this study of a hypothetical missing-person case in Saudi Arabia, two standard WiSAR models are compared: ring and mobility. In the presented study situation, both models can be used. However, the new approach used in the mobility model drastically reduces the extent of the possible search area, from 101,787 km2 in the ring model to 335.34 km of likely trails and unpaved roads, and also provides exact directions to where the missing person may be found

Assessing the Hazard Degree of Wadi Malham Basin in Saudi Arabia and Its Impact on North Train Railway Infrastructure

The North Train Railway in the Kingdom of Saudi Arabia (KSA) extends over vast areas, crossing various terrains, including valleys, sand veins, plateaus, and hills. Therefore, the railway was designed and implemented to suit this environmental diversity under the highest safety standards. However, the railway may be subject to hazards for various reasons. In general, the possibility of direct surface runoff disasters increases if there are residential areas and facilities within the boundaries of drainage basins. Therefore, these areas should be studied, and the degree of hazard in drainage basins should be accurately determined. Hence, this study analyzed the degree of risk of 14 drainage basins affecting the North Train Railway within the Wadi Malham drainage basin. The risk degree model was used with eight parameters that have hydrological indications to give an idea of the behavior of direct surface runoff and alter the risk of direct surface runoff. We found that 28.57% of the total basins in the study area have overall score values indicating they are high-risk basins, namely basins 6, 7, 13, and 14. It is recommended to estimate the rainfall depth during different return periods, analyze soil permeability and land use classification in the study area, and apply hydrological modeling of drainage basins, which contributes to estimating the volume and peak of direct surface runoff in such arid and semi-arid environments that do not contain hydrometric stations to monitor the runoff

Multilayer Perceptron for the Future Urban Growth of the Kharj Region in 2040

Urban growth is described as an increase in the size and use of cities, which is frequently the consequence of an increase in the number of residents due to internal or external migration and an increase in economic activity rates. In recent decades, modern technology and mathematical models have been used to determine future urban growth on a large scale and develop sustainable urban policies in the long term. The cities of the Kingdom of Saudi Arabia have witnessed economic growth in recent decades, which has resulted in urban expansion, as is evident in this case study of the Kharj region. Since most of the previous studies have not applied mathematical models to predict the urban growth of the Kharj region, this study aims at simulating urban growth over the next two decades, between 2020 and 2040, by monitoring the growth during the past thirty years, which is the period between 1990 and 2020. This study relies on the satellite visualizations of the Landsat satellites 5, 7, and 8 for classifying the land cover by applying the land change model (LCM) and comparing the land-use maps for the years 2000 and 2020. Then, the factors affecting urban growth, such as distance from the city center, the road network, valleys, and land slopes, are determined to monitor the prediction of urban growth. The results showed that the urban areas extended significantly toward the south, southeast, southwest, and northwest, with an area of 269 km². The results further revealed a significant decline in agricultural and vacant lands due to their transformation into residential areas, educational establishments, and industrial facilities. The model’s accuracy was tested to confirm the mathematical model’s validity. The Kappa index findings indicated a high percentage, ranging from 89% in 2010 to 90% in 2020

Pyramid Hierarchical Spatial-Spectral Transformer for Hyperspectral Image Classification

The Transformer model encounters challenges with variable-length input sequences, leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical Spatial-Spectral Transformer (PyFormer). This innovative approach organizes input data hierarchically into pyramid segments, each representing distinct abstraction levels, thereby enhancing processing efficiency. At each level, a dedicated Transformer encoder is applied, effectively capturing both local and global context. Integration of outputs from different levels culminates in the final input representation. In short, the Pyramid excels at capturing spatial features and local patterns, while the Transformer effectively models spatial-spectral correlations and long-range dependencies. Experimental results underscore the superiority of the proposed method over state-of-the-art (SOTA) approaches, achieving overall accuracies of 96.28% for the Pavia University dataset and 97.36% for the University of Houston dataset. Additionally, the incorporation of disjoint samples augments robustness and reliability, thereby highlighting the potential of PyFormer in advancing HSIC. The source code is available at https://github.com/mahmad00/PyFormer