نشریه پژوهش های سنجش از دور و اطلاعات مکانی
پیاپی 2 (تابستان و پاییز 1402)

In recent decades, the complex interaction between environmental factors and public health has attracted the attention of researchers, policy makers, and public health practitioners. Understanding how environmental factors affect human health is very important in dealing with citizens' health challenges. In recent years, geographic information systems (GIS) have been used as practical tools in this field and have enabled the investigation and analysis of complex relationships between environmental variables and health. These systems are a useful tool for zoning diseases, and with the spatial distribution of some diseases, significant results can be achieved. Results such as that some diseases are related to environmental factors. Diagnosing environmental factors in the direction of treatment, prevention and reduction of healthcare costs is a way to develop health. This article deals with the spatial analysis of some environmental factors affecting health in order to provide solutions to prevent the occurrence of pathogenic factors in citizens.

The research method of performing a location analysis includes several steps including data collection, data pre-processing, spatial analysis and integration with decision support systems. Collecting air quality data is one of the main parts of this research. The air quality of Tehran is affected by several factors, including the emission of pollutants by cars, industrial units and natural resources, the amount of each of which is measured using the network of air quality monitoring stations throughout the city of Tehran. This time-spatial data set enables us to investigate trends and changes in air quality in different areas of Tehran. Also, ensuring access to safe water is a fundamental aspect of public health. Therefore, the collection of water quality data is critical for our study. The desired parameters include water acidity or alkalinity (pH), clarity, chemical oxygen demand (COD), biological oxygen demand (BOD) and concentrations of certain pollutants such as heavy metals. Is. The data collected by local health centers have been used to analyze infectious diseases from animals. After collecting the required data, the process of data analysis and processing is done for their spatial analysis, and after the said analysis, the data obtained from different stages of the research are integrated in GIS. This allows us to combine spatial data to more clearly show the connections between environmental factors and diseases. This data integration should be done regularly and carefully so that the results of the analysis are valid. QGIS software was used to perform spatial data analysis and processing. In addition, Pandas and NumPy libraries in Python were used for statistical data analysis.

The data collected from the air quality monitoring stations allowed us to obtain detailed maps of the concentration of pollutants and their spatial changes. These maps are very valuable in monitoring health risks related to air pollution. Analysis of air quality data showed high concentrations of PM2.5 and PM10 particulate matter in densely populated areas. In addition, the concentration of NO2 near the main roads indicates the major contribution of vehicles in the production of this gas. The analysis of water quality in Tehran showed that there are no significant differences in the water quality of different regions. . The analysis of disease data provides important information about carriers, their habitats and behavioral patterns, which leads to a comprehensive understanding of the city's infectious disease ecosystem. Targeted measures to control infectious diseases in high-risk areas and education to the target community are necessary to reduce the risk of diseases.

Human health is affected by various environmental factors, including the place of their lives, so that it can be said that health-related issues almost always have spatial dimensions. Investigating the characteristics of these places (including anthropological characteristics and the presence of environmental risk factors) is very important in order to conduct studies. The results of this research showed that GIS has a valuable role in investigating and tracking the spread of diseases and other health issues in The length of time periods and the assessment of environmental risks for the residents of an area. Using GIS is one of the health warning solutions to people at risk. By specifying the location of the disease and the polluted areas of the city, people will become more aware of their surroundings and better understand prevention issues. Also, with the identification of high-risk areas, health costs and expenses will be adjusted because policy makers and health managers will focus on the necessary strategies to prevent and deal with the spread of these types of diseases in a targeted manner.

‎The rapid growth of cities is a global phenomenon that has become a challenge in our country. The lack of development of urban infrastructures at the same time as the expansion of cities, especially in the transportation sector, has created many problems such as heavy traffic and air pollution for big cities. The aim of this research is to provide a solution to deal with the challenges facing the city of Tehran in the field of development of urban roads by using analysis based on geographic information system (GIS) and decision support systems so that it can improve planning in construction. And the development of urban roads achieved a sustainable development in the transportation sector. For this reason, it is very important to know the current situation in planning for road development in Tehran, to identify areas for improvement and to provide solutions based on GIS. These solutions lead to optimization of route selection, environmental impact assessments, efficient project cost management and effective risk management.

To achieve the research objectives, a multifaceted method was adopted. At first, the collection of spatial data related to the urban road network including transportation networks, environmental parameters and existing road infrastructures was done. Then, GIS technology was used to perform spatial analysis, route optimization using Dijkstra's shortest path algorithm (SPA) and environmental impact assessments. Decision support systems were developed to facilitate data-based decision making in each scenario in road construction project analysis. Finally, the scenarios obtained from the spatial analysis were compared with the road construction operational projects.

The results of this research showed the significant capacity of spatial information analysis based on GIS and decision support systems in modifying planning for the development of road construction in urban Tehran. These technologies reduce urban traffic by 20% through optimal route selection and improve the efficiency of urban transportation. Environmental impact assessments also showed that the use of these methods can lead to a 36% reduction in harmful effects, including air pollution. Also, the integration of GIS-based cost management tools led to the reduction of road construction costs. Identifying risk reduction strategies through spatial analysis ensures the success of the project in terms of timing and cost and ultimately leads to the satisfaction of citizens.

Using GIS-based spatial analysis and SPA algorithm, routes with minimum travel time were identified that reduce traffic and increase transportation efficiency. Reduction in travel time can lead to increase in productivity and improve the quality of life of citizens. In addition, optimized routing can help reduce fuel consumption and air pollution. In addition, the Environmental Impact Assessment (EIA) index has shown a significant improvement in the environmental sustainability of scenarios that are based on spatial analysis. Thus, reducing the levels of air and noise pollution and maintaining green spaces can help to increase the quality of life of citizens. In addition, reducing harmful environmental effects can contribute to the long-term growth and development of the city. On the other hand, by identifying capacity risk in the planning stage, project managers can allocate resources more effectively and implement measures to prevent delays and cost overruns.

Geodesy is the basis of the science of Geomatics and Surveying Engineering. The Greek root of the word geodesy means dividing the earth, which shows that geodesy is historically closely related to the preparation of maps, analysis of the state of the earth, and geo-spatial data. Today, the science of geodesy discusses the set of rules related to the measurement and representation of the earth in a three-dimensional space that changes with time. This knowledge plays a key role in various scientific, engineering and navigational applications. In this paper, we will have an overview of the modern applications of geodesy in the field of navigation and earth monitoring and how these developments affect the global infrastructure of geo-spatial information and related scientific research.

In recent years, the Global Positioning Satellite System (GNSS) by increasing the accuracy and public access to the location caused a fundamental evolution in precise navigation, including the navigation of self-driving cars. Determining the earth's gravity field is another fundamental aspect of geodesy, which has made significant progress in this field along with the development of space programs. Advanced satellite missions such as GRACE-FO have provided an unprecedented ability to increase the accuracy of Earth's gravity field models. These models are used to understand Earth's dynamic processes, including sea level, mass balance of ice sheets, and Earth's internal dynamics. Moreover, using a standard framework to connect geodetic observations around the world is a necessary thing, for this purpose, Terrestrial Reference Frames (TRFs) are used. The development of the International Terrestrial Reference Frame (ITRF), the latest version of which is ITRF2023, is a sign of global joint efforts to increase the accuracy and reliability in the realization of reference frames for the unification of geodetic observations.

GNSS has provided the ability of positioning with very high spatial accuracy. The findings showed that GNSS can determine the position with centimeter accuracy. Also, navigation using GNSS technology has grown day by day and GNSS receivers play a vital role in aviation, shipping and transportation industries. This navigation system provides pilots with accurate information about the position, speed and direction of the aircraft, which helps to control the flight more accurately and respond faster in emergency situations. The maritime industry has also made extensive changes through the use of GNSS. Today, various ships need GNSS receivers to navigate and avoid potential risks of collision with other vessels. Also, GNSS technology plays an essential role in the transportation sector for managing urban and intercity traffic, optimizing transportation networks, reducing travel time, and improving the efficiency of the transportation system. In addition, the role of GNSS is very valuable in natural disaster management. Also, measurements of Earth's gravity field using satellite missions such as GRACE-FO have contributed to a better understanding of Earth's climate changes. These missions monitor the changes in mass distribution on the earth's surface and provide the possibility of monitoring phenomena such as the melting of polar glaciers and the displacement of underground water resources. This information is very useful for assessing the effects of climate change. Accurate satellite gravimetric data have the ability to monitor sea level elevation changes. By monitoring changes in ocean mass, scientists can make more accurate predictions about changes in sea level elevation. This information is necessary for the management of coastal settlements and policy making for coastal management. Ultimately, these measurements help scientists better understand Earth's internal structure, including plate tectonic movements. Quantifying plate tectonic motion is important for understanding the internal structure and behavior of plate tectonics, including the relationship of these processes to earthquakes and volcanic activity.

In today's world, cities have garnered significant attention as central hubs for social and economic activities. This research aims to enhance urban development and improve the quality of life for Tehran's residents, focusing on land use in Tehran, the capital of Iran and one of the most populous and pressured cities. The primary objective is to evaluate the distribution of various land uses across Tehran and analyze shortcomings that do not align with urban needs and standards. The study particularly delves into issues related to the appropriate number of land uses in different areas of Tehran and the role of recreational land use in urban development.

To conduct this comprehensive research, Tehran was divided into various zones, with diverse land uses thoroughly examined in each zone. These zoning divisions aimed to provide a more precise understanding of the unique needs of each part of the city for development and productivity. The distribution of land uses across the city was analyzed, and the frequency of each land use at the macro level was measured. Utilizing urban standards as evaluation criteria, a detailed analysis of land use distribution was performed. These standards served as benchmarks for assessment, highlighting areas where the standards fell short. This analytical approach facilitated a better understanding of strengths and weaknesses in urban development. Through this method, land use distribution in the city was evaluated more optimally, leading to recommendations for improvement and optimization.

The outcomes of this study underscore significant deficiencies in the allocation of land uses across Tehran, signaling a compelling call for refinement and heightened precision in regulation, particularly within the identified areas of 7, 10, 13, 14, 16, and 17. The identified weaknesses in land use distribution offer invaluable insights for urban decision-makers and planners, serving as a clarion call for immediate interventions in the city's development trajectory. The critical nature of this information lies in its potential to guide strategic decisions and policy implementations aimed at rectifying existing imbalances. Furthermore, the research accentuates a growing imperative to prioritize recreational land use within Tehran, shedding light on the inadequacy of current land use patterns to align with established urban standards and the evolving needs of society. The pressing need for increased attention to recreational spaces emerges as a key takeaway, suggesting that the development of such areas within the city could yield tangible enhancements in residents' overall quality of life. The envisaged benefits extend beyond mere leisure, with the potential to foster the creation of green spaces, promoting environmental sustainability, and providing platforms for diverse recreational activities that contribute to a healthier, more vibrant urban community. In essence, the research outcomes advocate for a strategic reevaluation and recalibration of land use policies in Tehran, with a heightened focus on rectifying existing imbalances and proactively addressing the evolving needs of the city's residents.

This research serves as a valuable tool for urban decision-makers and city planners in the development and enhancement of the quality of life for Tehran's residents. The obtained results indicate that optimizing land use and addressing urban needs can contribute to sustainable development and improved living conditions in Tehran.

In recent decades, geomatics science has made significant progress, and these advances are due to advanced measurement tools and innovative technologies in the field of geometric and spatial data acquisition. In this context, mobile laser scanners have been introduced as a basic and efficient tool that has the ability to perform accurate and fast measurements of various objects and environments, including urban spaces. These devices automatically record all the details of the urban space in the form of point cloud. To extract the geometric information of buildings from these details, it is necessary to use machine vision methods. In order to achieve accurate and reliable models of buildings, a sequence of post-processing operations is implemented when processing point cloud data. One of the most important stages of these processes is the segmentation of point cloud. These steps transform point cloud data into more conceptual and analyzable information. One of the important issues in processing point cloud data is the ability to extract planar surfaces of building facades (walls). These planar surfaces are of special importance as basic components in modeling and analyzing the condition of buildings. Accuracy in the information related to these planar surfaces allows for a more accurate and complete distinction between different components of buildings. This is important in several applications including urban planning, construction management, and energy consumption analysis of buildings.

In this article, MSAC and G-DBSCAN algorithms are used to extract planar surfaces from point cloud data. These two algorithms are executed sequentially. First, the most probable planar surfaces in the study area are extracted using MSAC, and then G-DBSCAN is used to separate the walls from these planar surfaces. In this article, the GeoSLAM ZEB-HORIZON mobile laser scanner device is used to collect data, and the area chosen for this purpose is the buildings of the Faculty of Engineering of Bo Ali Sina University in Hamedan. Because this place has features such as architectural diversity, the presence of non-planar and planar facades, different positions of the walls relative to each other with different dimensions, and challenges related to the diverse architecture of the space around the buildings.

Comprehensive evaluation of this research that includes three separate buildings. The results show an average precision of over 93%, which guarantees accurate data extraction. In addition, it has achieved an average recall of over 94%, which captures the majority of elements in the view. As a result, F1 score with an average value of 94% has been obtained. This research contributes to the progress in the field of accurate building data extraction and architectural modeling. Of course, when dealing with buildings and more complex environments, the algorithm faces challenges. Among the challenges that can be mentioned are various architectural features of buildings and external obstacles. For example, in buildings with large glass doors and windows, these algorithms may incorrectly extract interior walls. Also, the presence of dense vegetation around the facade can create obstacles that hinder the laser scanner's ability to fully capture the facade.

However, the results show that the algorithm in general was able to provide a significant performance in extracting the facade information of buildings, especially in challenging architectural scenarios. These developments are promising and create new possibilities in the field of spatial data analysis and building modeling. This innovative approach can be used in various applications and help to develop modern and data-based architectural models.

The oil sector has served as the predominant catalyst of our nation's economy since the oil industry was nationalized in Iran. Oil facilities are integral to the oil industry, and among the most crucial facilities throughout the nation are pipelines. The extensive network of pipelines, which facilitate the transportation of oil from production sites to the country's refineries, as well as the distribution of refined products to consumption centers, represent vital economic and industrial lifelines. The spatial challenge lies in the intersection points of these pipelines, which can be effectively addressed through the use of geographic information systems. The accurate and optimal routing of pipelines is of utmost significance and should be executed in a manner that aligns with technical and economic ideals, while minimizing adverse societal and environmental impacts.

In this research, an attempt has been made to ascertain the routing of the multi-directional pipeline by taking into account a plethora of criteria, data, and spatial and descriptive information. This information encompasses factors such as land slope, vegetation, water flow, faults, residential and population centers, power transmission lines, pipelines in the region, and roads, among others. Given the extensive range of indicators associated with the routing of energy transmission lines in existing research references and executive reports of projects, this article focuses on thoroughly examining and providing a specialized classification of these indicators. In the subsequent stage, Geographic Information System (GIS) is employed as a framework to integrate the diverse components of information. This integration allows for the utilization of varying influence weights during the compilation of indicators, based on the significance of each indicator's impact on the subject matter. For this purpose, the conventional fuzzy AHP method has been used.

The research conducted in this study focuses on the geographical area of Khuzestan province, specifically its northern region located between Rig Valley and Sabzab. The objective of this research is to establish the optimal route for the transmission line of oil, taking into account three different scenarios: economic optimality, environmental optimality, and a combination of both. The findings reveal that selecting the most favorable route in this context, in comparison to the current transmission line, leads to a reduction of 141 meters in terms of the economic scenario, 635 meters in terms of the environmental scenario, and 586 meters in terms of the comprehensive scenario. These outcomes represent tangible accomplishments resulting from the research.

The findings of this study clearly demonstrate that the conventional techniques for designing pipelines are insufficient in incorporating all the relevant criteria in the pipeline route design. Regardless, it is crucial to acknowledge that the quality of the routes produced with the aid of Geographic Information Systems (GIS) heavily relies on the quality of the input data. Any discrepancy or flaw in the input information may yield design outcomes that raise doubts about the effectiveness of the work. Consequently, in addition to harnessing the capabilities of GIS, meticulous attention to detail must be exercised during the data collection process. Another notable aspect of this research is its capacity to consider various scenarios. By leveraging this capability, decision-makers are empowered to make informed choices regarding the pipeline by examining the outcomes of diverse scenarios and taking into account a range of factors.

Close-range photogrammetry aims to produce accurate 3D geometric models of objects using images taken from the subject. Nowadays, the creation of realistic 3D models and their visualisation is a common practice that is becoming more popular every day. On the other hand, choosing the right modelling software for photogrammetry has always been a challenge and a topic of discussion among experts and researchers. Therefore, it is essential to examine and evaluate the models produced by different software tools. Due to the widespread use of Agisoft software among engineers and researchers in this field, this study aimed to perform image processing and modelling using two versions of this software, namely Photoscan and Metashape. In previous research, the criterion for optimising the image mesh has been based on improving the accuracy of the modelling. In order to assess and evaluate the 3D models produced by the two versions of the software, we defined different scenarios for the design of the image mesh. We compared the 3D models generated for each scenario with a mathematical reference model. We also examined the complete modelling in the software under different conditions using two different textures, as the texture of the image directly affects the quality of the point cloud. It is important to analyse the role of the image texture together with the geometry of the image mesh. Therefore, we evaluated the image texture as a radiometric index and investigated how these two factors affect the quality of the point cloud. As a result, we determined the optimal number of images with appropriate texture required to produce an accurate and high-quality 3D model.

close-range photogrammetry, we capture a series of images of an object using a specific image network. These images are then used with the Structure from Motion (SfM) method to generate point clouds and 3D models. The concept behind SfM is inspired by how our eyes perceive objects. This approach offers a quick, automated, and cost-effective way to obtain 3D data. It involves creating 3D coordinate models by processing a sequence of overlapping images of the object. Finally, the resulting 3D models are compared with a reference point cloud using the Cloud Compare point cloud processing software.

 The results of using images with simple texture show that in Photoscan software, increasing the number of images not only leads to noise in the point cloud, but also reduces the similarity of the generated model to the cube. According to the results, the best 3D model with a high similarity to the cube is associated with the fourth scenario (45 images) with an error of 0.01 millimetres. In the case of the Metashape software, the best model is associated with the third scenario (90 images) with an error of 0.05 millimetres. On the other hand, in cases where images with complex textures were used, the best point cloud is related to the fourth scenario (45 images) with an error of 0.02 millimetres in Photoscan software and to the third scenario (90 images) with an error of 0.04 millimetres in Metashape software. In general, the use of objects with complex textures leads to a better match and therefore to denser point clouds due to the presence of complex and non-uniform gradients in the images.

The results show that the optimal number of images and the presence of a complex image texture have a significant impact on the improvement of the quality of the 3D point cloud of the object. Despite the increased processing time, the quality of the 3D model does not increase significantly with a large number of images; it only leads to denser point clouds due to increased noise in the point cloud.

Earth has a complex ecosystem that is affected by natural processes and human activities, and understanding these processes is a real necessity. Environmental changes, from climate change to the reduction of natural resources, affect human life, economies, and the well-being of future generations. Therefore, the need for comprehensive Earth observation has never been more critical. To address this need, the Copernicus program was created by the European Space Agency in the early 2000s. The goal of this program was to create an operational system for earth monitoring with open access to high quality satellite data. The Sentinel satellites, as the core of the Copernicus program, is one of the symbols of humanity's progress in earth observation and environmental monitoring. This research examines the Sentinel satellites and shows the importance, basic principles and unique features of each of them. Sentinel satellites are unique operational platforms compared to the satellites of the previous generation.

In this paper, the main framework of the Copernicus program, such as open access to satellite data, global coverage, operational stability, diverse sensors, and the framework of global cooperation, which is of great help to researchers, policymakers, and other users of satellite data, has been evaluated. The Sentinel satellites includes several satellites that acquire images from the earth in different spectral ranges, with global coverage and different viewing times. These satellites have operational stability and new generations of satellites are developed and launched to replace older satellites. Each satellite is equipped with special sensors according to its mission goals and makes it possible to monitor the land, oceans and atmosphere with the best possible tools from space.

The Sentinel-1 satellite, which is equipped with a radar sensor, can image in the C frequency band and different spatial powers from 5 to 40 meters. Also, this satellite is able to take images in any weather conditions and at any hour of the day and night, which is valuable for many applications. The accuracy of Sentinel-1 data is very high in monitoring changes in the shape of the earth's surface, crisis management, polar ice observations and ocean monitoring. This vital role in detecting and monitoring land subsidence in urban areas makes it effective in urban planning and helping to prevent crises. On the other hand, the Sentinel-2 satellite with a multispectral sensor provides a powerful tool for Earth observation. By recording data in a wide spectral range in 13 spectral bands from visible to short-wave infrared, this satellite has been able to deepen our understanding of the features of the Earth's surface. The re-viewing time of this satellite has made it possible to monitor crops and evaluate the health of vegetation. Also, the spatial resolution of Sentinel-2 satellite images is an effective factor in urban planning, tree health monitoring, and natural disaster monitoring, such as fire and flood. In addition, the high bandwidth of this satellite helps to efficiently cover large areas and increases its efficiency in environmental monitoring.

The Copernicus program is known for several key principles that have underpinned their success. These principles include open access data, global coverage, operational interoperability, diverse sensors, and a global cooperation framework. The principle of open access has given the assurance to the general users, including researchers, policymakers and commercial companies, that a continuous flow of satellite images will support their activities. The Copernicus program with a system of satellites ensures global coverage with optimal revisit time. The operational continuity of the program has resulted in new generations of satellites being developed and launched to replace older generations, to ensure a continuous flow of data. Each Sentinel satellite is equipped with specific sensors designed for its mission objectives, enabling it to monitor the Earth, oceans, atmosphere, and more. According to the characteristics of the Copernicus program, Sentinel satellites have started a new era of Earth observation and have provided a powerful and versatile tool for monitoring and understanding the Earth's ecosystem.

Rice, recognized as a strategic product for food security, holds a significant position not only in national economies but also globally. The importance of rice in meeting the dietary needs of populations and its role in achieving food security have led to a serious and substantial emphasis on this staple crop. In this regard, accurate and up-to-date data collection on the status of rice fields, especially information related to the quantity and quality of products, is crucial. Remote sensing technologies have been proposed as an efficient and effective solution in this context, enabling cost-effective data collection over extensive areas. Among these technologies, drones, due to their superior spatial resolution and higher precision in various monitoring tasks compared to satellites, offer relative advantages. This research employs an advanced approach called deep learning to estimate the cultivation area of rice seedlings or seedbeds using RGB images captured by drones in the Wufeng region of Taichung Province, Taiwan. The method leverages the capabilities of deep neural networks as an effective tool for analyzing complex data, achieving high accuracy in distinguishing various types of rice seedling or seedbed cultivation areas.

In this study, an advanced deep learning technique called DenseNet is employed for modeling and predicting the rice seedling or seedbed cultivation area in RGB images taken by drones. This method, utilizing complex algorithms and a set of processing layers, can extract high-level abstract concepts from the data. One unique feature of DenseNet is its use of a layer-to-layer algorithm instead of traditional layer concatenation approaches, resulting in reduced weights and parameters, as well as increased network efficiency. The ability of deep learning to process data in real-time immediately after image acquisition demonstrates the dynamic potential of DenseNet in quickly and accurately processing information. This capability allows real-time analysis and prediction of the rice seedling or seedbed cultivation area, providing the necessary information for optimal farm management.

The results obtained from this research demonstrate a confirmation of an accuracy exceeding 99.8% on validation data. This exceptionally high percentage indicates the remarkable capability of the DenseNet deep learning method in accurately estimating the cultivation area of rice seedlings or seedbeds. This high accuracy not only showcases the excellent performance of the model in identifying and predicting the rice cultivation area but also instills confidence in users. The presented model has successfully achieved precise detection and assessment of the rice seedling or seedbed cultivation area. This practical application provides valuable tools for farmers and farm managers to gain more accurate and timely awareness of their farm's status, facilitating better decision-making in cultivation and productivity.

This study convincingly shows the viability of employing drones in conjunction with sophisticated deep learning techniques for accurately estimating the cultivation area of rice seedlings or seedbeds. This approach proves feasible, especially in geographical areas similar to Wufeng in Taichung Province, Taiwan. The integration of drones and deep learning represents a notable technological leap in monitoring capabilities, offering substantial assistance to pertinent authorities involved in agricultural management and ensuring food security.

The field of navigation, which is widely recognized as one of the most efficient and effective means of reaching a desired destination, holds immense significance in today's society. In order to cater to the needs of users by providing them with accurate routes to their intended destinations, navigation systems rely on the determination of the mobile location and the destination on the map. At present, the majority of location-based services heavily rely on navigation systems that utilize the Global Navigation Satellite System (GNSS) for determining the mobile location. However, it is crucial to acknowledge that this method is not applicable in indoor environments due to a multitude of limitations. Consequently, in order to overcome these limitations, a plethora of technologies have been developed for indoor positioning, such as Wi-Fi, Bluetooth, Inertial Navigation System (INS), ultrasonics, and acoustic waves. The range of applications involved in location-based services varies depending on the position quality (uncertainty), ranging from prevalent navigation that requires accuracy in the tens of meters to precise automatic object location that necessitates centimeter-level accuracy. The focus of this specific study is centered upon the utilization of image-based methods as a novel approach to address the problem of indoor mobile positioning.

The implementation of the image-based navigation method presents a series of challenges that must be comprehensively addressed in order to ensure its accuracy, efficiency, and cost-effectiveness. Extensive research has been conducted to tackle these challenges, and the aim of this study is to contribute to the existing knowledge base by delving into the accuracy requirements of the image-based navigation technique. To achieve this, a meticulous 3D model of the building was meticulously created, and the position of the image focal point was determined as the mobile location through the utilization of photography and image matching techniques. It is important to highlight the fact that this particular technique capitalizes on the widespread availability of current mobile phones, which are equipped with cameras, thereby rendering it an incredibly inexpensive, rapid, efficient, and accurate solution for indoor navigation.

The primary outcome of this study revolves around the assessment of positioning accuracy using the proposed image-based navigation method. The results obtained from this research possess significant implications for the design and development of an optimal image-based navigation system. The numerical analysis showcases an impressive average planimetric accuracy of 2.5 centimeters, thereby underscoring the efficacy of the proposed method in achieving precise indoor navigation.

In light of the findings derived from this study, it can be conclusively affirmed that the proposed image-based navigation method possesses a level of accuracy that fulfills all indoor navigation requirements. Furthermore, the widespread adoption of smartphones among the general population ensures that the utilization of this method is highly feasible. The outcomes of this study strongly bolster the applicability of the image-based navigation method for a myriad of indoor navigation applications, as well as certain close-range outdoor applications. Thus, it is evident that this research has paved the way for the implementation of a reliable and efficient navigation solution in both indoor and outdoor environments.

Every year, floods cause significant damages around the world. Timely and accurate prediction can significantly minimize the amount of human and financial losses after flood. In recent years, several machine learning models have been used to predict floods; So that their results indicate the better performance of these models compared to classical statistical models. However, these models do not take into account the spatial features that lead to the creation and strengthening of floods. Using convolutional long-short-term memory model (ConvLSTM), time series prediction is combined with spatial features and hydrological information. Therefore, a new model of combination of spatiotemporal prediction has been designed with the aim of extracting spatiotemporal features in order to solve the main challenge in flood prediction, which combine spatial data with time series hydrological data as much as possible.

In order to comprehensively analyze the spatiotemporal features of precipitation, we integrated the spatial features with time series analysis. For this purpose, the ConvLSTM model was used, whose inputs include longitude, latitude, altitude, precipitation, discharge and others gathered by ground stations. ConvLSTM is a time series processing model that extracts spatial features. To achieve spatiotemporal prediction, ConvLSTM was used as a basic block so that features can be extracted layer by designing a dense network, so that after mapping them, prediction can be performed. In the simulation stage, the batch size and the number of epochs were selected as 64 and 30, respectively. Also, a three-layer convolutional network with the number of kernels 1, 8 and 32 and the number of neurons 20, 40 and 80 in each layer was used.

By analyzing the results, it was found that the prediction accuracy gradually decreases with the increase of time. However, when the prediction time is 10 hours before the flood event, the prediction accuracy is lower than other times. The reason is that when the prediction time increases, the amount of accurate information will be less, which leads to less accuracy in learning the model and as a result, the prediction accuracy decreases. To solve this problem, the depth of the network should be increased, which increases the modeling time, which shows the importance of trade-off between the expected accuracy and processing time.

In conclusion, the ConvLSTM model is able to provide suitable prediction results, especially in short-term times, and this model is a suitable tool for time series prediction. Even though the ConvLSTM model achieved a remarkable performance for short-term prediction, there are still some limitations, including long-term flood prediction based on time series data. Moreover, the complexity and dependence of the ConvLSTM model on the number of training samples can be mentioned. Therefore, more accurate model requires the collection of more data in this model. Thus, in regions with limited number of samples, the accuracy of the prediction may be affected.

Land evaluation is a very important link in the chain that leads to sustainable management of land and soil resources. Exploitation of lands according to their capabilities, in addition to meeting the needs of the present and future generations, also maintains the ecological balance of the earth. Analyzing the suitability of land by using a variety of factors affecting the quantitative and qualitative production of products and examining the intricacies of their relationships with each other, as well as simultaneously with land use analysis, is one of the most useful applications of spatial information systems in agricultural land management. Many methods have been developed since the presentation of the FAO framework for land suitability assessment, and some of them are still widely used. In the present study, the qualitative assessment of land suitability using geographic information system (GIS) and multi-criteria evaluation (MCE) for three strategic crops of wheat, barley and rice was investigated in four study areas in northern Iran.

In order to implement the proposed model, data related to soil characteristics, climate data, topography data, geological map and land cover map were collected. Among the collected data, temperature plays an important role in determining the suitability of land for agricultural products, spatial patterns of rainfall are important for assessing water availability, and the slope of the land has a great impact on drainage, the amount of light received from the sun and, consequently, the required energy. It has plants to grow. Then, according to the climatic diversity in the north of Iran and the diverse set of crops that are grown in this region, appropriate criteria were selected. The selected criteria are: soil type, temperature, precipitation, slope and geological parameters. In order to assign weight to each of the criteria and intensify their effect in the land suitability assessment stage, a weight assignment process was carried out. This weight allocation was done by experts. Each criterion was evaluated based on its effect on the cultivation of different crops in this area and the weight of each layer was determined. Finally, a detailed examination of the results and analysis of suitability of land for agriculture in the studied areas was carried out.

The analysis of the results of the geospatial information system showed that the west of Gilan Province is an ideal place for rice cultivation, but this area has challenges for barley and wheat cultivation. Relatively good scores for all three crops, wheat, barley and rice in the east of Gilan Province showed that this area is prone to growing diverse crops. The center of Mazandaran Province did not get good points for the cultivation of wheat, barley and rice crops. Also, the center of Golestan Province was determined as a very suitable place for wheat and barley cultivation.

In conclusion, the areas with a relatively suitable score provide facilities for diversifying the cultivation methods, which increases flexibility and reduces the risks associated with market fluctuations and climate changes. Also, in areas with a low suitability score, the role of environmental protection and sustainable land management practices is important. From another dimension, the classification of land suitability allows the policy makers and managers of the agricultural sector to allocate resources correctly and optimally. For future research, it is suggested to analyze the time series of satellite data using deep learning models.