نشریه علوم و فنون نقشه برداری
سال دوازدهم شماره 4 (پیاپی 48، بهار 1402)

With the advancement of technology, flying robots are used in many applications such as mapping, inspection and safety, transportation of goods and military operations. In order to navigate these drones, there is an urge for real-time positioning. In outdoor environments, this is often done using satellite positioning systems. But in indoor environments, gnss are not operational. Therefore, in order to navigate flying robots inside built environments, other positioning methods should be implemented, such as the use of video cameras. It is also possible to use the data of the inertia sensor as a complement to image data. In interior area, due to the lack of texture and lighting problems, in addition to the effects of image points, the effects of image lines can also be used. Based on the above, researchers have proposed many algorithms to determine the position of flying robots. However, in order to compare these algorithms, it is sufficient to examine the general error of determining the position and time of implementation of the algorithm and to analyze the reasons for increasing or decreasing the geometric accuracy or the reason for the failure of algorithms in challenging environments. In this research, the positioning of EuRoC drones is performed using selected techniques, namely PL-SVO, ORB-SLAM 2 and vins fusion algorithms, and their results have been analyzed and compared. Also, the reasons for the success or failure of algorithms in robot positioning operations in different conditions are stated by referring to the robot physical information and radiometric properties of images. Among the mentioned algorithms, vins fusion with RMSE of less than one meter for the two tests and about 1.2 meters for the other test has the highest accuracy. Also, the ORB-SLAM 2 algorithm has an RMSE of less than one meter in the parts of the flight that have succeeded in estimating the position of the flying robot.

Floods are one of the most important natural hazards threatening human societies. Flood issues are diverse and complex in nature. The onslaught of floods destroys facilities and causes human and financial losses and disrupts transportation and communications. Estimating flood area in flooded areas allows us to obtain flood damage and determine the extent to which we can identify a plan to reduce the damage and high-risk areas and reduce the risk to some extent. In this regard, remote sensing and GIS techniques are very suitable methods for data collection, fast, accurate and cost-effective decision making. For this study, Sentinel 1 and 2A satellite images for January 2020 were used. Also, the object-oriented method of satellite images and the capability of the Google Earth engine system were used to model and extract the flood area. Based on the results of accuracy evaluation, kappa coefficient and overall accuracy of object-oriented classification algorithms showed the best result compared to other processes. Also, validation results showed that object-oriented classification algorithm has an overall accuracy of 0.94 and kappa coefficient of 0. 88 and the processes performed in the Google Earth engine system have an overall accuracy of 0.91 and a kappa coefficient of 0.87. These results indicate that object-oriented algorithms and the Google Earth engine system are useful tools for identifying flooded areas and can assist planners in managing natural hazards in the study area.

The combination of laser range finder and camera has proven to be very useful in the fields of robotics, mapping, and autonomous vehicles for producing maps and capturing information about the color and texture of objects. In order to fuse data from these two sensors, they need to be calibrated with high accuracy, meaning that their translation vector and rotation matrix with respect to each other must be determined. This paper proposes a method for calibrating a 2D lidar with a camera using the same 3D test fields commonly used in photogrammetric tasks such as camera calibration. In the proposed method, a point-on-plane and an additional constraint are used to obtain the calibration parameters, which is similar to using a chessboard pattern in previous research. To implement and evaluate the proposed method, a system consisting of a 2D laser range finder, a stereo camera, and a servo motor was designed and built. This method was compared with one of the most accurate new calibration methods which used a photogrammetric test field and ping-pong balls. The advantage of the proposed method over this method is the lack of the need for ping-pong balls and the selection of the corresponding points in the point cloud, which is a manual and time-consuming process. Additionally, the proposed method was compared with the method used by Jia fen et al., which uses a pyramid structure created in the corner of a room for calibration. Finally, the calibration parameters of laser range finder with respect to the camera were calculated using the proposed method, the calibration method using a cloud of 3D points, and the J. Fan et al. method. The RMSE of the check points for these three methods was 24.14, 40.12, and 94.15 millimeters, respectively.

The flood is the most important natural disaster that causes human and financial losses every year, then its management is very important. The most basic step in flood disaster management is the preparation of a flood susceptibility map, which integrating the geospatial information system and multi-criteria decision making is an efficient approach to handle it. In order to spatially model flood susceptibility, the present study has presented an approach of fusion the weights of the effective criteria on flood susceptibility using the Dempster-Shafer information fusion theory (DST). The purpose of the fusion of the weights of the criteria is to increase reliability, reduce the uncertainty of the weighting process, and increase the accuracy of flood susceptibility modeling. The present research has presented a hybrid weighting method by integrating the results of two weighting methods (Analytical Hierarchy Process (AHP) and Best-Worst Method (BWM)). The hybrid weighting methods of previous researches are mainly based on simple mathematical operators, and complex operators such as DST used in the present research are less used. The results of the fusion of the weights obtained from the two weighting methods of AHP and BWM indicate the very high weight of the flow accumulation criterion (0.437) and the very low weight of the vegetation index criterion (0.004). Comparing the results of the research with the facts of the studied area showed that the presented hybrid weighting approach with 96% accuracy Compared to each of the basic weighting methods used, is of higher performance. Also, the susceptibility modeling resulting from the new BWM weighting method has been more accurate compared to the common method of the AHP. According to the results of the research, more than 92% of the studied area has moderate to high flood susceptibility and less than 8% of the area has less than moderate susceptibility, which indicates that the studied area is prone to flooding.

Management of exposure and dealing with the consequences of the concentration of PM2.5 in urban environments requires accurate modeling of spatial-temporal changes of pollutant. Accurate modeling of spatial-temporal changes requires appropriate modeling methods and complete and accurate data. These data are measured by different sensors and with different accuracy, have different variability and due to unavoidable factors such as sensor damage. Missing data cause many problems such as loss of sample size and errors in data analysis; therefore, it is necessary to use solutions to estimate the missing data in modeling the concentration of PM2.5.  In this study, a method based on extra tree and decision tree models has been proposed to imputation the missing values of PM2.5 along with considering the relationships between variables while maintaining their variability and natural uncertainty. Meteorological variables and other main pollutants such as O3, Pm10, Co, So2, No2 were considered as effective variables in imputation the missing values of PM2.5. Meteorological variables including total precipitation, relative humidity, and temperature were extracted from the model of the European Center for medium-term weather forecasting. Using the ECMWF model, in addition to increasing the number of meteorological stations, provides the possibility of using hourly resolution with a very small number of missing data, as opposed to a limited number of three-hour resolutions with a large number of missing meteorological data. The results showed that the extra tree method has a higher accuracy than the decision tree method with an average of R2=0.813 due to the reduction of bias with an average of R2=0.653 in imputation of missing PM2.5 values. After managing the missing data using the extra tree method, the XGBoost method was used due to the non-linear evaluation of the importance of the effective variables with the aim of increasing the accuracy and reducing the computational cost for modeling the spatial-temporal changes of the PM2.5 pollutant in different geographical contexts.

Spatial equity of urban health (USEH) is closely related to quality-of-life standards and urban development. Its evaluation is very important in the performance of public health and urban planning.  The aim of this research is to develop an approach to prepare and evaluate the USEH map using two data sets including reference location information and Volunteered Geographic Information (VGI).  To determine the accuracy of the maps obtained from both types of data sets. This research was carried out in district 6 of Tehran metropolis. In this research, the effective criteria of the amount of USEH were first determined. The criteria map was prepared with spatial analysis and multi-criteria decision-making, and the final USEH map was prepared by combining the criteria map. To validate the prepared map, the USEH map was again produced based on the VGI information provided by the citizens and compared with the map obtained from the reference data. The amount of USEH for citizens is classified into 5 levels, from very suitable to very unsuitable. Based on the USEH map obtained from the reference data, approximately 62% of the study area has good or very good spatial equity. Comparison of the VGI map with the reference map showed a 72% match. Determining and evaluating USEH for different regions using new methods and technologies is a fundamental step to help health decision-makers in order to manage and allocate resources. It is expected that the results of the present research will be considered an essential criterion for decision-making in the field of health and increasing spatial equity in citizens' enjoyment of health facilities and components.

With the growth of urbanization, urban transportation has become one of the most critical challenges of urban management, which is closely related to the economic power of cities and countries. A robust economy requires adequate infrastructure in the freight division, and proper resource planning and management is the key to its success. In this research, the issue of transportation of postal items has been considered. The use of traditional methods prolongs receiving and delivering postal items and thus increases its costs. In this research, this issue has been studied. Using meta-heuristic algorithms (Artificial Intelligence), an attempt has been made to optimize the problem of receiving and delivering postal items.

The proposed method of this research is based on the use of a Genetic Algorithm to optimize the order of pickup and delivery of postal items using the travel cost matrix between the points of pickup and delivery. The genetic algorithm has high flexibility following the structure of different problems. In the developed model of this research, the order of picking and delivering shipments in each freight vehicle is in one array and five arrays representing five freight vehicles from five postal centers in one matrix created. The genetic algorithm tries to optimize the final solution by randomly generating these matrices (chromosomes) and measuring the fitness function of each matrix (answer) and using the combination and mutation operators. Finally, the best solution is obtained, which is the best arrangement and planning for the trucks carrying the items, in which the best order of receiving and delivering the postal items is determined.

The study area is 10, 11, 12, 14, 15, 16, 17, and 19 regions of Tehran (the capital of Iran), which were selected for implementation. Street network data was entered into the Network Analyst tool in ArcGIS software. Travel cost matrices between pickup and delivery points and consignment centers were extracted from the data of 50 pickup points and 50 delivery points entered into the developed model. After executing the algorithm for 1000 times and generating final output, which is the most optimal arrangement of pickup and delivery points, it was compared with the first random answer made in the model which represents old unplanned method for receiving and delivering the postal items. The total length of final optimal answer is 551689 meters, which is less than 720287 meters (the total length of first random answer). The decrease in the final solution in comparison to the first random solution is 168598 meters, which is equivalent to 24% savings and indicates the efficiency of the developed model.

Using old traditional experimental methods for pick-up and delivery of postal items leads to increase the route of postal vehicles which increase the urban congestion and produces some pollutions. Applying the scientific methods such as used model in this research helps to decrease the aforementioned problems and it is a key to approach the smart cities. We used a genetic algorithm optimization method for arranging the order of receiving and delivering the postal items and develop a method to decrease the distance between request points. By using this algorithm, the total length of postal vehicles decreased from 720 km to 551 km which is equivalent to 24% savings.For instance, the second truck's way can be checked to investigate the proposed model's performance. Since can be observed, the algorithm has put the pick-up and delivery points together properly to stop the truck from driving around the study area. It can similarly be recognized that the truck's movement numbers are adjacent to each other. It means that the delivery points are ordered to follow each other, and the postal vehicle evades moving significant ways. Consequently, the vehicle's driving length is decreased, which decreases the overall driving length of all vehicles. Nevertheless, the first vehicle's route does not look so visually optimal. It can be seen that the vehicle has been required to move to some distant points. First, the ultimate solution's fitness function's state holds the lowest possible value among the solutions. Furthermore, the algorithm could not optimize the paths more, and it has to insert some distant locations in the route of one of the vehicles. Indeed, every attempt has been performed to gain the most suitable paths. However, we can optimize this problem by improving our methods or use other metaheuristics algorithms for future research.