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In order to pursue rapid development of the new generation of wireless communication systems and elevate their security and efficiency, this paper proposes a novel scheme for automatic dual determination of modulation types and signal to noise ratios (SNR) for next generations of wireless communication systems, fifth-generation (5G) and beyond. The proposed scheme adopts unique signatures depicted in two-dimensional asynchronously sampled in-phase-quadrature amplitudes’ histograms (2D-ASIQHs)-based images and applies the support vector machines (SVMs) tool. Along with the estimation of the instantaneous SNR values over 0–35 dB range, the determination of nine modulation types that belong to different modulation categories i.e., phase-shift keying (Binary-PSK, Quadrature-PSK, and 8-PSK), amplitude-shift keying (2-ASK and 4-ASK) and quadrature-amplitude modulation (4-QAM, 16-QAM, 32-QAM, and 64-QAM) could be achieved by this scheme. The application of this scheme has been simulated using a channel model that is impaired by additive white Gaussian noise (AWGN) and Rayleigh fading, covering a broad range of SNRs of 0–35 dB. The performance of this dual-determination scheme shows high modulation recognition accuracy and low mean SNR estimation error. Therefore, it can be a better alternative for designers of next generation wireless communication systems.

The users can contribute to geographic information through platforms such as Wikimapia and OpenStreetMap. They can also generate data by themselves with their applications in cyber worlds like Google Earth. This study is primarily designed to be a guide regarding Volunteered Geographical Information (VGI) and to evaluate the geometric accuracy of data collected from volunteers on application. The main purpose of this study is to present basic information about Volunteered Geographical Information (VGI), why users are tending to use VGI, the accuracy of the data entered by the user, to examine the examples of use in various fields, to learn about geographic information systems and to compare this phenomenon and also by developing a VGI application to examine the similarity between the actual data and the data collected from volunteer users. A mobile and web-based application have been developed to collect traffic accident data from volunteer users. The geometric accuracy analysis was performed by comparing the data collected with this application with the data obtained from the General Directorate of Security.

Remote sensing satellite images plays a significant role in mapping land use/land cover LULC. Machine learning ML provide robust functions for satellite image classification. The objective of this paper is to extend the capability of GIS specialists in geospatial area with minimum knowledge in computer science to easily perform ML satellite image classification. A framework consisting 7 stages established. Tools of steps developed in two programing environments, which are ArcGIS for geospatial datasets structuring and Anaconda for ML training and classification. During the development, authors constrained to reduce the complexity of big data of satellite images and limited memory of computers to make tools available for implementation in PC. In addition, automation and improving the performance accuracy. TensorFlow-Keras library employed to perform the classification using neural networks. A case study using RASAT satellite image in Ankara-Turkey utilized to perform the analysis. The developed classifier gained 80% performance accuracy. The complete RASAT satellite image processed and smoothly classified based on blocks methods. The developed tools successfully tested and applied in geospatial area and can be effectively execute in PC by GIS specialist.

Development of landslides susceptibility (LS) predictors based on 3D data is an active area of research in the recent years. Predicting landslides susceptibility maps help to secure human lives and maintaining infrastructures from this risk. Several advanced frameworks proposed with high input data to improve the predictors. The aim of this paper is to develop low data requirement framework for LS predictors development. This framework is only using one input 3D ALOS PALSAR image. The framework has three stages. (A) data pre-processing, (B) deriving explanatory factors, and (C) neural networks training and testing. Exactly. 22 input spatial factors were extracted from ALOS PALSAR image. Extracted factors were utilized to develop the FFNN predictor. The structure of the predictor is 22 factors (input layer) × 150 neurons (hidden layer) × 1 (output layer). Furthermore, 5829 sample points utilized during the training stage, while 745810 points sent to the trained predictor to create LS map. Based on confusion matrix metric, performance accuracy (89.3% training and 82.3 testing), While (95.22% training and 84.7% testing) based on Receiver Operating Characteristic curve. Out of the study area in Karabuk, 3.53 km2 (3.03%) were located in very high susceptibility category. Lastly, the application of the proposed framework showed that it is capable develop low data requirement predictors with high accuracy. Framework provide guideline data for future development in taxing topographic circumstances and large scale of data coverage. In addition, the framework handled the inconsistency in data quality and data updating problem.

Global Navigation Satellite System is widely accepted and it has good positioning accuracy for outdoor applications. However, it does not provide sufficient positioning accuracy for inside of buildings. Signals are attenuated inside of buildings or dense high building areas because various materials between satellites and receiver create interference to the signal propagation. Thus, indoor positioning technologies have emerged to cover the problem. In this paper, indoor positioning technologies have been investigated and UWB based indoor positioning technology has been chosen between them. In addition to indoor positioning, accelerometer and Wi-Fi connection has been added to the system for mobility. The accelerometer detects emergency cases and alerts relatives of an intended person by using web service. Hereby, remote navigation system design is presented for first aid of the user. The system can detect emergencies, especially falls using the accelerometer and it can locate patients anywhere in the world. The designed system showed that it is possible to detect emergencies and transfer to the web with a 24.17 cm average positioning error. Also, it is an assuring system for future remote positioning applications.

Land use/land cover (LULC) maps represent a primary requirement for several geospatial applications around the world such as change detection, time series analysis, environment, and urban researches. Mapping LULC from remotely sensed data based on satellite image classification handle the rapid changes in extensive geographical areas. Several effective and efficient mechanisms suggested for supervised satellite image classification. The neural networks machine learning algorithm became a major method in supervised satellite image classification. The objective of this article is to employ neural networks as a machine learning algorithm for LULC mapping. The study applied in Ankara area, which is the capital city of Turkey. This work utilized a free Landsat 8 satellite image with the Operational Land Imager OLI sensor to implement the analysis. The image was obtained and processed in ArcGIS software. Then, the machine learning data set developed using Python scripting language. Every band out of 8 bands from Landsat 8 image considered as an explanatory variable, while the output variable defined based on visual interpretation. The training dataset built based on the signature file and random sample points. The training dataset divided into three sections, for training, for validation and the last section for testing. The training and testing processes were implemented using Google-Tensor Flow Keres library from Anaconda distribution. Feedforward neural network structure implemented with 500 neurons in the hidden layer. Confusion matrix used as accuracy assessment metrics to measure the performance of the developed model. The overall accuracy of the developed model was 92%. In terms of overall accuracy and robustness, the neural networks algorithm was effectively implemented and the LULC map produces. The model gained high accuracy that it is satisfied with the geospatial accuracy target. The consequence showed the competence of neural networks algorithm to generating LULC maps from Landsat 8 satellite images.

Building information modelling BIM is relying on plenty of geospatial information such as buildings footprints. Collecting and updating BIM information is a considerable challenge. Recently, buildings footprints automatically extracted from high-resolution satellite images utilizing machine learning algorithms. Constructing required training datasets for machine learning algorithms and testing data is computationally intensive. When the analysis performs in large geographic areas, researchers are struggling from out of memory problems. The requirement of developing improved, fit memory computation methods for accomplishing this computation is urgent. This paper targeting to handling massive data size issue in buildings footprints extraction from high-resolution satellite images. This article established a method to process the spatial raster data based on the chunks computing. Chunk-based decomposition decomposes raster array into several tiny cubes. Cubes supposed to be small enough to fit into available memory and prevent memory overflow. The algorithm of the method developed using Python programming language. Spatial data and developed tool were prepared and processed in ArcGIS software. Matlab software utilized for machine learning. Neural networks implemented for extracting the buildings’ footprints. To demonstrate the performance of our approach, high-resolution Orthoimage located in Tucson, Arizona state in American United States was utilized as a case study. Original image was taken by UltraCamEagle sensor and contained (11888 columns, 11866 rows, cell size 0.5 foot, 564,252,032 pixels in 4 bands). The case image contained (1409 columns, 1346 rows, and 7586056 pixels in 4 bands). The full image is impossible to be handled in the traditional central processing unit CPU. The image divided to 36 chunks using 1000 rows and 1000 columns. Full analysis spent 35 min using Intel Core i7 processor. The output performance accuracy of the neural network is 98.3% for testing dataset. Consequences demonstrate that the chunk computing can solve the memory overflow in personal computers during buildings footprints extraction process, especially in case of processing large files of high-resolution images. The developed method is suitable to be implemented in an affordable lightweight desktop environment. In addition, building footprints extracted effetely and memory overflow problem bypassed. Furthermore, the developed method proved the high quality extracted buildings footprints that can be integrated with BIM applications.

Non-wood forest products (NWFPs) provide important source of income for millions of households world-wide especially for those in rural areas. Especially nonwood plants are main source of income for rural people in many parts of the world. These plants are also consumed by local people for health, nutrition and many other needs. In order to ensure benefits of NWFPs, it is important to identify nonwood plants and determine their population distribution. However, plant identification by using manual method is a complex task to fulfill especially in varying conditions in the field. The leaves are often used to identify plant species based on their visible characteristics. Image recognition systems have been developed to identify plant species by using leaf image. Comparing manual plant identification systems, image recognition systems are easier and, in most cases, more accurate alternatives. In recent years, some mobile applications have been developed to assist people to identify plants by taking pictures of their leaves using smart phones. In this study, it was aimed to investigate the capabilities of the most common mobile image recognition systems in identification of plant species based on leaf images.

Interactive 3D architectural indoor design have been more popular after it benefited from Virtual Reality (VR) technologies. VR brings computer-generated 3D content to real life scale and enable users to observe immersive indoor environments so that users can directly modify it. This opportunity enables buyers to purchase a property off-the-plan cheaper through virtual models. Instead of showing property through 2D plan or renders, this visualized interior architecture of an on-sale unbuilt property is demonstrated beforehand so that the investors have an impression as if they were in the physical building. However, current applications either use highly resource consuming software, or are non-interactive, or requires specialist to create such environments. In this study, we have created a real-estate purposed low-cost high quality fully interactive VR application that provides a realistic interior architecture of the property by using free and lightweight software: Sweet Home 3D and Unity. A preliminary study showed that participants generally liked proposed real estate-purposed VR application, and it satisfied the expectation of the property buyers.

The natural ability of humans to receive messages from the surrounding environment can be obtained through the senses. The senses will respond to stimuli received in various conditions including emotional conditions. Psychologically, recognizing human emotions directly can be assessed from several criteria, such as facial expressions, sounds, or body movements. This research aims to analyze human emotions from the biomedical side through brainwave signals using EEG sensors. The EEG signal obtained will be extracted using Fast Fourier Transform and first-order statistical features. Monitoring of EEG Signals is obtained by grouping based on four emotional conditions (normal, focus, sadness and shock emotions). The results of this research are expected to help improve users in knowing their mental state accurately. The development of this kind of emotional analysis has the potential to create wide applications in the future environment. Research results have shown and compared frequency stimuli from normal emotions, sadness, focus and shock in a variety of situations.

Measurements of rainfall carried out are generally automatic, but how many parties have carried out research using automatic rain gauge instruments. The rainfall level data obtained can be used to detect flooding so that it reduces the occurrence of natural disasters earlier. The principle works are when rain falls the water is collected and the height will be detected. The Sharp GP2Y0A41SK0F sensor will be read on Arduino Uno and pass through the signal conditioning circuit and then forwarded to the Water Pump to pump water so that the water comes out of the tube and the data will be stored. ESP8266, the Wi-Fi module will send data to Android. Measurements are made when the water is full the value of max 53.2 means heavy rain. After the data is obtained, then look for the standard error of the measurement tool for 7 days. The design of the model builds a tool using a funnel with a diameter of 14 cm and a height of 26 cm. Based on the calculation of the design that has been made the measuring instrument developed is able to measure rainfall up to 26000 mm rain height.

Water, in any way it comes, is important for the life of all living things. Indonesia is an area of tropical equatorial with a variation of rain, which is quite high. The regularity of the distribution of rainfall is one of the aspects most important to the activity of the community. As the development of technology, the intensity of rainfall can be measured manually using Ombrometer Observatory tool. The manual tool for measuring the rain precipitation, Ombrometer Observatorium, is used to take data manually. Samples should be taken at 7.00 a.m. everyday using a measuring cup to know the height of the water contained. However, the type is prone to error at the high rainfall intensity, since the drainage of the samples is conducted every 24 hours. Therefore, much water is wasted. To solve the problem, a modification of a rainfall gauge was made, that is Ombrometer Observatory with ultrasonic sensor HC-SR04. The height of the water in the container is sent through a server of which the data is stored in the database every ten minutes to reduce the risk of evaporation. It also minimizes the error in measuring the rainfall intensity. The results have been compared to the ones by BMKG (Meteorology, Climatology, and Geophysics Agency). The correlation value of the measurement ratio reached 0.9739 or 97.39%.

The assessment of land use land cover change is extremely important for understanding the relationship between humans and nature. The enormous changes at a regional scale and advancements in technology have encouraged researchers to gather more information. The remote sensing technology and GIS tools cooperatively have made it easier to monitor the changes in land use land cover (LULC) from past to present. This technology has unraveled the changes at the regional and global level and has also contributed tremendous benefits to the scientific community. A variety of change detection algorithms have been used in the history of remote sensing to detect changes at earth's surface and newer techniques are still in process. The data from remote sensing satellites are the primary sources that provide an opportunity to acquire information about LULC change in recent decades, which extensively use different algorithms according to the research needs. The selection of appropriate change detection method is highly recommended in every remote sensing project. This review paper begins with the traditional pre and post-classification change detection techniques related to LULC information at the regional level. Therefore, this paper evaluated the mostly used change detection method among all others to find remarkable results. Thus the review concludes the post-classification change detection method using maximum likelihood classifier (MLC) supervised classification is applicable in all cases. The comparative analysis was also performed in a selected region having multiple land features during review in which MLC results best in comparison to others. MLC is the most commonly used technique from the past till present that has achieved high accuracy in all regions comparatively to other techniques.

The development of mobile technologies is important in the lives of humans. Mobile devices constitute a great part of the daily lives of people. It has come to such a point that when people first wake up, they check their smart phones for the first thing. Users may share their positions with the GNSS sensors in mobile devices or they can add information about their positions in mobile applications. Users contribute to Geographical Information System with this sharing. These users consist of native (citizens) living in that geographical position not of the CBS specialists. Creating, collecting, sharing and disseminating the geographical data provided by voluntary individuals constitute the Volunteered Geographic Information System. The data in the Volunteered Geographic Information System are received from amateur users. “How reliable will the data received from amateur users instead of specialists of the field be in scientific terms?” In this study, the reliability between the data received from the voluntary users through Volunteered Geographic Information System and real data is investigated. The real data consist of the traffic accident coordinates. The data that will be received from users will be received through the speed values in the relevant coordinates and the marking of the users for possible accident points on the map.

We propose SmartEscape, a real-time, dynamic, intelligent and user-specific evacuation system with a mobile interface for emergency cases such as fire. Unlike past work, we explore dynamically changing conditions and calculate a personal route for an evacuee by considering his/her individual features. SmartEscape, which is fast, low-cost, low resource-consuming and mobile supported, collects various environmental sensory data and takes evacuees&rsquo, individual features into account, uses an artificial neural network (ANN) to calculate personal usage risk of each link in the building, eliminates the risky ones, and calculates an optimum escape route under existing circumstances. Then, our system guides the evacuee to the exit through the calculated route with vocal and visual instructions on the smartphone. While the position of the evacuee is detected by RFID (Radio-Frequency Identification) technology, the changing environmental conditions are measured by the various sensors in the building. Our ANN (Artificial Neural Network) predicts dynamically changing risk states of all links according to changing environmental conditions. Results show that SmartEscape, with its 98.1% accuracy for predicting risk levels of links for each individual evacuee in a building, is capable of evacuating a great number of people simultaneously, through the shortest and the safest route.

In times of crisis, it is a priority to get help as soon as possible and any kind of help can be accepted. Especially, all kinds of support by volunteers, is a great help in crisis resolution. Crisis locations can be developed or less developed regions. It is sometimes difficult to reach help and support in less developed regions. To create Geographic data, which is an important requirement in crisis situations, is time-consuming and costly when attempted to be produced by conventional methods. When the data is produced by volunteers, it is faster and less costly because experts try to reach and support the region. Volunteered Geographic Information (VGI) is spatial data that has been contributed for free by volunteers. In our work, that is review, the examples of Volunteered Geographic Information applications in crisis management will be shown on the poster.

International Conference on Geomatic and Geospatial Technology (GGT) - Geospatial and Disaster Risk Managemen -- SEP 03-05, 2018 -- Kuala Lumpur, MALAYSIA Akay, Abdullah Emin/0000-0001-6558-9029 WOS:000466546500040 The importance of analysis high resolution satellite imagery plays an important research topic for geographical information analysis of cities. Geospatial data plays an important role in important issues such as governmental, industrial, research topics on traffic management, road monitoring, GNSS navigation, and map updating. In this study, road detection from satellite imagery methods are classified as classification-based, knowledge-based, mathematical morphology and dynamic programming. In the beginning, the road structures including feature and model are analyzed. Then, the advantages and disadvantages of road detection methods are evaluated and summarizez their accuracy and performance based on road detection principles. Therefore, in order to obtain remarkable results for road detection, it is better to use more than one method. In after days, performing a complex road extraction from a satellite image is still a necessary and important research topic. Int Soc Photogrammetry & Remote Sensing Karabuk University BAP We would like to thank to the Karabuk University BAP unit for financial assistance.

One of the most dangerous disaster threatening the high rise and complex buildings of today's world including thousands of occupants inside is fire with no doubt. When we consider high population and the complexity of such buildings it is clear to see that performing a rapid and safe evacuation seems hard and human being does not have good memories in case of such disasters like world trade center 9/11. Therefore, it is very important to design knowledge based realtime interactive evacuation methods instead of classical strategies which lack of flexibility. This paper presents a 3D-GIS implementation which simulates the behaviour of an intelligent indoor pedestrian navigation model proposed for a self -evacuation of a person in case of fire. The model is based on Multilayer Perceptron (MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems. A sample fire scenario following through predefined instructions has been performed on 3D model of the Corporation Complex in Putrajaya (Malaysia) and the intelligent evacuation process has been realized within a proposed 3D-GIS based simulation.

The need for 3D visualization and navigation within 3D-GIS environment is increasingly growing and spreading to various fields. When we consider current navigation systems, most of them are still in 2D environment that is insufficient to realize 3D objects and obtain satisfactory solutions for 3D environment. For realizing such a 3D navigation system we need to solve complex 3D network analysis. The objective of this paper is to investigate and implement 3D visualization and navigation techniques and develop 3D routing instruction engine for indoor spaces within 3D-GIS. As an initial step and as for implementation a Graphical User Interface provides 3D visualization based on CityGML data, stores spatial data in a Geo-Database and then performs complex network analysis. By using developed engine, the GUI also provides a routing simulation on a calculated shortest path with voice commands and visualized instructions.

This paper describes the usage of MUSCLE (Multidirectional Scanning for Line Extraction) Model for automatic generation of 3D networks in CityGML format (from raster floor plans). MUSCLE (Multidirectional Scanning for Line Extraction) Model is a conversion method which was developed to vectorize the straight lines through the raster images including floor plans, maps for GIS, architectural drawings, and machine plans. The model allows user to define specific criteria which are crucial for acquiring the vectorization process. Unlike traditional vectorization process, this model generates straight lines based on a line thinning algorithm, without performing line following-chain coding and vector reduction stages. In this method the nearly vertical lines were obtained by scanning the images horizontally, while the nearly horizontal lines were obtained by scanning the images vertically. In a case where two or more consecutive lines are nearly horizontal or nearly vertical, raster data become unmanageable and the process generates wrongly vectorized lines. In this situation, to obtain the precise lines, the image with the wrongly vectorized lines is diagonally scanned. By using MUSCLE model, the network models are topologically structured in CityGML format. After the generation process, it is possible to perform 3D network analysis based on these models. Then, by using the software that was designed based on the generated models, a geodatabase of the models could be established. This paper presents the correction application in MUSCLE and explains 3D network construction in detail.

This paper presents a new model, MUSCLE (Multidirectional Scanning for Line Extraction), for automatic vectorization of raster images with straight lines. The algorithm of the model implements the line thinning and the simple neighborhood methods to perform vectorization. The model allows users to define specified criteria which are crucial for acquiring the vectorization process. In this model, various raster images can be vectorized such as township plans, maps, architectural drawings, and machine plans. The algorithm of the model was developed by implementing an appropriate computer programming and tested on a basic application. Results, verified by using two well known vectorization programs (WinTopo and Scan2CAD), indicated that the model can successfully vectorize the specified raster data quickly and accurately.

3D navigation within a 3D-GIS environment is increasingly getting more popular and spreading to various fields. In the last decade, especially after the 9/11 disaster, evacuating the complex and tall buildings of today in case of emergency has been an important research area for scientists. Most of the current navigation systems are still in the 2D environment and that is insufficient to visualize 3D objects and to obtain satisfactory solutions for the 3D environment. Therefore, there is currently still a lack of implementation of 3D network analysis and navigation for indoor spaces in respect to evacuation. The objective of this paper is to investigate and implement 3D visualization and navigation techniques and solutions for indoor spaces within 3D-GIS. For realizing this, we have proposed a GIS implementation that is capable of carrying out 3D visualization of a building model stored in the CityGML format and perform analysis on a network model stored in Oracle Spatial. The proposed GUI also provides r...

The areas such as emergency services, transportation, security, visitor guiding, etc. are the subjects of 3D network analysis applications. Especially, the problem of evacuating the buildings through the shortest path with safety, has become more important than ever in a case of extraordinary circumstances (i.e. disastrous accidents, massive terrorist attacks) happening in complex and tall buildings of today’s world.