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The massive disasters that arise by nature and humanity are significantly leads to several losses in lives and infrastructures. Disasters such as chemical explosions, flash floods and volcanoes. The high level of preparedness from the governments and administration authorities and ambulance services can significantly reduce the losses in lives. The aim of this paper is to measure the spatial readiness of ambulance facilities for natural disasters using GIS networks analysis. The measurement performed based on three standards, the area covered by the ambulance service, speed of service and the proportion to the population. ArcGIS spatial analysis and network analysis tools employed to develop the coverage maps of the three measured standards. According to the analysis, 94.4% from the study area appeared within the standard distance (20 km) from the ambulance stations, while 91% from the study area appeared within the time response standard (15 minutes) from the ambulance stations. The study area has a deficit of 256,714 people and needs 5 additional ambulances to achieve the demographic standard. The main recommendation of this study is to apply this methodology regularly in the study area to avoid any weakness before the disasters and to increase the level of preparedness.

In this article, a new method of vehicles detecting and tracking is presented: A thresholding followed by a mathematical morphology treatment are used. The tracking phase uses the information about a vehicle. An original labeling is proposed in this article. It helps to reduce some artefacts that occur at the detection level. The main contribution of this article lies in the possibility of merging information of low level (detection) and high level (tracking). In other words, it is shown that many artefacts resulting from image processing (low level) can be detected, and eliminated thanks to the information contained in the labeling (high level). The proposed method has been tested on many video sequences and examples are given illustrating the merits of our approach.

Around the world, vegetation cover functioning as shelter to wildlife, clean water, food security as well as treat large part of air pollution problem. Accurate predictive data early warn and provide knowledge for decision makers to reduce the effects of changes in vegetation cover. In this paper, an automated prediction system was developed to forecast vegetation cover. Prediction system based on moderate satellite data spatial resolution and global coverage data. The tools of system automate processing Moderate Resolution Imaging Spectroradiometer (MODIS) images and training neural networks (NN) model based on 60,000 observations to forecast future density of Normalized Difference Vegetation Index (NDVI). Zonguldak data, located in north of Turkey as dense vegetation cover area utilized as case study for system application. This system significantly facilitates predictive process for users than previous long and complex models.

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.

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.

In this paper, we present an anomaly detection and localization system for surveillance systems. A new feature descriptor is proposed. The spatio-temporal identifiers are obtained by using optical flow histogram and the structural similarity index from the videos that contain normal conditions. An artificial neural network, Selforganizing maps are used in modeling. The proposed system has been tested on the UCSD dataset.

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.

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.

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.

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.

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%.

This paper discusses the method of developing Campus Information System. The system can handle 3D spatial data within desktop and web environment. The method consists of texturing of building facades for 3D building models and modeling 3D Campus Information System. In this paper, some of these steps are carried out; modelling 3D buildings, toggling these models on the terrain and ortho-photo, integration with a geo-database, transferring to the CityServer3D environment by using CityGML format and designing the service, etc. In addition to this, a simple but novel method of texturing of building façades for 3D city modeling that is based on Dynamic Pulse Function (DPF) is used for synthetic and procedural texturing. DPF is very fast compared to other photo realistic texturing methods. Last but not least, it is aimed to present this project on web using web mapping services. This makes 3D analysis easy for decision makers.

Uc boyutlu (3B) kent modellerinin farkli alanlardaki kullanimi gun gectikce onem kazanmaktadir. Gelisen teknoloji ile birlikte gercege cok yakin, semantik veri ile butunlestirilmis ve etkilesimli 3B kent modelleri olusturulabilmektedir. Gorsel ve veritabani tasarimi yapilmis 3B kent modelleri konum analizi, pazarlama, acil durum, afet yonetimi, tesis planlamasi gibi bircok alanda kendine kullanim alani bulmaktadir. Bu makalede 3B kent modeli olusturulurken, amaca yonelik gorsellestirme yapilmasinin onemine deginilmistir. Calismanin amaci, Kampus/Kent Bilgi Sistemleri kapsaminda 3B gorsel modellemeye yonelik ihtiyaclarin ortaya konmasi ve bu modellerin internet ortaminda sunulmasidir. Kampus/Kent bilgi sistemleri karar verme, muhendislik uygulamalari, projelendirme gibi bircok alanda imkan saglamaktir. Calisma kapsaminda dijital fotograflar ile ozel doku kaplama teknikleri kullanilarak Karabuk Universitesi'nin sanal olarak uc boyutlu modeli olusturulmustur. Veri toplama ve isleme kismi 3B bilgi sistemleri olusturulurken en onemli asamalardandir. Bu calismada sanal kampus modelinin uretiminde kampus yerlesim plani, sayisal arazi modeli ve 3B bina modellerinden olusan farkli tipte veriler kullanilmistir.

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.

Gel electrophoresis (GE) is one of the most used method to separate DNA, RNA, protein molecules according to size, weight and quantity parameters in many areas such as genetics, molecular biology, biochemistry, microbiology. The main way to separate each molecule is to find borders of each molecule fragment. This paper presents a software application that show columns edges of DNA fragments in 3 steps. In the first step the application obtains lane histograms of agarose gel electrophoresis images by doing projection based on x-axis. In the second step, it utilizes k-means clustering algorithm to classify point values of lane histogram such as left side values, right side values and undesired values. In the third step, column edges of DNA fragments is shown by using mean algorithm and mathematical processes to separate DNA fragments from the background in a fully automated way. In addition to this, the application presents locations of DNA fragments and how many DNA fragments exist on images captured by a scientific camera.

Designing 3D navigation systems requires addressing solution methods for complex topologies, 3D modelling, visualization, topological network analysis and so on. 3D navigation within 3D-GIS environment is increasingly growing and spreading to various fields. One of those fields is evacuation through the shortest path with safety in case of disasters such as fire, massive terrorist attacks happening in complex and tall buildings of today’s world. Especially fire with no doubt is one of the most dangerous disaster threatening these buildings including thousands of occupants inside. This chapter presents entire solution methods for designing an intelligent individual evacuation model starting from data generation process. The model is based on Multilayer Perceptron (MLP) which is one of the most preferred artificial neural network architecture in classification and prediction problems. We focus on integration of this model with a 3D-GIS based simulation for demonstrating an individual evacuation process.

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. One of the most important research areas is evacuating the buildings with safety as more complex building infrastructures are increasing in today’s world. The end user side of such evacuation system needs to run in mobile environment with an accurate indoor positioning while the system assist people to the destination with support of visual landscapes and voice commands. For realizing such 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 solutions for indoor spaces within 3D-GIS. As an initial step and as for implementation a GUI provides 3D visualization of Corporation Complex in Putrajaya based on CityGML data, stores spatial data in a Geo-Database and then performs complex network analysis under some different kind of constraints. The GUI also provides a routing simulation on a calculated shortest path with voice commands and visualized instructions which are intended to be the infrastructure of a voice enabled mobile navigation system in our future work.

Managing natural resources in wide-scale areas can be highly time and resource consuming task which requires significant amount of data collection in the field and reduction of the data in the office to provide the necessary information. High performance LiDAR remote sensing technology has recently become an effective tool for use in applications of natural resources. In the field of forestry, the LiDAR measurements of the forested areas can provide high-quality data on three-dimensional characterizations of forest structures. Besides, LiDAR data can be used to provide very high quality and accurate Digital Elevation Model (DEM) for the forested areas. This study presents the progress and opportunities of using LiDAR remote sensing technology in various forestry applications. The results indicate that LiDAR based forest structure data and high-resolution DEMs can be used in wide-scale forestry activities such as stand characterizations, forest inventory and management, fire behaviour modeling, and forest operations.

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.