From map images to geographic names

Principles of stereoscopic vision have long been used in geographic data processing and visualization, e.g. Laussedat created what he called metrophotography in 1895 [2][3] and a stereoautograph for plotting from terrestrial photographs was produced in 1911 [2]. Today, a common and established use of stereoscopy in geographic domain is three-dimensional (3D) modeling of terrains and cities based on aerial, satellite and/or terrestrial imagery with photogrammetric techniques. In these systems, commonly, stereoscopic imagery is used as an input and a vector 3D representation of the terrain or city is obtained as an output. Stereoscopy has also been used for close-range photogrammetry, however within the scope of this paper the focus is on the cartographic use of stereoscopic visualizations. In recent years, a number of mainstream digital devices with stereoscopic capabilities are marketed (e.g. [4], [5], [6]), which indicates that we can expect to see more stereoscopic content in the near future [1]. In anticipation of such a development, it is also plausible to expect that stereoscopic geo-data will become more available for everyday use. It is, therefore, interesting to document which stereoscopic visualization features are supported by mainstream geographic information systems (GIS) software and whether these features can be improved. This work presents a survey on current stereoscopic visualization support in geographic information (GI) systems. The underlying objective is to outline future research needs by examining current stereoscopic 3D visualization techniques and tools in current GISs. This paper focuses on visualization modules of GIS software, and tries to answer the question “How commonly is stereoscopic visualization supported in GIS?" To achieve this, first whether such support exits or not in GIS software is documented, then supported types of stereoscopic operations and viewing methods are reported. Motivated by improving efficiency of stereoscopic visualizations in cartographic domain, within this survey, it is also examined whether these software offer level of detail (LOD) management support for stereoscopic datasets. Findings from the study are meant as a guide to researchers and practitioners to assess the current state of True 3D support in widely used GIS software.

HUD Community Planning Software (HUDCPS), also referred to as HUD 2020 (coproduced by U.S. Department of Housing and Urban Development (HUD, Washington, DC) and Caliper Corporation (Newton, MA), telephone 800-998-9999 (request product sales), website (http://www.caliper.com/gishud.htm or http://www.hud.gov/cpd/2020soft.html) is a full-featured desktop GIS software program based on Caliper's Maptitude version 3.0 GIS software with a customized user interface and automated thematic mapping using data from HUD. HUDCPS runs under Windows 3.11, Windows 95, and Windows NT 3.5 and 4.0. HUDCPS requires an 80486 or Pentium-based computer equipped with a minimum of 12MB of RAM and a CD-ROM drive. Price is $249 with choice of HUD data from one of four (eastern, southern, central, and western) regions of the United States or $299 for HUD data from all four U.S. Census regions. It includes U.S. national TIGER/Line data sets (census tracts and block groups, streets, highways, landmarks, water areas) and attribute data (housing and population data from the 1990 U.S. Census).

In this paper , a user-friendly Geographic Information Systems (GIS) program was developed to manipulate the property listing for sale geographically, to display the property listing in relation to other social and environmental features including topography, ways, types of unit ,ways, gardens and/or shopping centers, and to provide tools for the houses buyers to search and inspect properties and make choices without the need of getting into the houses or without their need to travel to place of houses. This study combines four luminous areas Geographic Information Systems (GIS), Electronic business System, Management Information System (MIS) and Internet and applies the combined technology in the business of real state to make system this system called (GEMI).The first section explains the methodology and prototypes development for Sherouk Beach Resort Planning and Management (SBRPM) with GIS and how the GIS software program was developed. Secondly various application scenarios were provided to test the program developed. It proves that the GIS program developed in this paper provides a good and convenient option in the search of properties based on different selection criteria as concluded in the final sections. Keywords - Geographic Information Systems (GIS), Electronic business, MIS, Real state, GEMI, SBRPM. I. Introduction The following section will address issues that are raised in each of these three stages in the program development. The GIS program was developed in three stages. The first stage is in system planning that is, to identify what functions will be required by the program and how they are to be incorporated into the software program. To do this, first is to become more familiar with the existing functions and tools in MapInfo 7(1) and then to identify what other functions are needed in this System. The second, stage is to design the interface of the software program and to write codes using PHP to integrate the functions required into the MapInfo software program (2). The software program can be applicable to other regions if similar datasets can be used for those regions. In the third stage, the software program developed in this paper is applied to assist in the search of buying units in Sherouk Beach Resort Region (SBRR). By using different search criteria for the search, the software program is tested which demonstrates that the program is capable of assisting in buying in the real estate industry. Through this testing process, limitations of the software program and possible improvement solutions are also explored.

To perform an accurate interpretation of remote sensing images, it is necessary to extract information using different image processing techniques. Nowadays, it became usual to use image processing plugins to add new capabilities/functionalities integrated in Geographical Information System (GIS) software. The aim of this work was to develop an open source application to automatically process and classify remote sensing images from a set of satellite input data. The application was integrated in a GIS software (QGIS), automating several image processing steps. The use of QGIS for this purpose is justified since it is easy and quick to develop new plugins, using Python language. This plugin is inspired in the Semi-Automatic Classification Plugin (SCP) developed by Luca Congedo. SCP allows the supervised classification of remote sensing images, the calculation of vegetation indices such as NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) and other image processing operations. When analysing SCP, it was realized that a set of operations, that are very useful in teaching classes of remote sensing and image processing tasks, were lacking, such as the visualization of histograms, the application of filters, different image corrections, unsupervised classification and several environmental indices computation. The new set of operations included in the PI2GIS plugin can be divided into three groups: pre-processing, processing, and classification procedures. The application was tested consider an image from Landsat 8 OLI from a North area of Portugal.

This dataset provides a high-resolution spatial classification of urban morphology on Reunion Island, France, using a 100 m x 100 m grid based on the Local Climate Zone (LCZ) framework. The data were generated through a combination of satellite imagery analysis using the World Urban Database and Access Portal Tools (WUDAPT) method and Geographic Information System (GIS) data integration. The classification was refined using building height and surface fraction data from the BD TOPO 2018 database, improving the accuracy of urban form representation.The dataset is provided in shapefile format, facilitating its use in GIS software by urban planners, researchers, and policymakers. Each grid point contains geospatial and morphological attributes, including LCZ classification, average building height (m), and built surface fraction (%). These parameters allow researchers to spatially correlate urban morphology with observed temperature data, supporting targeted urban heat island (UHI) studies. The most compact LCZ types (LCZ 1, 2, and 3) are mapped in detail, highlighting zones on the island most exposed to climatic risks. These high-risk areas should be identified and prioritized for climate mitigation strategies.Beyond UHI analysis, the dataset allows for the identification of land use patterns and urban growth trends by analyzing the spatial distribution of LCZ across the island. Areas of recent expansion, often characterized by transitions from natural zones (LCZ 11, 14) to LCZ 6 or 9 (low-rise and sparsely built, can be geolocated and compared with zoning regulations. This offers urban planners a tool to evaluate whether current development aligns with local urban containment policies and to identify zones at risk of unregulated sprawl.Because the LCZ classification system is internationally standardized, the dataset also supports comparative urban climate studies. It can be directly used to contrast Reunion’s urban structure with other small island developing states, such as Mauritius, in order to evaluate similarities and differences in urbanization dynamics under similar climatic constraints.

As the prevalence of online services and platforms continues to grow, so too does the threat posed by automated attacks seeking to circumvent security measures such as CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) mechanisms. This research paper delves into the various attack vectors and vulnerabilities inherent in CAPTCHA systems, aiming to provide a comprehensive understanding of the methodologies employed by attackers to bypass these safeguards. Through an investigation into machine learning-based algorithms, optical character recognition (OCR) techniques, and adversarial attacks, this study sheds light on the evolving landscape of CAPTCHA circumvention strategies. By analyzing the strengths and limitations of each attack vector, as well as the potential countermeasures available, this research contributes to the ongoing efforts to enhance the resilience of CAPTCHA systems against automated threats. Ultimately, the findings of this study serve to inform the development of more robust and adaptive CAPTCHA designs, thereby bolstering cyber security in the digital realm. This research paper focuses on investigating attack vectors and vulnerabilities in CAPTCHA mechanisms, including machine learning-based algorithms, OCR techniques, and adversarial attacks. It emphasizes the importance of understanding these methodologies to inform the development of more resilient CAPTCHA designs that can secure online platforms from various attacks.

Throughout the course of human history, significant pressures have been in place to develop the coastline. Whether these pressures are a result of commercial and industrial needs or recreational preference, humans have always tended to settle at or near the waterfront. While obvious benefits can be reaped from this anthropogenic pattern, it also results in significant portions of the world's population subject to flood hazards. With the potential for these hazards to increase due to climate change, coastal zone managers and planners require tools to mitigate the risk to this population. One such tool is Geographic Information Systems (GIS). With GIS technology being at the forefront of Federal Emergency Management Agency's (FEMA) Map Modernization efforts, it has proven to be an effective tool in coastal hazard mitigation. The GIS technology is extensively used to evaluate land use and population change in the coastal areas which ultimately drives the coastal hazard mapping. The identification of risk requires analyzing large GIS data sets including topographic data (e.g. TIN {triangular irregular network} and GRID), orthophotography, land use data, and census population data. The GIS technology can effectively analyze these large data sets using the calculation algorithms built in to the GIS software such ArcGIS and present information that will guide decision makers. The 3D rendering capability of GIS software can be used to visualize various scenarios which aid in effective hazard identification and mitigation. The 3D GIS mapping and animation tools can also be applied to simulate various disaster scenarios that can ultimately aid planners in prevention of human and property loss during coastal disasters. In FEMA Region I, CDM, a FEMA study contractor and its partner OCC have been involved in numerous coastal hazard studies across New England. Together CDM and OCC have scoped total of six counties and have been involved in coastal engineering and mapping activities in more than 10 counties across New England. This paper will present methodologies to analyze various data sources and identify coastal flooding risks.


 
 
 Flood susceptibility analysis (FSA) is a prerequisite for adopting flood mitigation and adaptation techniques. A number of technologies and models have emerged through time, and this study focuses on the Binary Logistic Regression Model (BLRM) to investigate flood vulnerability of Elapatha Divisional secretariat (DS), Ratnapura District, Sri Lanka where has high impact from inundation. Rainfall, land use and land cover (LULC), elevation, slope, slope aspect, distance to the river, topographic wetness index (TWI), and stream power index (SPI) were the factors used in the model construction. These components were investigated in terms of their contribution to flood susceptibility using all location data and field plotting of responsible parameters in the study area using Geographic Information System (GIS) software, and all extracted data points were 96489, of which 50% were used for BLRM development using SPSS statistical software and remaining 50% for model validation. The coefficient of rainfall parameters, log value of elevation in meters, the tan value of slope in degrees, radiant value of aspect in degrees, (distance from the river)0.1, the ratio between SPI and TWI (SPI/TWI) and LULC band values of built-up area, water bodies and vegetations were 0.023, -2.254, -1.018, -0.005, -0.164, -0.003, 2.707, -.067 and -0.004 respectively. The accuracy was validated using Mean standard error (MSE) and area under curve (AUC) analysis, with values of 0.031 and 0.724 respectively. The Built-up area, elevation, and slope had the most impact on the inundation Elapatha DS division, and model performance represents 72.4% accuracy. Therefore, mitigation of inundation problems can be achieved through proper landscaping in the area.
 Keywords: Binary logistic Regression, Flood susceptibility Analysis, GIS software, Spatial analysis
 
 

Eddy covariance measurements are increasingly utilized for assessing the exchange of matter and energy between ecosystems and the atmosphere across various time scales, ranging from hours to years. The flux footprint represents the area observable by flux tower sensors and illustrates how the surface influences the measured flux. Flux footprint models describe both the spatial extent and the specific location of the surface area contributing to the observed turbulent fluxes. In this study, we applied a simple two-dimensional parameterization for flux footprint prediction (FFP), developed by Kljun et al. to identify the location of peak footprint contribution every half hour over a six-year period. Monthly cluster analysis was performed on these data. Using an open-source geographic information system (GIS) software, the resulting clusters were overlaid on a base map of the site obtained from the Estonian Land Board, where different compartments have varying growth stages and species compositions. Our main objective was to integrate forest inventory data with ecosystem exchange and productivity data continuously recorded by the eddy covariance measurement tower at Järvselja, Estonia. This integration enabled spatially explicit visualization of half-hourly flux contributions using geographic information system software.

Scene texts refer to arbitrary text found in images captured by cameras in real-world settings. The tasks of text detection and recognition are critical components of computer vision, with applications spanning scene understanding, information retrieval, robotics, and autonomous driving. Despite significant advancements in deep learning methods, achieving accurate text detection and recognition in complex images remains a formidable challenge for robust real-world applications. Several factors contribute to these challenges. First, the diversity of text shapes, fonts, colors, and styles complicates detection efforts. Second, the myriad combinations of characters, often with unstable attributes, make complete detection difficult, especially when background interruptions obscure character strokes and shapes. Finally, effective coordination of multiple sub-tasks in end-to-end learning is essential for success. This research aimed to tackle these challenges by enhancing text discriminative representation. This study focused on two interconnected problems: Scene Text Recognition (STR), which involves recognizing text from scene images, and Scene Text Detection (STD), which entails simultaneously detecting and recognizing multiple texts within those images. This research focuses on implementing and evaluating the Efficient and Accurate Scene Text Detector (EAST) algorithm for text detection and recognition in natural scene images. The study aims to compare the performance of three prominent Optical Character Recognition (OCR) techniques—TesseractOCR, PaddleOCR, and EasyOCR. The EAST model was applied to a series of sample test images, and the results were visually represented with bounding boxes highlighting the detected text regions. The inference times for each image were recorded, highlighting the algorithm's efficiency, with average times of 0.446, 0.439, and 0.440 seconds for the respective test images. These results indicate that the EAST algorithm is accurate and operates in real-time, making it suitable for applications requiring immediate text recognition.

Geographic Information Systems (GIS) services in academic libraries tend to differ, based on availability of GIS data, software, hardware, and staff expertise. GIS services at the University of Kansas are closely aligned with support for government information, data, maps, and statistics. Thus, our responses to users' needs are often naturally collaborative, optimizing the expertise of multiple staff members and various types of resources. The GIS and Data Specialist assists campus researchers with spatial data and software, as well as facilitating access to GIS data. Lab space for research and coursework involving spatial data is a core component of GIS services. In addition, various levels and types of GIS workshops are offered each semester, and custom training sessions are also available. "Word of mouth" and hands-on workshops are some of the most effective methods of outreach.

Petrotrin, a state owned company has production operations which span the whole of south Trinidad. The company has numerous wells, the miles of gas and oil lines, and the passage of these lines through agricultural areas, towns and villages are a major environmental concern to the company. Many of these installations were inherited from predecessor companies. Geographical Information System (GIS) technology is only about twenty years, and its usefulness over the years has shown that it is a cost saving to the company. Groups monitoring GIS has concluded that it is a billion dollar industry if one were to add the value of the software, hardware and services. Other computer software packages are not as beneficial as GIS. In GIS the storage of data is independent of the mode of presentation4. GIS can provide the following information needed for management to make the right decisions: ∼identify all areas within 100 feet of the wells∼identify all watercourses within 100 feet of installations∼identify all buildings, agricultural lands that are within 100 feet of the gas and crude oil pipelines among others. Several departments within Petrotrin can look at the same data simultaneously because GIS allows that flexibility. Previously, in using paper maps only one person can look at the information since there were not several copies of these maps circulating. If these maps had to be updated it took a long time. Now with the GIS software and hardware packages more than one person can look at the same data and make faster decisions. The report presented herein conveys the benefits of using GIS in environmental decisions in Petrotrin's production operations.

Use of geographic information systems (GIS) in fisheries science has increased in prevalence since its introduction in the late 1980s, but use among and within fisheries management agencies has not been quantified. We surveyed 89 administrators of fisheries management agencies in the United States and Canada to determine the current status of GIS in fisheries management and received 54 responses (61% return rate). Survey respondents indicated that GIS was used to help manage fish populations, and 63% of respondents believed that GIS was either “very useful” or “extremely useful” for meeting agency objectives. However, most GIS work conducted by fisheries management agencies was executed by few individuals within the agency or by contracted service. Barriers preventing more widespread use by managers within agencies included lack of knowledge or training and limited time to use GIS in job duties. Our results suggest that GIS is an important tool for fisheries management. Further, GIS use within an agency might be increased by focusing on increased biologist participation in training exercises, integration with existing job duties, and recognizing diversity among GIS software. RESUMEN desde que se introdujo en la década de los ochenta, los Sistemas de Información Geográfica (GIS) se han utilizado cada vez más en las ciencias pesqueras, sin embargo aún no se ha cuantificado dicha utilización entre y hacia el interior de las agencias de manejo pesquero. Se realizó un sondeo a 89 encargados de agencias manejo pesquero en los Estados Unidos de Norteamérica y Canadá para determinar el estado actual de los GIS en el ámbito de manejo, recibiéndose 54 respuestas (tasa de retorno de 61%). Los encuestados indicaron que los GIS fueron desde “muy útiles” hasta “extremadamente útiles” para cumplir los objetivos de las agencias. No obstante, la mayor parte del trabajo de las agencias basado en los GIS, era realizado por algunos pocos individuos de las propias agencias o bien por servicios contratados. Las barreras que impiden un uso más generalizado de los GIS hacia el interior de las agencias de manejo incluyen la falta de conocimiento o entrenamiento y limitaciones de tiempo para usar este enfoque como parte del trabajo diario. Estos resultados sugieren que los GIS son una herramienta importante para el manejo de pesquerías. Más aún, el uso de los GIS dentro de las agencias, pudiera incrementarse enfocando o aumentando la participación de los biólogos en los ejercicios de entrenamiento, integración a las labores diarias de trabajo y reconociendo la diversidad de software de los GIS.

Data acquired from site surveys provide an essential resource to various exploration and production activities, such as field development planning, well positioning, and subsea equipment positioning. For many years, site survey data has been delivered as a Computer-aided Design (CAD) product. As operators move into a Geographic Information Systems (GIS) environment, shortfalls from CAD-based workflows have become obvious. This paper presents a method of managing site survey data based on geo-information management principles and practices. The methodology presented in this paper defines a GIS data model for seabed survey data interpretation that satisfies GIS query, presentation, and data exchange purposes. This paper also defines a standard for GIS seabed survey data deliverables from contractors to operators, including methods and processes to move the model to/from CAD-based applications. The implementation of the data model has proven to be a relative success and allows for a better integration of subsurface applications internally. The results have helped define an efficient contractor-operator data exchange process by eliminating data ambiguity and enabling seamless GIS data transfer. Results have also produced a more intelligent seabed survey GIS dataset with rules and behaviors capability offered by an ArcGIS Geodatabase embedded into the data model. Use of a standard GIS data model and a standard list of GIS data deliverables required from contractors to operators will allow project teams to fully embrace the power of GIS data integration and allow nearly seamless and lossless movement of CAD/GIS representations of the model. Introduction In order to reach maximum potential benefit of data provided from seabed site surveys throughout the E & P lifecylcle (Figure 1), there is a need to ensure that data delivered from survey contractors to operators is done so in a structured, efficient, and consistent manner.

The first step of a risk assessment analysis is the evaluation of flood-prone areas. Its importance is considered for both managing and planning emergency activities, such as hydraulic risk reduction management, and also town planning. Nowadays, using Geographic Information System (GIS) technology for risk assessment analysis is very common. However, it is not widely used for defining inundated areas. LiDAR data, such as digital elevation models (DEM), makes GIS numerical models attractive methods for obtaining a flooded area automatically. Using GIS tools is beneficial for effective processing and accuracy assessment in comparison to traditional methods which are based on topographic maps and field surveys. A first approach (Federici & Sguerso, Bollettino SIFET 4:25–42, 2007; Marzocchi, 2009) uses a GIS module in order to simulate perifluvial flood maps, having as prerequisites (i) the conformation of the river floodplain by a high-resolution DEM and (ii) a water surface profile along the river axis calculated for a given water discharge through a generic one-dimensional (1D) hydraulic model (HEC-RAS, Basement, MIKE 11, etc.). On the other hand, a second approach uses a GIS-embedded two-dimensional (2D) model in order to simulate flooded areas due to a dam break (Cannata & Marzocchi, Nat Hazards 61(3):1143–1159, 2012). This module solves the conservative form of the 2D shallow water equations (SWE) using a finite volume method (FVM). The intercell flux is computed by a one-side upwind conservative scheme extended to a 2D problem (Ying et al., J hydraul eng-ASCE 10:977–98, 2004). The newly developed GIS module gives as output maximum intensity maps which can be directly used in a risk assessment process. Both models have been implemented in geographic resources analysis support system (GRASS)-GIS software (Neteler et al., Environ Model Softw 31:124–130, 2012; GRASS, 2013) and two new commands (r.inund.fluv and r.damflood) have been created. Both are available on the official GRASS addons website and are distributed under the terms of the GNU General Public License (GPL). In this work we present a comparison between the two models mentioned above. We analyse the possibility of integrating these two approaches. We intend to use the 1D model, GIS embedded if possible, to calculate the water surface profile along the river axis and the 2D numerical one to analyse inundation beside the river levees.