Geospatial Applications Research Articles

Identification of Ecological Hotspots Using the Eco-track: Case of Keoladeo National Park, Bharatpur, India.

Ecological conservation and sustainable land management are vital endeavors in the face of rising anthropogenic pressures and habitat deterioration. Accurate and effective evaluation techniques are essential for identifying regions that are of ecological relevance and concern. The present research introduces an innovative approach using geospatial tools to detect natural hotspots and deficits within a landscape. The research incorporates six essential ecological parameters, namely spatial variability, vegetation health, road network connectivity, fragmentation, biological richness, and habitat areas, obtained from existing literature studies. These parameters indicate the overall health of ecosystems and the extent of biodiversity present, which are crucial for developing effective strategies for ecological planning. The research project aims to use geospatial applications to identify the "ecological rich", "ecological moderate" or "ecological deficit" areas in the study area and to establish a model framework for automating the geospatial analysis. The resulting map offers a comprehensive and practical depiction of the ecological condition of the landscape, facilitating decision-makers in strategically allocating resources for conservation and restoration initiatives. The importance of this research resides in its capacity to streamline and automate what was previously a time-consuming and labor-intensive procedure. This innovative approach empowers conservationists, land managers, and policymakers with a powerful tool "Eco-track' to identify and prioritize ecological hotspots and deficits, ultimately fostering more effective and targeted efforts in preserving the natural heritage.

Towards maximizing geospatial data usage in north eastern India using open-source scalable user-centric applications

Abstract. The north eastern region of India is characterized by limited physical connectivity and challenging terrain, that requires innovative approaches for effective developmental planning and monitoring using geospatial data and user-centric applications. This paper focuses on leveraging space-based data and applications to address these challenges and enhance decision-making processes in governance. A unified framework for interoperable spatial data sharing has been developed, ensuring that data is easily discoverable, searchable, and consumable through metadata standards and OGC interoperable data services. A dedicated spatial data repository has been established, offering real-time access to over 1400 geospatial datasets and facilitating better decision-making and geo-analytics among diverse user groups. The repository operates on an open-source platform with integrated map viewers and geo-analytics tools, allowing for interactive exploration and visualization of geospatial data. Additionally, 35 user-centric geospatial applications have been operationalized, all developed using open-source geospatial tools and standards. Capacity building programs have been conducted to increase awareness and maximize the utilization of geospatial data services and applications across the region. The platform has boosted utilization of geospatial data services and applications in the region for overall planning and developmental activities for smart governance and societal benefits.

Geometry, Topology and p-Adic Numbers in Geospatial Data Modelling, Management and Processing: Review and Future Approaches

Abstract. Geometry and topology are both central concepts in geospatial data modelling and management. While geometry is the central concept for computational geometry applications, e.g. to intersect surfaces and solids in 3D space, topology is helpful for many application classes starting from city modelling to subsurface modelling and indoor navigation. Furthermore, p-adic numbers are useful for describing hierarchical processes on topological models. In this paper we first shortly review geometry- and topology-based approaches used for geospatial applications. We then describe our approach on turning geometry “upside down” focusing on topology during the whole process of distributed geospatial computing, data modelling, data management, and simulation. Furthermore, the way to use p-adic numbers for the description of hierarchical processes on topological models is shown for the example of simulation and the idea of p-adic analysis in distributed simulations is presented in the context of topological city and building models. The approach opens new insights such as topological relationships, components, and efficiently studying of the approximate behaviour of processes in the built environment using distributed computational systems. Finally, exemplary applications are presented to underline the importance of this new approach.

One Country, Different Reactions- How did the Gay Community in Israel Respond to the New Mpox Threat?

Mpox affected mainly men who have sex with men (MSM). This study aimed to assess MSM's response to the threat, and compare MSM living in central Israel vs. its periphery. Data were collected by anonymous electronic surveys between September and October 2022 through a geospatial application ("Grindr"). Of the 665 MSM participants, 221 (33.2%) were vaccinated against mpox. In the multivariate analysis, living in central Israel, being in steady relationships, HIV infection, PrEP use, noticing suspicious skin lesions, and changing sexual behavior predicted vaccination. Of all participants, 317 (47.6%) changed their sexual behavior. In the multivariate analysis, living in central Israel, engaging in risky sexual behavior and being vaccinated against mpox predicted sexual behavior change. Of the 444 participants who were not vaccinated, 245 (55.1%) lived in peripheral regions of Israel. Those who lived in the periphery were less likely to get vaccinated or change their sexual behavior compared with MSM who lived in central Israel. Although the study was limited in size and the study population was relatively homogeneous, MSM perceived mpox as a potential threat on health, and almost 50% changed their sexual behavior and nearly one-third were vaccinated against mpox. The trend was mainly observed in MSM who were at high risk to acquire mpox and those who lived in central Israel.

Utilizing serverless framework for dynamic visualization and operations in geospatial applications

ABSTRACT While substantial efforts have been invested in the development of Discrete Global Grid Systems (DGGS) spatial operations and their potential applications in the geospatial domain, it has become evident that there is a demand for an efficient and scalable system to handle the visualization of large-scale DGGS data. This study demonstrated the potential of DGGS in conjunction with the serverless framework for dynamic visualization at various resolutions, which is based on data storage and effective querying using PostgreSQL integrated into Amazon Aurora Serverless. The use of Amazon Web Services (AWS) Lambda for on-the-fly generation of hexagon geometries significantly reduced the storage requirements and improved the speed of the visualization process. In addition, we implemented on-the-fly spatial operations including point binning, thresholding, aggregation, and neighborhood operations in the DGGS, highlighting the capabilities of DGGS in vector and raster processing. The proposed system has shown promising results in terms of efficiency, scalability, and adaptability, making it a viable solution for large-scale geospatial data processing and visualization. Case studies using flood risk data and terrain data further illustrate the system’s practical applicability in on-the-fly spatial operations and rapid visualization.

Visualization of explainable artificial intelligence for GeoAI

Shapley additive explanations are a widely used technique for explaining machine learning models. They can be applied to basically any type of model and provide both global and local explanations. While there are different plots available to visualize Shapley values, there is a lack of suitable visualization for geospatial use cases, resulting in the loss of the geospatial context in traditional plots. This study presents a concept for visualizing Shapley values in geospatial use cases and demonstrate its feasibility through an exemplary use case—predicting bike activity in a rental bike system. The visualizations show that visualizing Shapley values on geographic maps can provide valuable insights that are not visible in traditional plots for Shapley additive explanations. Geovisualizations are recommended for explaining machine learning models in geospatial applications or for extracting knowledge about real-world applications. Suitable visualizations for the considered use case are a proportional symbol map and a mapping of computed Voronoi values to the street network.

Capturing and interpreting wildfire spread dynamics: attention-based spatiotemporal models using ConvLSTM networks

Predicting the trajectory of geographical events, such as wildfire spread, presents a formidable task due to the dynamic associations among influential biophysical factors. Geo-events like wildfires frequently display short and long-range spatial and temporal correlations. Short-range effects are the direct contact and near-contact spread of the fire front. Long-range effects are represented by processes such as spotting, where firebrands carried by the wind ignite fires distant from the flaming front, altering the shape and speed of an advancing fire front. This study addresses these modeling challenges by clearly defining and analyzing the scale-dependent spatiotemporal dynamics that influence wildfire spread, focusing on the interplay between biophysical factors and fire behavior. We propose two unique attention-based spatiotemporal models using Convolutional Long Short-Term Memory (ConvLSTM) networks. These models are designed to learn and capture a range of local to global and short and long-range spatiotemporal correlations. The proposed models were tested on two datasets: a high-resolution wildfire spread dataset produced with a semi-empirical percolation model and a satellite observed wildfire spread data in California 2012–2021. Results indicate that attention-based models accurately predict fire front movements that are consistent with known wildfire spread-biophysical dynamics. Our research suggests there is considerable potential for attention mechanisms to capture the spatiotemporal behavior of wildfire spread, with model transferability, that can guide rapid deployment of wildfire management operations. We also highlight directions for future studies that focus on how the self-attention mechanism could enhance model performance for a range of geospatial applications.

Persepsi Peserta Didik Terhadap Aplikasi Teknologi Geospasial Pada Pembelajaran Geografi Kelas X SMAN 1 Sungai Tarab

The purpose of this study was to determine the perception of class X students at SMAN 1 Sungai Tarab towards Geospatial Technology Applications. Geospatial technology is a technology that measures, analyzes, and visualizes objects or events on earth. This research uses a qualitative descriptive approach. The selection of subjects in this study used a total random sampling method consisting of 255 class X students. The research data were collected using a semi-open questionnaire method with a google form consisting of 25 questions given the answer options yes, no, and others. The results showed that there were 68.41% of students answered yes with the Good category, on the indicator of using geospatial technology applications as learning media. There were 64.61% of students answered yes with the Good category, on the indicator of the use of geospatial technology as learning material. And there are 70.44% of students answered yes with the Good category, on the indicator of the use of geospatial applications in everyday life. From the perception of students, there are still many students who do not know and use geospatial technology applications. Based on observations made at SMAN 1 Sungai Tarab, it appears that geography teachers have not utilized this geospatial technology application-based learning media.

Geospatial Applications in Alzheimer’s Disease Research and Beyond: A Systematic Review

With the increasing prevalence and social impacts of Alzheimer’s disease (AD), innovative approaches to our understanding of its etiology, progression, and potential interventions are critical. Geospatial applications are a promising tool in addressing the complex spatial and temporal dynamics of AD. A substantial knowledge gap remains, however, regarding the comprehensive utilization and impact of geospatial applications in AD research. We comprehensively consider the current status of geospatial-information application in AD based on 159 research articles on geospatial applications published between 1991 and 2022 from the Web of Science and PubMed databases. Four main research themes are identified: geographic environmental factors, geographic disparities, activity behaviors, and care services. Geospatial information has been extensively applied using techniques such as spatial clustering and hotspot analysis. We investigate the influence of location and spatial factors on AD risk, the activity behavior of patients with AD using location data and spatial analysis, and medical-resource proximity via geospatial modeling and predictive analysis. Additionally, we identify three knowledge gaps: geographic environment measurement, spatiotemporal contrastive analysis of disease prevalence, and multidisciplinary integration. The review underscores the value of geospatial information in AD-related research and public health, offering researchers insights into prospective directions and collaborative opportunities with geographers.

RESEARCH ON THE USE OF ARTIFICIAL INTELLIGENCE (GEOAI) FOR TASKS OF TOPOGRAPHIC MAPPING

This paper analyzes the main areas of artificial intelligence application for topographic mapping tasks using the example of six projects that implemented AI functions. It also forms the main goals, problems, challenges, approaches, and methods for automating the processes of creating and updating geospatial data and improving the quality of the data itself. The study's purpose is to investigate the use of tools based on artificial intelligence technology for topographic mapping tasks. The integration of AI and GeoAI into topographic mapping and urban monitoring has led to significant advances in the accuracy, efficiency, and scope of geospatial analysis. Projects such as Swisstopo's LCLU monitoring and NLS's ATMU project demonstrate the transformative potential of these technologies. However, addressing challenges related to data quality, model interpretation, and ethical issues is critical to the continued successful adoption and use of AI-driven solutions in geospatial applications. Integrating diverse and emerging data streams presents both opportunities and challenges. For example, a study of urban monitoring using street imagery and wearable sensors demonstrated the potential of GeoAI to analyze urban soundscapes and human comfort. These studies underscore the crucial role of using a variety of data sources. They show that it's not just about the data but about the comprehensive insights they can provide into urban environments.

Zero-Shot Building Age Classification from Facade Image Using GPT-4

Abstract. A building’s age of construction is crucial for supporting many geospatial applications. Much current research focuses on estimating building age from facade images using deep learning. However, building an accurate deep learning model requires a considerable amount of labelled training data, and the trained models often have geographical constraints. Recently, large pre-trained vision language models (VLMs) such as GPT-4 Vision, which demonstrate significant generalisation capabilities, have emerged as potential training-free tools for dealing with specific vision tasks, but their applicability and reliability for building information remain unexplored. In this study, a zero-shot building age classifier for facade images is developed using prompts that include logical instructions. Taking London as a test case, we introduce a new dataset, FI-London, comprising facade images and building age epochs. Although the training-free classifier achieved a modest accuracy of 39.69%, the mean absolute error of 0.85 decades indicates that the model can predict building age epochs successfully albeit with a small bias. The ensuing discussion reveals that the classifier struggles to predict the age of very old buildings and is challenged by fine-grained predictions within 2 decades. Overall, the classifier utilising GPT-4 Vision is capable of predicting the rough age epoch of a building from a single facade image without any training. The code and dataset are available at https://zichaozeng.github.io/ba_classifier.

Under and Through Water Datasets for Geospatial Studies: the 2023 ISPRS Scientific Initiative “NAUTILUS”

Abstract. Benchmark datasets have become increasingly widespread in the scientific community as a method of comparison, validation, and improvement of theories and techniques thanks to more affordable means for sharing. While this especially holds for test sites and data collected above the water, publicly accessible benchmark activities for geospatial analyses in the underwater environment are not very common. Applying geomatic techniques underwater is challenging and expensive, especially when dealing with deep water and offshore operations. Moreover, benchmarking requires ground truth data for which, in water, several open issues exist concerning geometry and radiometry. Recognizing this scientific and technological challenge, the NAUTILUS (uNder And throUgh waTer datasets for geospatIaL stUdieS) project aims to create guidelines for new multi-sensor/cross-modality benchmark datasets. The project focuses on (i) surveying the actual needs and gaps in through and under-the-water geospatial applications through a questionnaire and interviews, (ii) launching a unique publicly available database collecting already existing datasets scattered across the web and literature, (iii) designing and identifying proper test site(s) and methodologies to deliver to the extended underwater community a brand-new multi-sensor/cross-modality benchmark dataset. The project outputs are available to researchers and practitioners in underwater measurements-related domains, as they can now access a comprehensive tool providing a synthesis of open questions and data already available. In doing so, past research efforts to collect and publish datasets have received additional credit and visibility.

Determination of the Geoid Heights of Awka and Environ from the Satellite Altimetry data using Broadview RADAR Altimeter Toolbox (BRAT) and Sentinel-3 Missions

Satellite altimetry has revolutionized our ability to measure Earth's surface with unprecedented accuracy, offering invaluable insights into various geophysical phenomena. This study presents the determination of geoid heights (Ns) of Awka and Environ utilizing the Broadview RADAR Altimeter Toolbox (BRAT) in conjunction with data from the Sentinel-3 missions. The geoid, a surface of constant gravitational potential representing mean sea level, is a fundamental reference surface for geodetic measurements and understanding Earth's gravity field. The methodology involves processing raw altimetry data acquired by the Sentinel-3 missions using BRAT, followed by precise corrections for various factors affecting the altimeter measurements, such as atmospheric delays, sea state bias, and orbit errors. Subsequently, the derived SSH data are combined with precise geoid models to compute the geoid heights at different spatial resolutions. The results demonstrate the effectiveness of the approach in determining geoid height (N) with high precision and spatial resolution, offering valuable contributions to geodetic research and applications. The utilization of Sentinel-3 data combined with BRAT facilitates robust and accurate geoid determination, which is essential for a wide range of geospatial applications, including oceanography, geophysics, and climate studies. The integration of BRAT and Sentinel-3 missions offers a powerful tool for geodetic research and applications, contributing to our understanding of Earth's dynamic processes and improving the accuracy of geospatial measurements.

Zero-shot urban function inference with street view images through prompting a pretrained vision-language model

Inferring urban functions using street view images (SVIs) has gained tremendous momentum. The recent prosperity of large-scale vision-language pretrained models sheds light on addressing some long-standing challenges in this regard, for example, heavy reliance on labeled samples and computing resources. In this paper, we present a novel prompting framework for enabling the pretrained vision-language model CLIP to effectively infer fine-grained urban functions with SVIs in a zero-shot manner, that is, without labeled samples and model training. The prompting framework UrbanCLIP comprises an urban taxonomy and several urban function prompt templates, in order to (1) bridge the abstract urban function categories and concrete urban object types that can be readily understood by CLIP, and (2) mitigate the interference in SVIs, for example, street-side trees and vehicles. We conduct extensive experiments to verify the effectiveness of UrbanCLIP. The results indicate that the zero-shot UrbanCLIP largely surpasses several competitive supervised baselines, e.g. a fine-tuned ResNet, and its advantages become more prominent in cross-city transfer tests. In addition, UrbanCLIP’s zero-shot performance is considerably better than the vanilla CLIP. Overall, UrbanCLIP is a simple yet effective framework for urban function inference, and showcases the potential of foundation models for geospatial applications.

Multi-sensor Data Analysis for Aerial Image Semantic Segmentation and Vectorization

Abstract. One of the urgent and constantly in demand problems is updating maps. Maps, representing geo-information in vector form, have undoubted advantages in compactness and ”readability” compared to aerial photographs. The issue of maps actuality is critically important for rational urban planning, precision farming, the relevance of the cadastre and other geospatial applications. Various sources of data are used for maps updating, with aerial imagery being the main and rich source of information. Automatic processing of aerial photographs makes it possible to efficiently extract vector information, providing operational monitoring and accounting for changes that have appeared. The presented study addresses the problem of multi sensor information fusion in order to obtain accurate vector information. We use aerial images as a main data source and additionally the data of laser scanning and ground survey to increase performance of automatic image semantic segmentation and vectorization. The proposed framework is demonstrated on the task of forest monitoring.

WEB VISUALIZATION AND SPATIAL ANALYSIS OF TREE SPECIES DISTRIBUTION AT UNIVERSITY OF THE PHILIPPINES DILIMAN ACADEMIC OVAL

Abstract. Understanding the distribution of tree species is crucial for ecological comprehension and effective conservation strategies. This research presents a web-based platform aimed at visually representing the trees found at the University of the Philippines Diliman campus. Open-source geospatial tools such as OpenStreetMap, KoboToolbox, Pl@ntNet, JOSM, uMap, and QGIS were employed for data visualization, collection, analysis, validation, and spatial pattern assessment. Through these tools, a total of 11 tree species were identified, with acacia emerging as the dominant species, especially concentrated along the academic oval. The application of Kernel Density Estimation (HeatMap) in QGIS revealed these spatial distribution patterns. The findings contribute to the effective management, conservation, and restoration of the campus' green spaces, shedding light on the significance of trees in urban ecosystems. The online interactive map developed serves as a valuable tool for improving management and conservation strategies. Importantly, the study assesses the effectiveness of open-source tools, showcasing their versatility and comparable capabilities to proprietary alternatives in geospatial applications.

Evaluating the accuracy of the digital elevation models (DEMs) within the territory of Bosnia and Herzegovina

This study focuses on evaluating the accuracy of Digital Elevation Models (DEMs) in Bosnia and Herzegovina, a region characterized by diverse topography. The accuracy of the gravimetric geoid model is directly correlated with the resolution and accuracy of the digital elevation model (DEM). To date, no research has been conducted on the accuracy of global and regional models for the area of Bosnia and Herzegovina, using ground control points. The analysis encompasses various aspects of DEMs, including interpolation methods (bicubic, B-spline, bilinear, and nearest neighbor), data sources, control points, and datum alignment. Both global and regional DEM models are integrated, introducing a native terrain model. Two sets of control points are utilized for accuracy assessment, along with datum alignment approach. The performance of the BIHDEM model is scrutinized, and a comparison with LIDAR data is conducted. Accuracy assessment involves calculating the Root Mean Square Error (RMSE) for all DEMs and interpolation methods. The study reveals that the bicubic interpolation method yields the best results. The BIHDEM model proves to be the most effective for SET1 (high precision leveling network), while COPERNICUS emerges as the optimal choice for SET2 (trigonometric network). Furthermore, an analysis of the elevation profiles obtained from LIDAR and DEM models highlights significant differences, with ASTER, MERIT, NASADEM and SRTM exhibiting the largest variations. In contrast, FABDEM and BIHDEM models demonstrate minimal differences. Mean values and RMSE further support the superiority of FABDEM and BIHDEM models, providing valuable insights for geospatial applications in the region.

Enhancing Maritime Navigational Safety: Ship Trajectory Prediction Using ACoAtt–LSTM and AIS Data

Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emerging as a new trend in sophisticated geospatial applications. However, the complexity of the marine environment and data quality issues pose significant challenges to accurate ship trajectory forecasting. This study introduces an innovative trajectory prediction method, combining data encoding representation, attribute correlation attention module, and long short-term memory network. Initially, we process AIS data using data encoding conversion technology to improve representation efficiency and reduce complexity. This encoding not only preserves key information from the original data but also provides a more efficient input format for deep learning models. Subsequently, we incorporate the attribute correlation attention module, utilizing a multi-head attention mechanism to capture complex relationships between dynamic ship attributes, such as speed and direction, thereby enhancing the model’s understanding of implicit time series patterns in the data. Finally, leveraging the long short-term memory network’s capability for processing time series data, our approach effectively predicts future ship trajectories. In our experiments, we trained and tested our model using a historical AIS dataset. The results demonstrate that our model surpasses other classic intelligent models and advanced models with attention mechanisms in terms of trajectory prediction accuracy and stability.

Performance Assessment of Mobile Laser Scanning Systems Using Velodyne Hdl-32e

Mapping systems using multi-beam LiDARs are widely used nowadays for different geospatial applications graduating from indoor projects to outdoor city-wide projects. These mobile mapping systems can be either ground-based or aerial-based systems and are mostly equipped with inertial navigation systems INS. The Velodyne HDL-32 LiDAR is a well-known 360° spinning multi-beam laser scanner that is widely used in outdoor and indoor mobile mapping systems. The performance of such LiDARs is an ongoing research topic which is quite important for the quality assurance and quality control topic. The performance of this LiDAR type is correlated to many factors either related to the device itself or the design of the mobile mapping system. Regarding design, most of the mapping systems are equipped with a single Velodyne HDL32 in a specific orientation angle which is different among the mapping systems manufacturers. The LiDAR orientation angle has a significant impact on the performance in terms of the density and coverage of the produced point clouds. Furthermore, during the lifetime of this multi-beam LiDAR, one or more beams may be defected and then either continue the production or returned to the manufacturer to be fixed which then cost time and money. In this paper, the design impact analysis of a mobile laser scanning (MLS) system equipped with a single Velodyne HDL-32E will be clarified and a clear relationship is given between the orientation angle of the LiDAR and the output density of points. The ideal angular orientation of a single Velodyne HDL-32E is found to be at 35° in a mobile mapping system. Furthermore, we investigated the degradation of points density when one of the 32 beams is defected and quantified the density loss percentage and to the best of our knowledge, this is not presented in literature before. It is found that a maximum of about 8% point density loss occurs on the ground and 4% on the facades when having a defected beam of the Velodyne HDL-32E.

The South Carolina Water Monitoring Portal - A Hub for South Carolina Water Quality Monitoring Data

The South Carolina (SC) Water Monitoring Portal is an ArcGIS-based, geospatial application that incorporates water quality data from a variety of sources and projects them onto an interactive map. This application will provide those interested in accessing and retrieving South Carolina water quality data with a free and easyto- use tool that allows for spatial visualization, interaction with, and direct download of data. At the time of this publication, the application accesses roughly four million water quality data records collected by seventeen different organizations. To allow users to seamlessly find and access the data they need, filters are available to query the data to match the user's needs. Once users find the data applicable to them, they can download and export it into various formats. The application also allows groups and organizations to indirectly upload their own water quality data to have it incorporated into the application. With these capabilities in a spatial format, this application has the potential to provide many benefits. For example, in academia it could be utilized in both classroom and research settings, and regulatory agencies could use this application to communicate water quality from a spatial viewpoint, potentially fostering a better understanding of water quality to stakeholders. The management and protection of our state’s waters are difficult to achieve when the data pertaining to water quality isn’t easily accessible or available in a user-friendly format. The SC Water Monitoring Portal has the potential to aid in the protection and management of our state’s waters as it makes water quality data more accessible and the retrieval of data more user-friendly. The SC Water Monitoring Portal is hosted on the South Carolina (SC) Sea Grant Consortium and the College of Charleston’s Lowcountry Hazards Center websites, making it available to anyone interested in water quality data in the State of South Carolina (available online: www.scseagrant.org/SCWMP).