 Subjects -> AERONAUTICS AND SPACE FLIGHT (Total: 124 journals)
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- Leveraging artificial intelligence for computational urban analysis:
building StreetRose with ChatGPT-
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Abstract: The emergence and rapid development of large language models (LLMs) has changed the way we interact with information and solve complex problems. The aim of this study is to explore how an urban analysis tool can be created and used effectively with the aid of a large language model. By focusing on this objective, we highlight both the potential and the limitations of ChatGPT. Using GPT-3.5 Turbo, one of the popular AI-powered Large Language Models, we built "StreetRose 0.0.1", a Python-based application. OpenStreetMap was used as a data source. The development process followed the steps of data collection, software development, analysis and visualisation. The resulting tool, StreetRose, visualises street trends in settlements and provides insights into street network analysis. This study provides an example of the use of ChatGPT in urban studies and discusses its advantages and disadvantages. In addition, the tool provides practical information and visualisations that can help researchers interested in settlements and serve as a valuable asset for urban planning and street network analysis. The results show that there are areas for improvement and shortcomings that need to be addressed, although ChatGPT significantly speeds up the coding process. The study also highlights the transformative impact of LLMs on urban analysis and sets an example for future applications in this field.
PubDate: 2025-03-29
- Assessing the spatial accuracy of geocoding flood-related imagery using
Vision Language Models-
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Abstract: While the capabilities of large language models and visual language models for various classification tasks have advanced significantly, their potential for location inference remains largely underexplored. Therefore, this study evaluates the performance of four prominent models — BLIP-2, LLaVA1.6, OpenFlamingo, and GPT-4o — for geocoding flood-related images from Flickr. Model inferences are compared against the original photo locations and human-labelled assessments. Our findings reveal that GPT-4o achieves the highest spatial accuracy (median deviation of 89.12 km). OpenFlamingo geocodes the highest number of images (90.7%), albeit with fluctuating quality (median 408.35 km), while still outperforming the human annotators. LLaVA1.6 geocodes only 18.9% of all images, while BLIP-2 exhibits the highest median deviation (1,781 km). We observe a spatial bias in our results, with inferences being most accurate in Central Europe. Additionally, model results improve when images feature recognisable landmarks. The proposed workflow could significantly increase the amount of geocoded web-based data available for disaster management, though further research is required to enhance accuracy across diverse geographic contexts.
PubDate: 2025-03-22
- Land suitability analysis for apple cultivation in mountainous Kashmir
valley using scenario based modelling-
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Abstract: The Kashmir Valley, renowned for its horticultural potential, has increasingly shifted towards apple cultivation due to favourable climatic conditions and economic benefits. In North Kashmir, land scarcity, geophysical instability, soil degradation, and socioeconomic vulnerabilities hinder agricultural output. By employing the scenario-based modelling integrated with Geographic Information System (GIS), a comprehensive suitability map for apple cultivation was developed, considering physical, climatic, and socioeconomic factors. The analysis categorized land into five suitability classes (S1–S5), identifying 12% of the area as highly suitable (S1) and 64.3% as unsuitable (S4 and S5) due to rugged terrain, rocky outcrops, and water bodies. Soil texture, physiography, and elevation were key determinants of suitability, with Karewa’s emerging as the most favourable zones. While socioeconomic enhancements could improve marginal areas, environmental constraints like slope and elevation impose rigid limitations. In order to maximise apple production while preserving ecological and financial stability, the study emphasises the necessity of sustainable management and targeted planning of high-suitability sites. In environmentally sensitive areas, this methodology offers a reproducible framework for combining sustainable agriculture methods with land-use planning.
PubDate: 2025-03-13
- Quantifying the economic impact of nature, recreation and heritage: a
spatial assessment of land prices in alpine tourism regions-
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Abstract: The dynamics of land prices in Alpine tourism regions are shaped by natural amenities, limited buildable land, and demand from second-home owners, posing significant challenges for sustainable regional development. This study examines the spatial impacts of alternative determinants of land value in Tyrol, Austria, including recreational activities, cultural heritage, and environmental features, using a Spatial Durbin Error Model (SDEM) while controlling for primary land value drivers. The results reveal skiing as the most influential recreational driver of land prices, with strong direct and spillover effects, while running and cycling activities show significant localized positive impacts, with the latter exhibiting nonlinear effects influenced by topographical and infrastructural variations across municipalities. Environmental features such as Natura 2000 protected areas and water areas consistently exhibit negative effects, reflecting restrictions on buildable land use. Cultural heritage positively impacts land values; however, its influence diminishes when control variables are introduced. The inclusion of controls enhances the model’s explanatory power, confirming the critical roles of tourism intensity, residential and leisure attractiveness in shaping land markets but it also underscores the relevance of the alternative determinants investigated, highlighting the delicate balance and inherent tensions between tourism growth, housing affordability, and environmental protection in Alpine regions. This emphasizes the necessity of sustainable land use strategies to manage these competing priorities effectively. Furthermore, the study leverages Strava heatmap data to provide advanced insights into physical activity patterns, demonstrating the potential of alternative and multimodal data in spatial econometric analysis.
PubDate: 2025-03-08
- Prioritizing flood drivers: an AHP-based study of physical factors in
Digha’s coastal belt, East Coast, India-
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Abstract: Assessing flood-triggering factors and implementing risk reduction approaches are crucial policy issues, especially in low-lying coastal areas that experience significant losses due to flood disasters. In low-lying Digha, West Bengal coastal region has suffered from saltwater intrusion due to flow accumulation of Topographic Wetness Index (TWI). Nonetheless, a scarcity of research exists regarding experts’ viewpoints on identifying factors that lead to floods. Consequently, utilizing an Analytic Hierarchy Process (AHP) questionnaire survey involving 25 participants, this study aims to investigate experts’ perspectives on the impact of distinct climatic and non-climatic elements in initiating flash floods. A total of five triggering factors have been assessed by the expert opinion and mapped them through GIS (Geographical Information System). The obtained values are 1.12 RI for five criteria, CI of 0.085, and CR of 0.0765. The final weights were obtained where 9% weights in the distance to the coastline, 33.9% in slope, 27.8% in LULC, 21.8% in TWI, and 7.5% in the distance to river. Hence, achieving this objective may be possible by involving professionals to gather crucial data on predicting and managing disaster risks and understanding the demographics of communities residing in areas prone to disasters.
PubDate: 2025-03-07
- Leveraging geographic information systems (GIS) in water, sanitation, and
hygiene (WASH) research: a systematic review of applications and
challenges-
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Abstract: Safe drinking water, sanitation, and hygiene (WASH) are essential for the health, well-being, and socio-economic development of communities. Despite global efforts, the challenge of providing safe access to WASH service persists, particularly in low- and middle-income countries. Geographic Information Systems (GIS) play a pivotal role in understanding and addressing these challenges by enabling the monitoring, mapping, and analysis of WASH facilities and their impacts. This systematic literature review aims to comprehensively understand how GIS is being used in WASH research. The review reveals that GIS is being used in various aspects of WASH, including mapping and monitoring of WASH facilities, spatial analysis of WASH-related health outcomes, and planning. The review also highlights the challenges of using GIS in WASH, such as data availability and quality, integration of technological advancement and adoption of a comprehensive approach. The review provides valuable insights for researchers, practitioners, and policymakers working in the field of WASH.
PubDate: 2025-02-28
- Spatial distribution of naming patterns in Russian law firms through text
embeddings-
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Abstract: The present study analyzes the naming of law firms in Russia, with a particular focus on the geographical aspects of this phenomenon. The main objectives of this research are to identify law firm naming patterns, to categorise them and to test whether there are any spatial regularities in the distribution of law firms according to the identified patterns. To conduct the analysis, data regarding law firms is extracted from the open dumps of the state registry of small and medium-sized enterprises. The names of the firms are transformed into numerical vectors, subsequently clustered with the KMeans method, and then manually grouped to identify naming patterns and strategies. Global and local Moran’s I statistics are calculated to test for spatial autocorrelation in the proportion of a given pattern in the regions. As a result, 22 naming patterns are identified, grouped into four naming strategies. Apart from no-strategy (40% of law firms), the most popular strategy is belonging (to the legal services market—30%), followed by uniqueness (of service or location—20%) and market (words and phrases with positive or promotional meaning—10%). The research findings indicate that the spatial distribution of law firm naming patterns is generally not significantly different from random, with two exceptions: geographical branding in Siberia and an unusual heighborhood of regions with high and low proportions of “Expert” and “Personal brand” patterns in the European South.
PubDate: 2025-02-27
- Mensuration of FABDEM’s vertical accuracy using ICESat-2 photon
profiles over sloped terrains-
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Abstract: This research evaluates the vertical accuracy of the Forest and Buildings Removed Copernicus Digital Elevation Model (FABDEM), the first open-access bare-earth model with a spatial resolution of 30 m. Using elevation data from the return photons of the ICESat-2 ATLAS sensor as a reference, the study assessed the uncertainties associated with FABDEM in sloped terrains covered with forests and buildings. Three test sites were analyzed: Lavasa, Shimla, and Mussoorie. The evaluation involved calculating the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) and a visual comparison of the 2D elevation profiles from FABDEM and ICESat-2. The error metrics revealed uncertainties of 5.28 m (RMSE) and 4.01 m (MAE) for Lavasa, characterized by low-lying hills. In contrast, Shimla and Mussoorie, located in the Himalayas, showed uncertainties of 8.5 m (RMSE) and 6.5 m (MAE). Nearly 97% of the FABDEM profiles were lower than the canopy heights and buildings, indicating the model's effectiveness in minimizing biases. This research opines that the FABDEM is the best available open-access bare-earth model for these challenging terrains.
PubDate: 2025-02-27
- Spatial assessment of the availability of healthcare facilities at
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Abstract: The availability of healthcare facilities equipped with allied health services is crucial for achieving better health. Balochistan, the largest province of Pakistan by area, faces severe problems in ensuring the availability of healthcare facilities at the district level. This study aims to assess the geospatial analysis of the distribution and availability of healthcare facilities at the district level across Balochistan province. Data from global and national open sources were used to generate maps showing the location of healthcare facilities at the district level across the province. The data on healthcare facilities were collected from the Health Department of the Government of Balochistan. The geospatial analysis was carried out using ArcGIS 10.8.2 software. Additionally, the health care services were overlaid with the district’s population density to measure availability. The findings revealed the categorization of districts as low, medium and high based on the availability of healthcare facilities present in each district concerning population. The findings revealed that the number of hospitals in the districts of Balochistan varies i.e., availability is low in 13 districts, medium in 12 districts and high in 7 districts.
PubDate: 2025-02-13
- From core to fragmented: analysing the morphological spatial patterns and
significant decline of deforestation in regional forest landscape of
Telangana, India-
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Abstract: Mapping and quantifying forest cover change and fragmentation is essential for effective conservation planning. Assessing forest fragmentation and connectivity requires advanced geospatial tools and techniques that provide accurate and timely information. This study focuses on forest cover mapping and morphological spatial pattern analysis to assess changes in forest cover, forest fragmentation and connectivity in the regional forest landscape of Bhadradri-Kothagudem district, Telangana between 1972 and 2023. Overall, net forest cover decreased by 37.3% between 1972 and 2023, resulting in fragmented landscapes that disrupt wildlife movement, gene flow, and overall ecosystem connectivity. The dominant morphological type is core forest, which makes up 50.5% of the total forest area in 2023. The findings revealed that deforestation and fragmentation of forest cover continued, but at a slower rate than in previous decades. By measuring the spatial patterns of forest cover change and connectivity, study provides valuable insights into the extent of forest fragmentation. This information is essential for developing targeted interventions, prioritizing restoration efforts, and mitigating the adverse effects of fragmentation on species survival and ecosystem resilience.
PubDate: 2025-02-08
- An open-source framework for mosaic radar-based rainfall estimation across
Thailand's watersheds-
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Abstract: Thailand frequently faces meteorological hazards, such as storms and floods, necessitating accurate rainfall monitoring for disaster management. This study presents an open-source radar-based rainfall mosaic framework to estimate rainfall at a national scale, integrating data from 12 C-band radars operated by the Thailand Meteorological Department (TMD). The framework combines data from both conventional Doppler and dual-polarimetric radars, calibrated with rain gauge measurements within each radar’s 240 km observation radius. These data are mosaicked to produce hourly Constant Altitude Plan Position Indicator (CAPPI) maps. A key innovation is the application of Mean Field Bias (MFB) correction, which significantly improves radar-derived rainfall accuracy. Validation during Tropical Storm Son-Tinh (2018) demonstrated that the Rosenfeld-Tropical Z-R relationship yielded superior performance. Post-MFB correction, radar estimates achieved correlation coefficients exceeding 0.7 in most regions and exhibited reduced bias, supporting operational applications in flood forecasting and water resource management. The outputs include a GIS-compatible spatial rainfall database for hydrological analysis at basin and sub-basin scales. The open-source code promotes customization and collaboration, providing a scalable tool for national meteorological agencies and researchers. This study advances radar-based quantitative precipitation estimation (QPE) in Thailand and highlights its potential for enhancing real-time disaster response and long-term climatological studies.
PubDate: 2025-02-01
- Dengue fever prediction using LSTM and integrated temporal-spatial
attention: a case study of Malaysia-
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Abstract: This paper presents an evaluation of Long Short-Term Memory (LSTM) models with integrated temporal-spatial attention mechanisms for prediction dengue fever in Malaysia. The performance of the models was assessed using Root Mean Square Error (RMSE) as the evaluation metric and compared with other LSTM variations and benchmark methods. The results indicate that the Spatial–Temporal Stacked LSTM (ST-SLSTM) model outperformed all other models, including the Spatial–Temporal LSTM (ST-LSTM) model, with a minimum RMSE of 1.94 for a lookback period of 5. Comparatively, the RMSE values of the LSTM and benchmark techniques like RBFSVM, DT, S-ANN, and D-ANN were higher, ranging from 4.37 to 5.58. These findings demonstrate the superiority of the proposed LSTM models with integrated attention mechanisms in dengue fever prediction. The attention mechanisms effectively capture temporal and spatial patterns, leading to enhanced predictive performance. The implications of this research are significant, offering potential improvements in resource allocation and timely interventions for managing and controlling dengue fever outbreaks. Future research directions include cross-validation in different geographical regions, incorporation of additional data sources, optimization of computational efficiency, and exploration of applications in other healthcare domains.
PubDate: 2025-01-22
- Multivariate spatiotemporal windspeeds prognostics across parts of Pacific
Ocean using the Gaidai risk assessment approach-
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Abstract: It is important to quantify multimodal uncertainties, associated with the monitoring and the management of natural resources e.g., ocean wave and wind energy. Current case the study offers a state-of-the-art methodology for multidimensional environmental/structural systems damage risk and natural hazard prognostics. Proposed reliability approach has been specifically designed for the analysis of quasi-stationary, multi-dimensional engineering systems (both environmental and structural), that were either been simulated numerically over a representative period, or were physically monitored. Presented case study shows that relatively accurate forecasts of the system’s hazard or failure probability/risk are attainable even given a limited underlying dataset. Due to nonstationary and nonlinear correlations between system’s essential elements (or dimensions), high dimensional environmental/structural systems are not easily treated by to existing reliability and risk assessment techniques. Risk assessment being important design issue for marine, naval and offshore structures, operating in particular ocean regions of interest, occasionally encountering adverse weather conditions. Advocated multimodal risk evaluation methodology makes it possible to forecast natural hazards for nonlinear high-dimensional dynamic environmental and structural systems robustly and effectively. Ability to assess risks for spatiotemporal environmental systems, possessing number of interconnected components higher than two, i.e., beyond bivariate systems, being the primary advantage of the advocated novel Gaidai hazard/risk evaluation methodology. Artificial intelligence pattern recognition features of the underlying windspeed dataset are briefly discussed.
PubDate: 2025-01-22
- The land use and land cover changes, 1994–2024: implications for
livelihood options and employment opportunities in Dhanbad, India-
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Abstract: Focusing on the coal mining and potential alternative livelihoods in the context of global net-zero targets by using Remote Sensing (RS) and geospatial techniques, this article examines the Land Use and Land Cover (LULC) changes in Dhanbad district during 1994–2024. Deploying the Landsat-7, 8 and 9 MSS Datasets, supervised classification with a Maximum Likelihood Classifier (MLC) and validation through high-resolution imagery and field observations ensure an 85% accuracy rate. While agriculture land occupies 60% of the area and provides employment to 24% of the population, aquaculture development is supported by a 140% rise in water bodies mostly from repurposed mining sites. Agricultural areas and natural vegetation decrease by 12% and 28% respectively exhibiting resource demands. There is a 275% rise in settlement areas owing to coal mining-related urbanization and population rise. Even though coal mining areas occupy 3–4% of the district’s territory, it is a major reservoir of coal mines in India attracting a large number of migrant workers. Thus, there is an imperative to emphasize the necessity of diversifying the economy in view of the ongoing coal phaseout that would serve as the foundation of policy proposal in pursuit of Dhanbad’s economic growth, environmental sustainability and social wellbeing.
PubDate: 2025-01-20
- Performance comparison between E-learning during COVID-19 pandemic versus
traditional classes utilizing sentiment analysis-
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Abstract: The lockdown was announced on 24 March 2020 due to the ongoing pandemic COVID-19. Universities and colleges were in a panic about the learning of students as traditional classroom sessions were not possible. Human–Computer Interaction (HCI) was the only solution as students had to interact using computers for learning and sharing resources. In this paper, the effectiveness of E-learning and HCI is compared to offline learning. We have collected responses from students of various universities. Furthermore, the LearningSent score is proposed for various questions based on relevance in teaching–learning, assessment, and recruitment. Experiment analysis reveal a lot of interesting insights. Experiment analysis revealed that 71% of respondents are in favor of E-learning, 43% of students are in favor of E-assessment and 30.60% are of the opinion that E-learning is effective for recruitment. Furthermore, 29.50% of students have given a rating of 4 and 6.90% of users have given a rating of 5 to an overall methodology which indicates that improvements. 83% accuracy is achieved using training and testing on responses. Correlation analysis on numerical ratings and calculated regression score is 0.79 which proves that regression score calculated from textual responses using Logistic Regression and numerical ratings are in synchronization.
PubDate: 2025-01-17
- Sustainable urban growth boundaries for ecological protection via 2046
models of İzmir-
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Abstract: Long-term sustainable development that urban lands threaten ecological security in developing countries has made it necessary to define urban growth boundaries in terms of spatial planning. Our study creates a framework to determine the boundaries of the urban structure in 1990 and 2018 in İzmir, which is rapidly developing in terms of its form in 2046. With the high immigration, preserving its existing natural structure poses an environmental problem. It estimates with the Cellular Automata-Markov Chain (CA-MC), based on interactions and constraints, with a multi-scenario approach. In the proposed framework, the validation of future predictions was supported by Kappa coefficients of Kno: 0.9922, Klocation: 0.8747, and KlocationStrata: 0.8747. CA-MC model shows the urban area of İzmir district increased from 104.263 to 150.082 ha according to λMSRA and to 143.665 ha according to λARYA during 2018–2046. While the λMSRA scenario predicts a built-up area of 12.63% by 2046, the λARYA scenario limits it to 12.09%. According to the unique models, when the urban area is left to itself without restrictions, it grows more and pressures other land uses. Urban growth limits must be brought to the agenda for sustainable development and compact urbanization. Graphic Abstract
PubDate: 2025-01-17
- Cellular automata modelling to simulate patterns of urban growth for
Nusantara: Indonesia’s new capital-
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Abstract: This paper uses cellular automata (CA) modelling to simulate possible patterns of urban growth for Nusantara–Indonesia’s new capital. The modelling uses criteria such as projected population growth and planned development stages and a range of relevant factors that influence urban development. Further the study simulates the possible impact of future urban growth on key biodiversity areas (KBAs). Two scenarios were modelled to simulate urban growth patterns–(1) the nature sensitive city and (2) the Indonesian government’s current plan. Results of the scenario-based CA modelling demonstrate that scenario 1 offers a more sustainable and liveable approach to urban growth, despite its larger land footprint. This is achieved by preserving protected and key biodiversity areas, which are essential for the long-term well-being and resilience of the environment. While scenario 2 is more land-efficient, it presents a possible risk to the overall ecological integrity and liveability of the metropolis by impinging into key biodiversity areas. The study’s cellular automata approach and methodology can be employed for urban planning and biodiversity impact assessment in similar contexts of new city development.
PubDate: 2024-10-03
- Tracking five decades (1972–2024) of spatio-temporal dynamics and
hotspots of Prosopis juliflora in Keoladeo national park, a World Heritage
Site-
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Abstract: Assessing the spatial distribution and composition of vegetation is key to biodiversity assessment and conservation efforts. This study examines the changes in Keoladeo National Park (KNP) over the last five decades due to the invasion of Prosopis juliflora. Prosopis deliberately planted in the KNP during the 1960s and 1970s has spread at an alarming rate and significantly affected native terrestrial and wetland species. Emerging hot spot analysis, an advanced space–time pattern mining technique was used to analyze the spatio-temporal trends of invasion by Prosopis juliflora. The annual rate of spread was estimated to be 3.31% over the last five decades (1972–2024). A slight decrease in the distribution of P. juliflora from 2021 indicates that management's continued efforts are having a positive effect. Grasslands decreased by 51%, while P. juliflora increased by 82%, indicating a long-term trend of invasive species encroachment. This study identified different categories of spatiotemporal trends in invasion hotspots. Oscillating hot spots with fluctuating levels of P. juliflora coverage contributed the most (82.73%), followed by persistent hot spots (6.55%) and new hot spots (4.83%). The dominance of oscillating hotspots suggests that reactive management strategies in KNP may be effective in mitigating the spread of P. juliflora. The current work highlights the application of multi-temporal remote sensing data and geospatial techniques to monitor the spread of invasive species and to identify sites where more effective management is needed.
PubDate: 2024-09-18
- Exploring lifestyle patterns from GPS trajectory data: embedding
spatio-temporal context information via geohash and POI-
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Abstract: This study explored the growing potential for knowledge discovery from GPS-based trajectory data across various domains, including urban planning and transportation systems. We analyzed the temporal and spatial data from GPS-derived stay points to examine life patterns. Stay points indicate specific geographic locations and provide data on the occurrence and duration of stays. We extracted these stay points from the GPS trajectory data and organized them into temporal sequences. Each sequence was transformed into a vector, incorporating Points of Interest, geohash codes, occurrence times, and duration times. Using representation learning, we reduced the dimensionality of these vectors and applied clustering techniques to identify distinct life pattern. Our analysis revealed five distinct patterns: locally active, student, school-centric, worker, and homebody. This research makes significant contributions to fields such as urban planning by integrating spatial characteristics with temporal and semantic information in life pattern analysis. The application of representation learning has enabled the discovery of meaningful life patterns from GPS-based trajectories.
PubDate: 2024-08-31
- Flood risk assessment of a small river with limited available data
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Abstract: Flood risk modeling of small watercourses is challenging when only limited input data are available. Therefore, this study assessed the flood characteristics of a small river (Tarna River: entire watershed-C, upper-VS, middle-TMS, and lower section-TOS) from 1990 to 2019. The assessment focused on modeling, model calibration, and validation using feature event-based time-series data in data-scarce environments. We showed that since the 2000s, the number of high-water levels above 250 cm, and the frequency of three flood types had increased. Flood simulation results showed the largest flooded area in the TMS section, followed by the VS, and then the TOS. The outcomes from the VS, TMS, and TOS sections did not exhibit superior performance compared to the C area. Models performed well for larger flood events, with Kling Gupta Efficiency corresponding well to NRMSE and Nash-Sutcliffe efficiency metrics. Accordingly, flood events characterized by the longest duration and high-water levels yielded outstanding results across all areas, followed by moderate flood events with good accuracy. Normal water level events exhibited significant deviations from the reference across all sections. In summary, despite the event-based modeling challenges in data-limited environments, such models can still mitigate potential flood events and improve decision-making processes.
PubDate: 2024-08-30
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