Landslides pose a significant threat to the lives and livelihoods of marginalised communities residing in rural areas and the delicate ecological balance of the environment.Implementing advanced technologies is crucia...Landslides pose a significant threat to the lives and livelihoods of marginalised communities residing in rural areas and the delicate ecological balance of the environment.Implementing advanced technologies is crucial for improving hazard risk assessment and enhancing preparedness measures in regions characterised by diverse topography and complex geological formations.Geospatial applications and modelling techniques have emerged as indispensable in mitigating landslide risks,particularly in environmentally sensitive areas.This study presents a comprehensive approach to landslide susceptibility mapping in the Nilgiri District of Tamil Nadu,India,leveraging the power of Artificial Neural Networks(ANNs)and integrating multi-dimensional geospatial datasets.Integrating ANN-based modelling and geospatial techniques offers significant advantages in terms of statistical robustness,reproducibility,and the ability to analyze the complex interplay of factors influencing landslide hazards quantitatively.The methodology involves rigorous pre-processing and integrating spatial data,including landslide event occurrences as the dependent variable and ten independent parameters influencing landslide susceptibility.These parameters encompass elevation,slope aspect,slope degree,distance to roads,land use patterns,geomorphology,lithology,drainage density,lineament density,and rainfall distribution.Feature extraction and selection techniques are employed to effectively model the complex interactions between these factors and landslide occurrences.This process identifies the most relevant variables influencing landslide susceptibility,enhancing the model's predictive capabilities.The state-of-the-art ANNs are trained using historical landslide occurrence data and the selected influencing factors,enabling the development of a robust and accurate landslide susceptibility model.The performance of the developed model is rigorously evaluated using a comprehensive suite of metrics,including accuracy,precision,and the Area under the Receiver Operating Characteristic(ROC)curve.Preliminary results indicate that the ANN-based landslide susceptibility model outperforms traditional zonation methods,demonstrating higher accuracy and reliability in predicting landslideprone areas.The resulting Landslide Susceptibility Map(LSM)categorises the study area into five distinct hazard zones,ranging from very high(664.1 km^(2)),high(598.9 km^(2)),moderate(639.7 km^(2)),low(478.9 km^(2))and to very low(170.9 km^(2)).Notably,the eastern and central regions of the district emerge as particularly vulnerable to landslide occurrences.The study's findings have far-reaching implications for disaster risk reduction efforts,landuse planning,and sustainable development strategies in the ecologically sensitive Nilgiri District and beyond.展开更多
This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By e...This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.展开更多
With the increase of different sensors,applications and customers,the demand from data providers and users is for a new geospatial data service model,which supports low cost,high dexterity,and which would provide a co...With the increase of different sensors,applications and customers,the demand from data providers and users is for a new geospatial data service model,which supports low cost,high dexterity,and which would provide a comprehensive service.Based on such requirements and demands,the 21AT TripleSat constellation terminal and data delivery and management system has been developed by a Beijing based high-tech enterprise,Twenty First Century Aerospace Technology Co.,Ltd.(21AT).The company is the first commercial Earth observation satellite operator and service provider in China.This new geospatial data service model allows the user to directly access multi-source satellite data,manage the data order,and carry out automatic massive data production and delivery.The solution also implements safe and hierarchical user management,statistical data analysis,and automatic information reports.In addition,a mobile application is also available for users to easily access system functions.This new geospatial solution has already been successfully applied and installed in many customer sites in China,and is now available globally for international clients interested in fast geospatial solutions.It enables the success of customers’operational services.Besides providing TripleSat Constellation images,the multi-source data access system also allows the users to access other satellite data sources,based on customized agreement.This paper describes and discusses this new geospatial data service model.展开更多
The Step Pyramid of Djoser at Saqqara, Egypt is one of the oldest stone monuments in the world and along with other historical monuments of this area is included in the World Heritage List of UNESCO （United Nations E...The Step Pyramid of Djoser at Saqqara, Egypt is one of the oldest stone monuments in the world and along with other historical monuments of this area is included in the World Heritage List of UNESCO （United Nations Educational, Scientific, and Cultural Organization）. In a way, this monument was an experimental construction and served as a prototype for other pyramids afterwards built in Ancient Egypt. Innovative materials, mortar, construction and engineering solutions were introduced and approbated during the construction process of the Step Pyramid. Therefore, the reconstruction of this monument possibly close to its original state is an extremely difficult task. The preservation of this pyramid for future generations is a challenge to the specialists of various scientific fields. Current study is focusing on systematic assessment of the exposed surfaces of the pyramid＇s facades identifying various stone material weathering types and their intensities, as well as major deformations of the structure further integrated into the geospatial model of the pyramid. The results of this study provide possibility to determine the most endangered areas of pyramid＇s facades and calculate the volume of necessary reconstruction work.展开更多
This paper presents a dynamic geospatial knowledge graph as part of The World Avatar project,with an underlying ontology based on CityGML 2.0 for three-dimensional geometrical city objects.We comprehensively evaluated...This paper presents a dynamic geospatial knowledge graph as part of The World Avatar project,with an underlying ontology based on CityGML 2.0 for three-dimensional geometrical city objects.We comprehensively evaluated,repaired and refined an existing CityGML ontology to produce an improved version that could pass the necessary tests and complete unit test development.A corresponding data transformation tool,originally designed to work alongside CityGML,was extended.This allowed for the transformation of original data into a form of semantic triples.We compared various scalable technologies for this semantic data storage and chose Blazegraph™as it provided the required geospatial search functionality.We also evaluated scalable hardware data solutions and file systems using the publicly available CityGML 2.0 data of Charlottenburg in Berlin,Germany as a working example.The structural isomorphism of the CityGML schemas and the OntoCityGML Tbox allowed the data to be transformed without loss of information.Efficient geospatial search algorithms allowed us to retrieve building data from any point in a city using coordinates.The use of named graphs and namespaces for data partitioning ensured the system performance stayed well below its capacity limits.This was achieved by evaluating scalable and dedicated data storage hardware capable of hosting expansible file systems,which strengthened the architectural foundations of the target system.展开更多
文摘Landslides pose a significant threat to the lives and livelihoods of marginalised communities residing in rural areas and the delicate ecological balance of the environment.Implementing advanced technologies is crucial for improving hazard risk assessment and enhancing preparedness measures in regions characterised by diverse topography and complex geological formations.Geospatial applications and modelling techniques have emerged as indispensable in mitigating landslide risks,particularly in environmentally sensitive areas.This study presents a comprehensive approach to landslide susceptibility mapping in the Nilgiri District of Tamil Nadu,India,leveraging the power of Artificial Neural Networks(ANNs)and integrating multi-dimensional geospatial datasets.Integrating ANN-based modelling and geospatial techniques offers significant advantages in terms of statistical robustness,reproducibility,and the ability to analyze the complex interplay of factors influencing landslide hazards quantitatively.The methodology involves rigorous pre-processing and integrating spatial data,including landslide event occurrences as the dependent variable and ten independent parameters influencing landslide susceptibility.These parameters encompass elevation,slope aspect,slope degree,distance to roads,land use patterns,geomorphology,lithology,drainage density,lineament density,and rainfall distribution.Feature extraction and selection techniques are employed to effectively model the complex interactions between these factors and landslide occurrences.This process identifies the most relevant variables influencing landslide susceptibility,enhancing the model's predictive capabilities.The state-of-the-art ANNs are trained using historical landslide occurrence data and the selected influencing factors,enabling the development of a robust and accurate landslide susceptibility model.The performance of the developed model is rigorously evaluated using a comprehensive suite of metrics,including accuracy,precision,and the Area under the Receiver Operating Characteristic(ROC)curve.Preliminary results indicate that the ANN-based landslide susceptibility model outperforms traditional zonation methods,demonstrating higher accuracy and reliability in predicting landslideprone areas.The resulting Landslide Susceptibility Map(LSM)categorises the study area into five distinct hazard zones,ranging from very high(664.1 km^(2)),high(598.9 km^(2)),moderate(639.7 km^(2)),low(478.9 km^(2))and to very low(170.9 km^(2)).Notably,the eastern and central regions of the district emerge as particularly vulnerable to landslide occurrences.The study's findings have far-reaching implications for disaster risk reduction efforts,landuse planning,and sustainable development strategies in the ecologically sensitive Nilgiri District and beyond.
文摘This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.
基金supported by the project of Beijing Municipal Science and Technology Commission and Science and Technology Innovation Base of Cultivating and Developing Engineering[grant number Z161100005016069]the National High Technology Research and Development Program[grant number 2013AA12A303].
文摘With the increase of different sensors,applications and customers,the demand from data providers and users is for a new geospatial data service model,which supports low cost,high dexterity,and which would provide a comprehensive service.Based on such requirements and demands,the 21AT TripleSat constellation terminal and data delivery and management system has been developed by a Beijing based high-tech enterprise,Twenty First Century Aerospace Technology Co.,Ltd.(21AT).The company is the first commercial Earth observation satellite operator and service provider in China.This new geospatial data service model allows the user to directly access multi-source satellite data,manage the data order,and carry out automatic massive data production and delivery.The solution also implements safe and hierarchical user management,statistical data analysis,and automatic information reports.In addition,a mobile application is also available for users to easily access system functions.This new geospatial solution has already been successfully applied and installed in many customer sites in China,and is now available globally for international clients interested in fast geospatial solutions.It enables the success of customers’operational services.Besides providing TripleSat Constellation images,the multi-source data access system also allows the users to access other satellite data sources,based on customized agreement.This paper describes and discusses this new geospatial data service model.
文摘The Step Pyramid of Djoser at Saqqara, Egypt is one of the oldest stone monuments in the world and along with other historical monuments of this area is included in the World Heritage List of UNESCO （United Nations Educational, Scientific, and Cultural Organization）. In a way, this monument was an experimental construction and served as a prototype for other pyramids afterwards built in Ancient Egypt. Innovative materials, mortar, construction and engineering solutions were introduced and approbated during the construction process of the Step Pyramid. Therefore, the reconstruction of this monument possibly close to its original state is an extremely difficult task. The preservation of this pyramid for future generations is a challenge to the specialists of various scientific fields. Current study is focusing on systematic assessment of the exposed surfaces of the pyramid＇s facades identifying various stone material weathering types and their intensities, as well as major deformations of the structure further integrated into the geospatial model of the pyramid. The results of this study provide possibility to determine the most endangered areas of pyramid＇s facades and calculate the volume of necessary reconstruction work.
文摘This paper presents a dynamic geospatial knowledge graph as part of The World Avatar project,with an underlying ontology based on CityGML 2.0 for three-dimensional geometrical city objects.We comprehensively evaluated,repaired and refined an existing CityGML ontology to produce an improved version that could pass the necessary tests and complete unit test development.A corresponding data transformation tool,originally designed to work alongside CityGML,was extended.This allowed for the transformation of original data into a form of semantic triples.We compared various scalable technologies for this semantic data storage and chose Blazegraph™as it provided the required geospatial search functionality.We also evaluated scalable hardware data solutions and file systems using the publicly available CityGML 2.0 data of Charlottenburg in Berlin,Germany as a working example.The structural isomorphism of the CityGML schemas and the OntoCityGML Tbox allowed the data to be transformed without loss of information.Efficient geospatial search algorithms allowed us to retrieve building data from any point in a city using coordinates.The use of named graphs and namespaces for data partitioning ensured the system performance stayed well below its capacity limits.This was achieved by evaluating scalable and dedicated data storage hardware capable of hosting expansible file systems,which strengthened the architectural foundations of the target system.
