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spatial data mining-focused research on image processing techniques for remotely sensed images

IMAGING SCIENCE JOURNAL 2025年第4期73卷 428-442页

作者： Fan, Yongxia Feng, Shizhou Du, Jing Zhengding Adv Normal Coll Hebei Artificial Intelligence Dept Shijiazhuang Peoples R China Chongqing Coll Int Business & Econ Big Data & Intelligence Engn Sch Chongqing Peoples R China Chongqing Coll Humanities Sci & Technol Sch Comp Sci & Engn Chongqing 401520 Peoples R China

spatial data mining is an important approach for collecting useful data from big datasets, especially remotely sensed images. This study tackles issues in environmental monitoring and management using sophisticated image processing. The Horse Herd Optimization-based VGG19 (HHO-VGG19) is proposed to improve land cover classification, recognition of objects, detection of changes, and detection of anomalies. The study used the BCDD dataset, which was scaled to 512 x 512 pixels, then applied Z-score normalization and extracted features using Principal Component Analysis (PCA). The VGG19 architecture was enhanced by utilizing Horse Herd Optimization to enhance image classification efficiency. The HHO-VGG19 model surpasses conventional techniques, with F1-score of 92%, a recall of 94%, an accuracy of 98.5%, and a 30-second execution time reduction. The findings indicate the efficiency of integrating sophisticated image processing with spatial data mining, giving an effective tool for remote sensing image processing in environmental uses including tracking ecosystems and handling of natural resources.

关键词： spatial data mining image processing remotely sensed images environmental uses Horse Herd Optimization (HHO) tracking ecosystems

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Utilizing IoT Sensors and spatial data mining for Analysis of Urban Space Actors' Behavior in University Campus Space Design

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SENSORS 2025年第5期25卷 1393-1393页

作者： Koszewski, Krzysztof Olszewski, Robert Palka, Piotr Walczak, Renata Korpas, Przemyslaw Dabrowska-Zoltak, Karolina Wyszomirski, Michal Czeranowska-Panufnik, Olga Manujlo, Andrzej Szczepankowska-Bednarek, Urszula Kuzmicz-Kubis, Joanna Szalwa, Anna Ejsmont, Krzysztof Czernic, Pawel Warsaw Univ Technol Fac Architecture Ul Koszykowa 55 PL-00659 Warsaw Poland Warsaw Univ Technol Fac Geodesy & Cartog Plac Politech 1 PL-00651 Warsaw Poland Warsaw Univ Technol Fac Elect & Informat Technol Ul Nowowiejska 15-19 PL-00665 Warsaw Poland Warsaw Univ Technol Fac Civil Engn Mech & Petrochem Ul Lukasiewicza 17 PL-09400 Plock Poland Warsaw Univ Technol Doctoral Sch Ul Rektorska 4 PL-00614 Warsaw Poland Warsaw Univ Technol Fac Power & Aeronaut Engn Nowowiejska 24 PL-00665 Warsaw Poland Warsaw Univ Technol Ctr Innovat Ul Rektorska 4 PL-00614 Warsaw Poland Warsaw Univ Technol Univ Social Responsibil Off Ul Mochnackiego 10 PL-02042 Warsaw Poland Warsaw Univ Technol Fac Mech & Ind Engn Ul Narbutta 85 PL-02524 Warsaw Poland

This paper discusses the use of IoT sensor networks and spatial data mining methods to support the design process in the revitalization of the university campus of the Warsaw University of Technology (WUT) in the spirit of universal design. The aim of the research was to develop a methodology for the use of IoT and edge computing for the acquisition of spatial knowledge based on spatial big data, as well as for the development of an open (geo)information society that shares the responsibility for the process of shaping the spaces of smart cities. The purpose of the article is to verify the hypothesis on whether it is possible to obtain spatial-temporal quantitative data that are useful in the process of designing the space of a university campus using low-cost Internet of Things sensors, i.e., already existing networks of CCTV cameras supported by simple installed beam-crossing sensors. The methodological approach proposed in the article combines two main areas-the use of IT technologies (IoT, big data, spatial data mining) and data-driven design based on analysis of urban space actors' behavior for participatory revitalization of a university campus. The research method applied involves placing a network of locally communicating heterogeneous IoT sensors in the space of a campus. These sensors collect data on the behavior of urban space actors: people and vehicles. The data collected and the knowledge gained from its analysis are used to discuss the shape of the campus space. The testbed of the developed methodology was the central campus of the WUT (Warsaw University of Technology), which made it possible to analyze the time-varying use of the selected campus spaces and to identify the premises for the revitalization project in accordance with contemporary trends in the design of the space of HEIs (higher education institutions), as well as the needs of the academic community and the residents of the capital. The results are used not only to optimize the process o

关键词： IoT networks data management and analytics smart cities urban design data-driven design spatial data mining

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spatial data mining IN AERIAL OBJECT DETECTION dataSETS FOR FINDING CO-LOCATIONS AND ANOMALIES

SPATIAL DATA MINING IN AERIAL OBJECT DETECTION DATASETS FOR ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Sharma, Bishwajit Suman, Pranay Sharma, Anima Mishra Jamal, Arshad Rakshit, Subrata Ctr AI & Robot CAIR Bangalore India

ISBN: (纸本)9798350320107

The remote sensing community has developed algorithms to extract diverse geographic information from electro-optical and radar imagery, and study them along with data from other weather sensors such as wind, temperature and humidity. Simultaneously, the computer vision community has adopted machine learning algorithms for detection problems with promising success. Many such algorithms utilize labeled datasets to learn object signatures for remote sensing. Meanwhile, research in the data mining domain also has evolved spatial algorithms for discovering association and outliers in the geographic data. In a first, the current work introduces techniques developed in data mining domain to the computer vision community utilizing object detection datasets for applications in remote sensing. Specifically, this work applies the concepts of rule mining to geo-registered aerial object detection datasets. Aerial object detection datasets were originally intended for training a classifier. However, this work innovates the use of ground-truth labels with geo-location to identify co-located classes. The paper establishes a method to adapt the aerial object detection dataset to mining friendly data. A theoretical analysis of the co-location frequency and size of search area is presented. The search area size for a set of objects is heuristically estimated. The work utilizes state-of-the-art data mining methods and uses the Support and Lift metrics to study the effects of varying count of object instances per class. In addition to finding prominent co-locations and anomalies, this work demonstrates how composite objects can be detected, which have varying spatial configurations of their constituents. The developed method is tested on publicly available XView (60-classes) and FAIR1M (37-classes) datasets.

关键词： object detection co-location mining Geo-AI spatial data mining association rule mining

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The use of spatial data mining methods for modeling HR challenges of generation Z in greater Poland Region

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2023年第1期31卷 205-237页

作者： Graczyk-Kucharska, Magdalena Olszewski, Robert Weber, Gerhard-Wilhelm Poznan Univ Tech Fac Engn Management Ul Jacka Rychlewskiego 2 PL-60965 Poznan Poland Warsaw Univ Technol Fac Geodesy & Cartog Pl Politech 1 PL-00661 Warsaw Poland

Challenges connected with neuroscience and the use of machine learning to support analytical processes encompass more and more areas, thus supporting practitioners and managerial decisions. These changes can also be seen in the area of human resource management and support for decisions on key future spending on the remuneration of future employees. The article presents an original spatial data enrichment and spatial data mining methodology used for the analysis of primary data based on a sample of 1149 young candidates from generation Z to measure the effectiveness of data mining learning methods. The studies used data collected directly from surveys that were "enriched" with spatial geolocation. The fact that the spatial context was taken into account in the studies made it possible to develop a model explaining the spatio-temporal differentiation of professional expectations of respondents from generation Z who were studying professions connected with broadly understood IT. The analyzes used modeling with linear polynomial regression, the neural network of a multi-layer perceptron type and the multivariate adaptive regression splines method in the variant with and without spatial data filtration. The use of different spatial data mining methods made it possible to compare the reliability of models of knowledge extraction from the data and to explain the significance of individual factors which affected the respondents' beliefs. The analysis shows that spatial filtering of the data generates twice lower mean squared error while effective application of machine learning methods requires the use of explanatory spatial data.

关键词： Human resources Modeling spatial data mining Multivariate adaptive regression splines Artificial neutral networks Geolocalization

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Recognition of Critical Built-Up Areas Located on High Hill-Slope Regions using spatial data mining Technique 6

Recognition of Critical Built-Up Areas Located on High Hill-...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Kodge, B.G. School of Science Department of Computer Science GITAM University Hyderabad India

ISBN: (纸本)9798350306613

In mountainous places, structures are being built for residential or commercial uses without the necessary safety precautions. Every year, landslides, torrential downpours, severe snowfall, earthquakes, volcanic eruptions, and floods cause buildings to collapse. The bulk of them are found in high-hill slope areas with loose soil types, close to river flows and other sorts of water sources. Therefore, these incidents have claimed thousands of lives. This paper deals with the process of automatic identification of critical buildings (residential/commercial) located in mountainous area which are on high-hill-slope, close to river flows, having loose soil type and high variations in land elevation contours. This study use the primary data like, built-up/residential area and water body areas which are extracted from sample LULC (Land Use and Land Cover) using image classification techniques, and another important data like slope map and land elevation contour maps which are generated from DEM (Digital Elevation Model). In addition, the supplementary data like, river maps, soil maps and other base maps, are also collected. All the data are integrated and taken into consideration for the identification and extraction of critical residential/build-up areas using spatial data mining technique. © 2024 IEEE.

关键词： Critical residential area identification DEM Image Segmentation LUCL spatial data mining

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Application of spatial data mining to national mines inventories for exploration and land planning of high place-value mineral resources. The case of aggregates in Spain

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RESOURCES POLICY 2022年 79卷

作者： Lopez-Acevedo, F. J. Escavy, J. I. Herrero, M. J. Univ Complutense Madrid Fac Ciencias Geol Calle Jose Antonio Novais 12 Madrid 28040 Spain Univ Politecn Madrid ETSI Caminos Canales & Puertos Calle Prof Aranguren S-N Madrid 28040 Spain

Countries' mine inventories usually include information related to exploited rock type or mineral, amount of resources, owner, location, mining rights associated, etc. All this information is a vast dataset that in the last couple of decades has been included in digital cartography in official websites, some of them freely available. This information is usually exploited as individual registers classified by substance, location, etc., but when analyzed as a whole and combined with other economic, social, and environmental variables, valuable infor-mation can be obtained. If the variable time is included in the study, these databases can reveal the evolution of mining activity in recent decades. This study analyzes the distribution of aggregates quarries in Spain, as a case study, relating their location to different socio-geographical features such as urban areas, environmentally protected areas, or population density, by means of spatial data mining (SDM) tools. Aggregates are used as construction materials and are the largest non-energy extractive industry, being the second most consumed product by human beings after water. Due to the low unit value and high volume of consumption, aggregates are considered a high place-value commodity. By combining the information of aggregates in the Spanish mines inventory with geological, so-cial, demographic, environmental, and administrative information, and using SDM techniques, relevant con-clusions can be obtained such as the delineation of areas in the land use planning to be dedicated to aggregates exploitation, or the identification of priority zones for exploration for mining companies. This information could serve policy makers and land use planners in making data-driven decisions and mining companies in determining areas with the highest profit potential to develop their operations.

关键词： spatial data mining Geographical information systems Aggregates Quarries spatial analysis

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Knowledge discovery of Middle East dust sources using Apriori spatial data mining algorithm

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ECOLOGICAL INFORMATICS 2022年 72卷

作者： Papi, Ramin Attarchi, Sara Boloorani, Ali Darvishi Samany, Najmeh Neysani Univ Tehran Fac Geog Dept Remote Sensing & GIS Tehran Iran Natl Cartog Ctr NCC Tehran Iran

Identifying the areas susceptible to dust storm formation is one effective way of dealing with this destructive environmental phenomenon. This study is the first attempt to employ the Apriori spatial data mining algorithm to dust source susceptibility mapping (DSSM). The research process was based on extracting association rules between spatial-temporal patterns of dust drivers (including soil, vegetation, and climate parameters) in the Middle East's hotspots dust sources (HDSs). For this purpose, HDSs were identified using visual interpretation of sub-daily MODIS-Terra/Aqua RGB images from 2000 to 2021. The Middle East's HDSs mainly correspond to desert areas with poor vegetation cover and ephemeral/dried-up water bodies. A total of three million rules were extracted by running the Apriori algorithm. Accordingly, bare and non-vegetated lands, high soil thickness, low soil moisture, very high wind speed, and high temperature were identified as the most common features of HDSs. Using three measures including support, confidence, and lift, 54 frequent, reliable, and logical rules were selected, and the related maps were generated. Then, the susceptible dust sources (SDSs) map of the Middle East was produced in five classes of extreme (13% of the areas), high (14%), moderate (16%), low (17%), and no (40%) susceptibility through the weighted linear combination of the rule maps. The accuracy of the identified SDSs was estimated at 83.7% using the verification points. A sensitivity analysis was performed using the leave -one-out method to determine the isolated effect of the selected rules on the produced SDSs map. The model uncertainty varied between 15.7% and 16.8% for different rules. The variation range of uncertainty was 1.1%, demonstrating that a single rule does not significantly affect the model's performance;however, some rules have a more influential role. Our results revealed that Apriori's ability to provide generalizable association rules is a robust algorith

关键词： Dust source susceptibility mapping spatial data mining Apriori Remote sensing Middle East

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data Optimization for spatial data mining and Classification in Marine Geochemical Exploration 11th

Data Optimization for Spatial Data Mining and Classification...

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11th International Conference on Modelling, Identification and Control (ICMIC)

作者： Wang, Haihong Liu, Li Wang, Jingjing Gao, Yanming Qingdao Univ Sci & Technol Coll Informat Sci & Technol Qingdao 266061 Peoples R China State Ocean Adm Key Lab Marine Ecol Environm & Disaster Prevent & North China Sea Branch Qingdao 266033 Peoples R China

ISBN: (纸本)9789811504747;9789811504730

In this paper, spatial big datamining is presented based on a new simplified data optimization and minimum bounding rectangle (MBR) methods. In order to speed up the spatial query and reduce the setup time of spatial index in marine geochemical exploration spatial database, a two-step data mining of objects method has been used. The spatial join queries such as the spatial relations between the objects can be transferred into the relative distance problem of points with their spatial characteristics. Then, support vector machines (SVMs) based on radial basis function (RBF) kernel classification have been used to those points and lines to get spatial targeting areas in order to evaluate the oil and gas resources. Comparison simulations of precision and spatial query speed of the method proposed have been conducted based on ArcGIS and Hadoop to verify the effectivness.

关键词： data optimization spatial data mining Classification MBR

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Parallel and distributed clustering framework for big spatial data mining

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2019年第6期34卷 671-689页

作者： Bendechache, Malika Tari, A-Kamel Kechadi, M-Tahar Univ Coll Dublin Insight Ctr Data Analyt Dublin Ireland Univ A Mira Bejaia Bejaia Algeria

Clustering techniques are very attractive for identifying and extracting patterns of interests from datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality, heterogeneity, and high complexity of some algorithms. Distributed clustering techniques constitute a very good alternative to the Big data challenges (e.g., Volume, Variety, Veracity, and Velocity). In this paper, we developed and implemented a Dynamic Parallel and Distributed clustering (DPDC) approach that can analyse Big data within a reasonable response time and produce accurate results, by using existing and current computing and storage infrastructure, such as cloud computing. The DPDC approach consists of two phases. The first phase is fully parallel and it generates local clusters and the second phase aggregates the local results to obtain global clusters. The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in time and memory allocation. DPDC was thoroughly tested and compared to well-known clustering algorithms BIRCH and CURE. The results show that the approach not only produces high-quality results but also scales up very well by taking advantage of the Hadoop MapReduce paradigm or any distributed system. [GRAPHICS]

关键词： Big data MapReduce Hadoop spatial data mining clustering distributed clustering parallel clustering DBSCAN dynamic K-means

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Explorative spatial data mining for Energy Technology Adoption and Policy Design Analysis 19th

Explorative Spatial Data Mining for Energy Technology Adopti...

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19th EPIA Conference on Artificial Intelligence (EPIA)

作者： Heymann, Fabian Soares, Filipe Joel Duenas, Pablo Miranda, Vladimiro Univ Porto Fac Engn Porto Portugal INESC TEC Porto Portugal MIT 40 Ames St Cambridge MA 02142 USA

ISBN: (纸本)9783030302412;9783030302405

spatial data mining aims at the discovery of unknown, useful patterns from large spatial datasets. This article presents a thorough analysis of the Portuguese adopters of distributed energy resources using explorative spatial data mining techniques. These resources are currently passing the early adoption stage in the study area. Results show adopter clustering during the current stage. Furthermore, spatial adoption patterns are simulated over a 20-year horizon, analyzing technology concentration changes over time while comparing three different energy policy designs. Outcomes provide useful indication for both electrical network planning and energy policy design.

关键词： Diffusion of innovation Renewable energy spatial data mining

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