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    Wyświetlanie 1-7 z 7
    Tytuł:
    Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms
    Autorzy:
    Nandika, Muhammad Rizki
    Ulfa, Azura
    Ibrahim, Andi
    Purwanto, Anang Dwi
    Powiązania:
    https://bibliotekanauki.pl/articles/8413878.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    accuracy
    coral
    seagrass
    Maximum Likelihood
    Minimum Distance
    Support Vector Machine
    remote sensing
    Opis:
    Remote sensing technology is reliable in identifying the distribution of seabed cover yet there are still challenges in retrieving the data collection of shallow water habitats than with other objects on land. Classification algorithms based on remote sensing technology have been developed for application to map benthic habitats, such as Maximum Likelihood, Minimum Distance, and Support Vector Machine. This study focuses on examining those three classification algorithms to retrieve information on the benthic habitat in Pari Island, Jakarta using visual interpretation data for classification, and data field measurements for accuracy testing. This study used five classes of benthic objects, namely sand, sand-seagrass, rubble, seagrass, and coral. The results show how the proposed approach in this study provides an overall good classification of marine habitat with an accuracy produced 63.89–81.95%. The Support Vector Machine algorithm produced the highest accuracy rate of about 81.95%. The Support Vector Machine algorithm at a very high spatial resolution is considered to be capable of identifying, monitoring, and performing the rapid assessment of benthic habitat objects.
    Źródło:
    Geomatics and Environmental Engineering; 2023, 17, 2; 69--87
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq
    Autorzy:
    Alwan, Imzahim A.
    Aziz, Nadia A.
    Powiązania:
    https://bibliotekanauki.pl/articles/1838006.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    land cover mapping
    Sentinel 2
    supervised classification
    maximum likelihood
    Support Vector Machine (SVM)
    confusion matrix
    Opis:
    Land cover mapping of marshland areas from satellite images data is not a simple process, due to the similarity of the spectral characteristics of the land cover. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Sentinel 2B by ESA (European Space Agency) were used to classify the land cover of Al Hawizeh marsh/Iraq Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B images provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.
    Źródło:
    Geomatics and Environmental Engineering; 2021, 15, 1; 5-21
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo
    Autorzy:
    Taha, Lamyaa Gamal El-deen
    Ibrahim, Rania Elsayed
    Powiązania:
    https://bibliotekanauki.pl/articles/2055780.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    multi-source image fusion
    random forest
    support vector machine
    DEM extraction
    unplanned unsafe areas
    remote sensing
    Opis:
    Building detection in Ashwa’iyyat is a fundamental yet challenging problem, mainly because it requires the correct recovery of building footprints from images with high-object density and scene complexity. A classification model was proposed to integrate spectral, height and textural features. It was developed for the automatic detection of the rectangular, irregular structure and quite small size buildings or buildings which are close to each other but not adjoined. It is intended to improve the precision with which buildings are classified using scikit learn Python libraries and QGIS. WorldView-2 and Spot-5 imagery were combined using three image fusion techniques. The Grey-Level Co-occurrence Matrix was applied to determine which attributes are important in detecting and extracting buildings. The Normalized Digital Surface Model was also generated with 0.5-m resolution. The results demonstrated that when textural features of colour images were introduced as classifier input, the overall accuracy was improved in most cases. The results show that the proposed model was more accurate and efficient than the state-of-the-art methods and can be used effectively to extract the boundaries of small size buildings. The use of a classifier ensample is recommended for the extraction of buildings.
    Źródło:
    Geomatics and Environmental Engineering; 2022, 16, 2; 39--58
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming
    Autorzy:
    Arif, Salah
    Djellal, Adel
    Djebbari, Nawel
    Belhaoues, Saber
    Touati, Hassen
    Guellati, Fatma Zohra
    Bensouilah, Mourad
    Powiązania:
    https://bibliotekanauki.pl/articles/2174666.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    microcystis cell density
    Multiple Linear Regression
    Support Vector Machine
    Particle Swarm Optimization
    Genetic Algorithm
    Bird Swarm Algorithm
    Opis:
    Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.
    Źródło:
    Geomatics and Environmental Engineering; 2023, 17, 2; 31--68
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Computational intelligence methods in the problem of modelling technical wear of buildings in mining areas
    Metody inteligencji obliczeniowej w problemie modelowania stopnia zużycia technicznego budynków na terenach górniczych
    Autorzy:
    Rusek, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/385956.pdf
    Data publikacji:
    2012
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    technical wear
    neural networks
    support vector machine (SVM)
    fuzzy systems
    szkody górnicze
    zużycie techniczne
    sieci neuronowe
    systemy rozmyte
    Opis:
    In the work presented approach with a view to building the model of degree of technical wear of buildings in the mining areas, as well as an indication that the contribution of the consumption on technical factors interact mining and civil construction origin. Set out criteria for the selection and research methodology effects are synthetically summarised existing work in this field. Justified choice of the ϵ-SVR method confronting its advantages to the characteristics of typical neural network.
    W artykule zaprezentowano podejście mające na celu budowę modelu przebiegu stopnia zużycia technicznego budynków na terenach górniczych, jak również analizowano, w jakim stopniu na zużycie techniczne oddziałują czynniki górnicze oraz ogólnobudowlane. Przedstawiono kryteria doboru metodyki badań oraz podsumowano efekty dotychczasowych prac w tej dziedzinie. Uzasadniono wybór metody &vepsilon;-SVR, konfrontując jej zalety z własnościami typowych, jednokierunkowych sieci neuronowych. Opisano sposób optymalnego doboru parametrów charakteryzujących złożoność modelu ϵ-SVR oraz wskazano możliwość zastosowania tak utworzonego modelu w systemach ekspertowych.
    Źródło:
    Geomatics and Environmental Engineering; 2012, 6, 3; 83-91
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Machine-Learning Methods for Assessing Dynamic Resistance of Existing Bridge Structures Subjected to Mining Tremors
    Metody uczenia maszynowego w ocenie odporności dynamicznej istniejących obiektów mostowych poddanych wstrząsom górniczym
    Autorzy:
    Rusek, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/385657.pdf
    Data publikacji:
    2018
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    dynamika budowli
    uczenie maszynowe
    sztuczne sieci neuronowe
    SVM
    wstrząsy górnicze
    odporność dynamiczna
    mosty
    dynamics of structures
    machine learning
    Artificial Neural Networks
    SVM Support Vector Machine
    mining tremors
    dynamic resistance
    bridges
    Opis:
    W pracy przedstawiono wyniki badań, których celem było utworzenie modelu pozwalającego na określenie odporności istniejących obiektów mostowych na wpływy wstrząsów górniczych. Podstawą do analiz była utworzona przez autora baza danych o odporności dynamicznej żelbetowych obiektów mostowych poddanych wymuszeniu sejsmicznemu charakterystycznemu dla terenu Legnicko-Głogowskiego Okręgu Miedziowego (LGOM). Odporność dynamiczna każdego obiektu w bazie danych została wyrażona w postaci granicznych wartości przyspieszeń drgań gruntu, jakie dana konstrukcja może przejąć bez zagrożenia bezpieczeństwa. Badania przeprowadzono, wykorzystując metodę Support Vector Machine (SVM) w ujęciu regresyjnym (SVR – Support Vector Regression) oraz sztuczne sieci neuronowe (ANN – Artificial Neural Network). Utworzone w ten sposób modele porównano w aspekcie jakości predykcji oraz uogólniania nabytej wiedzy. Pozwoliło to na wytypowanie metody najbardziej efektywnej pod względem oceny odporności dynamicznej istniejących obiektów mostów.
    This paper demonstrates the results of research studies aimed at creating a model that allows to determine the resistance of existing bridge structures to the impact of mining tremors. A database (created by the author of this article) of the dynamic resistance of reinforced concrete bridge structures subjected to seismic excitations commonly occurring in the Legnica-Głogów Copper District (LGOM) formed the basis for the analysis. The dynamic resistance of each structure contained in the database was expressed as the limit values of the acceleration of ground vibrations that may be carried by a given structure without compromising its safety. The study was carried out using the Support Vector Machine (SVM) method in a Support Vector Regression (SVR) approach as well as an Artificial Neural Network (ANN). The models were compared in terms of the quality of the predictions and generalization of the acquired knowledge. This allows to select the most-effective method in evaluating the dynamic resistance of existing bridge structures.
    Źródło:
    Geomatics and Environmental Engineering; 2018, 12, 1; 109-120
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images
    Autorzy:
    Taha, Lamyaa Gamal El-deen
    Ibrahim, Rania Elsayed
    Powiązania:
    https://bibliotekanauki.pl/articles/1837996.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    ensemble classifiers
    machine learning
    random forest
    maximum likelihood
    support vector machines
    backpropagation
    image classification
    Opis:
    The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery. The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.
    Źródło:
    Geomatics and Environmental Engineering; 2021, 15, 4; 101-116
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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