Browsing by Subject "random forest"

Now showing 1 - 8 of 8

Access status: Open Access ,
A machine learning model for improving building detection in informal areas - a case study of Greater Cairo
(Wydawnictwa AGH, 2022) Taha, Lamyaa Gamal El-deen; Ibrahim, Rania Elsayed
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.
Access status: Open Access ,
Assessment of approaches for the extraction of building footprints from pléiades images
(Wydawnictwa AGH, 2021) Taha, Lamyaa Gamal El-deen; Ibrahim, Rania Elsayed
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.
Access status: Open Access ,
Comparison of machine-learning algorithms for SPOT 7 multispectral image classification
(Wydawnictwa AGH, 2025) Morale, Davide; Parente, Claudio; Bolognesi, Salvatore Falanga
Precise and timely land-cover identification plays an important role in effective environmental monitoring and land management. This study compares the performance of five machine-learning classifiers – support vector machine (SVM), decision tree (DT), normal Bayes (NB), random forest (RF), and k-nearest neighbor (k-NN) – in the land-cover mapping of the Agro Nocerino Sarnese area (Southern Italy) using high-resolution SPOT 7 pan-sharpened multispectral images with a pixel size of 1.5 m × 1.5 m. The data set consisted of blue, green, red, and near-infrared (NIR) bands and was processed with Orfeo ToolBox (OTB) software. Two data sets were analyzed: DS-3B (which included only the visible bands [blue, green, and red]), and DS-4B (which also included the NIR band). A comparison of the classifiers’ performances across various land-cover classes was conducted in order to assess their respective classification accuracy. The results showed that SVM and k-NN achieved the highest overall accuracy levels (93% and 92%, respectively) using only the visible bands, whereas the decision tree classifier performed best when the NIR band was included. Random forest achieved excellent accuracy in vegetation classes (88–99%) but struggled with misclassifications in bare soil and man-made classes such as buildings and roads. These results emphasized the significant impact of data set characteristics on classifier performance as well as the importance of band selection and pan-sharpening techniques in high-resolution land-cover mapping.
Access status: Open Access ,
Comparison of Statistical and Machine-Learning Model for Analyzing Landslide Susceptibility in Sumedang Area, Indonesia
(Wydawnictwa AGH, 2024) Fitriana, Hana Listi; Ismanto, Rido Dwi; Tulus, Jessica Stephanie; Julzarika, Atriyon; Nugroho, Jalu Tejo; Manalu, Johannes
Landslides have produced several recurrent dangers, including losses of life and property, losses of agricultural land, erosion, population relocation, and others. Landslide mitigation is critical since population and economic expansion are rapidly followed by significant infrastructure development, increasing the risk of catastrophes. At an early stage in landslide-disaster mitigation, landslide-risk mapping must give critical information to help policies limit the potential for landslide damage. This study will utilize the comparative frequency ratio (FR) and random forest (RF) techniques, they will be utilized to properly investigate the distribution of flood vulnerability in the Sumedang area. This study has identified 12 criteria for developing a landslide-susceptibility model in the research region based on the features of past disasters in the research area. The FR and RF models scored 88 and 81% of the AUC value, respectively. Based on the McNemar test, the FR and RF models featured the same performance in determining the landslide-vulnerability level performances in Sumedang. They performed well in assessing landslides in the research region, therefore, they may be used as references in landslide prevention and references in future regional development plans by the stakeholders.
Access status: Open Access ,
Development of flood-hazard-mapping model using random forest and frequency ratio in Sumedang Regency, West Java, Indonesia
(Wydawnictwa AGH, 2023) Ismanto, Rido Dwi; Fitriana, Hana Listi; Manalu, Johanes; Purboyo, Alvian Aji; Prasasti, Indah
Flooding, often triggered by heavy rainfall, is a common natural disaster in Indonesia, and is the third most common type of disaster in Sumedang Regency. Hence, flood-susceptibility mapping is essential for flood management. The primary challenge in this lies in the complex, non-linear relationships between indices and risk levels. To address this, the application of random forest (RF) and frequency ratio (FR) methods has been explored. Ten flood-conditioning factors were determined from the references: the distance from a river, elevation, geology, geomorphology, lithology, land use/land cover, rainfall, slope, soil type, and topographic wetness index (TWI). The 35 flood locations from the flood-inventory map were selected, and the remaining 18 flood locations were used for justifying the outcomes. The flooded areas from the RF model were 28.39%, the rest (71.61%) were non-flooded areas. Also, the flooded areas from the FR method were 8.02%, and the non-flooded areas were 91.98%. The AUC for both methods was a similar value – 83.0%. This result is quite accurate and can be used by policymakers to prevent and manage future flooding in the Sumedang area. These results can also be used as materials for updating existing flood-susceptibility maps.
Access status: Open Access ,
Multi-Perspective Evaluation of Urban Green Views: Spatial and Street-View Data Integration in Sudirman Central Business District, Indonesia
(Wydawnictwa AGH, 2025) Pradana, Mohammad Raditia; Wibowo, Adi; Semedi, Jarot Mulyo
Urban green spaces (UGSs) are critical for enhancing urban livability and sustainability by providing both ecological and human-centered benefits. This study integrates spatial landscape metrics and the street-level visibility of greenery (measured through the green view index [GVI]) in order to evaluate the structural and visual characteristics of UGSs in a dynamic urban area – specifically, the Sudirman Central Business District (SCBD) of Jakarta, Indonesia. The analysis focuses on examining the roles of landscape metrics such as area, perimeter, compactness, shape index, and elongation in influencing the GVI and its spatial variability across different types of urban green spaces (including parks, green corridors, and open spaces). The results indicated that larger and more compact UGSs significantly contributed to higher GVI levels (thus, reflecting better visual greenery), while elongated and fragmented green spaces exhibited greater variability and lower visibility. Non-linear relationships (assessed through random forest regression and SHAP analysis) further revealed the complex interactions between GVI and landscape metrics, thus emphasizing the importance of incorporating advanced statistical approaches. The limitations that are related to data quality, temporal coverage, and spatial heterogeneity are also discussed, thus highlighting opportunities for future research for addressing these challenges through multi-temporal analyses and spatially explicit models. By bridging the gap between the spatial configurations and visual perception of UGSs, this study contributes to sustainable urban-planning strategies that are aimed at optimizing green spaces for ecological functionality and human well-being.
Access status: Open Access ,
Optchain: an advanced optimization method for enhancing IoT Data security via blockchain
(Wydawnictwa AGH, 2025) Kokate, Shatakshi; Shrawankar, Urmila
The increased use of IoT devices in various domains generates abundant data traffic. Securing this data during its transfer and storage is essential. Blockchain is now a trending technology to provide security to the data; however, it is observed that blockchain performs poorly while managing large volume data. To mitigate this issue, an advanced Optchain method to reduce the data size before submitting it to the blockchain network is discussed in this paper. This Optchain method optimizes IoT-generated data using data-classification and compression techniques. The classification of data as relevant or irrelevant is based on predefined thresholds of critical healthcare parameters. Subsequently, the Optchain method employs the Z-standard algorithm for compressing only the relevant data, ensuring efficient storage and faster blockchain transactions. Simulation results using the iFogSim simulator and Ethereum blockchain demonstrated improved storage costs and computational times compared to traditional methods.
Access status: Open Access ,
Sparse data classifier based on first-past-the-post voting system
(Wydawnictwa AGH, 2022) Cudak, Magdalena; Piech, Mateusz; Marcjan, Robert
A point of interest (POI) is a general term for objects that describe places from the real world. The concept of POI matching (i.e., determining whether two sets of attributes represent the same location) is not a trivial challenge due to the large variety of data sources. The representations of POIs may vary depending on the basis of how they are stored. A manual comparison of objects is not achievable in real time, therefore, there are multiple solutions for automatic merging. However, there is no yet the efficient solution solves the missing of the attributes. In this paper, we propose a multi-layered hybrid classifier that is composed of machine-learning and deep-learning techniques and supported by a first-past-the-post voting system. We examined different weights for the constituencies that were taken into consideration during a majority (or supermajority) decision. As a result, we achieved slightly higher accuracy than the best current model (random forest), which also is based on voting.