In recent years, ensemble learning methods have become popular in landslide susceptibility mapping (LSM) with varying degrees of success. Within classifier concept, decision tree based learners such as random forest (RF) (i.e. forest) and rotation (RotFor) gained a great interest due to their robustness against conventional statistical methods. This study proposes canonical correlation (CCF), new member family, the prediction for Yenice district Karabuk Turkey. To test suitability CCF...

Landslide susceptibility mapping (LSM) is a major area of interest within the field disaster risk management that involves planning and decision-making activities. Therefore, preparation dataset, construction predictive model analysis results are considered to be important stages for effective efficient in LSM. In recent years, large number studies has mainly focused on effects using machine learning (ML) algorithms as Decision tree-based ensemble known decision forest one popular ML...

Machine learning algorithms reported to be robust and superior the conventional parametric classifiers have been recently employed in object-based classification. Within these algorithms, ensemble methods that construct set of individual combining their predictions make final decision about unlabelled data successfully applied. In this study, performance effectiveness a novel algorithm, rotation forest (RotFor) aiming build diverse accurate classifiers, was investigated for first time...

Increasing the accuracy of thematic maps produced through process image classification has been a hot topic in remote sensing. For this aim, various strategies, classifiers, improvements, and their combinations have suggested literature. Ensembles that combine prediction individual classifiers with weights based on estimated accuracies are strategies aiming to improve classifier performances. One recently introduced ensembles is rotation forest, which idea building accurate diverse by...

Logistic model tree (LMT), a new method integrating standard decision (DT) induction and linear logistic regression algorithm in single tree, have been recently proposed as an alternative to DT-based learning algorithms. In this study, the LMT was applied context of pixel- object-based classifications using high-resolution WorldView-2 imagery, its performance compared with C4.5, random forest Adaboost. Results study showed that generally produced more accurate classification results than...

Classifying spectrally similar crop types in fragmented landscapes is a difficult task due to the low spectral and spatial resolution of satellite imagery. The objective this study twofold: (I) evaluate performance recent ensemble methodology, namely canonical correlation forest (CCF), (ii) investigate potential recently launched Sentinel-2 (S-2) image for classification. algorithm based on building forest-type model using multiple trees. Its was compared widely-used random (RF) rotation...

ABSTRACTABSTRACT Marine mucilage that threatens marine habitats is one of the natural disasters, mainly resulting from global warming and pollution. Monitoring sea surface formations mapping their spatial distributions provide valuable information to local authorities decision-makers in developing prevention rehabilitation strategies. This study proposes a new spectral index called Automated Mucilage Extraction Index (AMEI) allows effective accurate detection aggregates using Sentinel-2...

Ayçiçeği, ülkemiz için önemli bir yağlı tohum kaynağı olup, büyük ve kendine özgü çiçek tablasıyla diğer tarımsal bitkilerden ayrılır. Ayçiçeği tablasının doğru şekilde tespit edilmesi, verim tahmini sürdürülebilir üretim planlaması açısından çok önemlidir. Bu çalışmanın temel amacı, derin öğrenme tabanlı Mask R-CNN modelinin RGB multispektral İHA ortomozaiklerinden ayçiçeği tablasını etme performansının değerlendirilmesidir. amaçla, Sakarya'nın Arifiye ilçesindeki çalışma alanı üzerinde...

The classification of very high-resolution satellite imagery remains a focal point in remote sensing, attracting increased attention across diverse scientific disciplines. Various methods, including pixel- and object-based techniques, have been proposed, their performances limitations discussed the literature. This paper presents hybrid method that combines strengths methods image to minimize errors associated with segmentation process, particularly under-segmentation analysis. core concept...

The main purpose of this study is to propose an interoperable land valuation data model for residential properties as extension the national geographic infrastructure (GDI) and make mass process applicable with use machine learning approach. As example, random forest (RF) ensemble algorithm was implemented in Pendik district Istanbul evaluate prediction performance by using thematic datasets compatible model. This provides a methodology various urban applications robustness increases real...

Global warming threatens ecosystems through rising temperatures, increasing sea levels, drought, and extreme weather conditions. The natural balance of seas oceans is also at stake with recent outbreaks mucilage events all over the world. phenomenon, which has been frequently observed in Adriatic Tyrrhenian seas, taken place second time Sea Marmara Spring 2021. dividing Asian European parts Turkey an important inland heavy maritime traffic, hosting many industrial zones surrounded by highly...

Lately, unmanned aerial vehicle (UAV) become a prominent technology in remote sensing studies with the advantage of high-resolution, low-cost, rapidly and periodically achievable three-dimensional (3D) data. UAV enables data capturing different flight altitudes, imaging geometries, viewing angles which make detailed monitoring modelling target objects possible. Against earlier times, UAVs have been improved by integrating real-time kinematic (RTK) positioning multispectral (MS) equipment. In...