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Mr. Amin Naboureh
University of Chinese Academy of Sciences, 100049; Beijing, China

Basic Info


Research Keywords & Expertise

0 Machine Learning
0 Spatial Analysis
0 Remote Sensing and Gis
0 Land Cover
0 Land Use and Land Cover Change

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Short Biography

I received the M.Sc. degree in geographic information system and remote sensing field from the University of Tabriz, Iran, in 2015. I am currently a PhD student in Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China. My research interests include data mining, machine learning , image classification, land use/cover mapping. I have received scholarship award of CAS-TWAS president’s fellowship programme in 2018.

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Review
Published: 01 December 2020 in Big Earth Data
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Large-scale projects, such as the construction of railways and highways, usually cause an extensive Land Use Land Cover Change (LULCC). The China-Central Asia-West Asia Economic Corridor (CCAWAEC), one key large-scale project of the Belt and Road Initiative (BRI), covers a region that is home to more than 1.6 billion people. Although numerous studies have been conducted on strategies and the economic potential of the Economic Corridor, reviewing LULCC mapping studies in this area has not been studied. This study provides a comprehensive review of the recent research progress and discusses the challenges in LULCC monitoring and driving factors identifying in the study area. The review will be helpful for the decision-making of sustainable development and construction in the Economic Corridor. To this end, 350 peer-reviewed journal and conference papers, as well as book chapters were analyzed based on 17 attributes, such as main driving factors of LULCC, data collection methods, classification algorithms, and accuracy assessment methods. It was observed that: (1) rapid urbanization, industrialization, population growth, and climate change have been recognized as major causes of LULCC in the study area; (2) LULCC has, directly and indirectly, caused several environmental issues, such as biodiversity loss, air pollution, water pollution, desertification, and land degradation; (3) there is a lack of well-annotated national land use data in the region; (4) there is a lack of reliable training and reference datasets to accurately study the long-term LULCC in most parts of the study area; and (5) several technical issues still require more attention from the scientific community. Finally, several recommendations were proposed to address the identified issues.

ACS Style

Amin Naboureh; Jinhu Bian; Guangbin Lei; Ainong Li. A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries. Big Earth Data 2020, 5, 237 -257.

AMA Style

Amin Naboureh, Jinhu Bian, Guangbin Lei, Ainong Li. A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries. Big Earth Data. 2020; 5 (2):237-257.

Chicago/Turabian Style

Amin Naboureh; Jinhu Bian; Guangbin Lei; Ainong Li. 2020. "A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries." Big Earth Data 5, no. 2: 237-257.

Journal article
Published: 23 October 2020 in Remote Sensing
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Timely and accurate Land Cover (LC) information is required for various applications, such as climate change analysis and sustainable development. Although machine learning algorithms are most likely successful in LC mapping tasks, the class imbalance problem is known as a common challenge in this regard. This problem occurs during the training phase and reduces classification accuracy for infrequent and rare LC classes. To address this issue, this study proposes a new method by integrating random under-sampling of majority classes and an ensemble of Support Vector Machines, namely Random Under-sampling Ensemble of Support Vector Machines (RUESVMs). The performance of RUESVMs for LC classification was evaluated in Google Earth Engine (GEE) over two different case studies using Sentinel-2 time-series data and five well-known spectral indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The performance of RUESVMs was also compared with the traditional SVM and combination of SVM with three benchmark data balancing techniques namely the Random Over-Sampling (ROS), Random Under-Sampling (RUS), and Synthetic Minority Over-sampling Technique (SMOTE). It was observed that the proposed method considerably improved the accuracy of LC classification, especially for the minority classes. After adopting RUESVMs, the overall accuracy of the generated LC map increased by approximately 4.95 percentage points, and this amount for the geometric mean of producer’s accuracies was almost 3.75 percentage points, in comparison to the most accurate data balancing method (i.e., SVM-SMOTE). Regarding the geometric mean of users’ accuracies, RUESVMs also outperformed the SVM-SMOTE method with an average increase of 6.45 percentage points.

ACS Style

Amin Naboureh; Hamid Ebrahimy; Mohsen Azadbakht; Jinhu Bian; Meisam Amani. RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine. Remote Sensing 2020, 12, 3484 .

AMA Style

Amin Naboureh, Hamid Ebrahimy, Mohsen Azadbakht, Jinhu Bian, Meisam Amani. RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine. Remote Sensing. 2020; 12 (21):3484.

Chicago/Turabian Style

Amin Naboureh; Hamid Ebrahimy; Mohsen Azadbakht; Jinhu Bian; Meisam Amani. 2020. "RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine." Remote Sensing 12, no. 21: 3484.

Journal article
Published: 11 October 2020 in Remote Sensing
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Distribution of Land Cover (LC) classes is mostly imbalanced with some majority LC classes dominating against minority classes in mountainous areas. Although standard Machine Learning (ML) classifiers can achieve high accuracies for majority classes, they largely fail to provide reasonable accuracies for minority classes. This is mainly due to the class imbalance problem. In this study, a hybrid data balancing method, called the Partial Random Over-Sampling and Random Under-Sampling (PROSRUS), was proposed to resolve the class imbalance issue. Unlike most data balancing techniques which seek to fully balance datasets, PROSRUS uses a partial balancing approach with hundreds of fractions for majority and minority classes to balance datasets. For this, time-series of Landsat-8 and SRTM topographic data along with various spectral indices and topographic data were used over three mountainous sites within the Google Earth Engine (GEE) cloud platform. It was observed that PROSRUS had better performance than several other balancing methods and increased the accuracy of minority classes without a reduction in overall classification accuracy. Furthermore, adopting complementary information, particularly topographic data, considerably increased the accuracy of minority classes in mountainous areas. Finally, the obtained results from PROSRUS indicated that every imbalanced dataset requires a specific fraction(s) for addressing the class imbalance problem, because different datasets contain various characteristics.

ACS Style

Amin Naboureh; Ainong Li; Jinhu Bian; Guangbin Lei; Meisam Amani. A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions. Remote Sensing 2020, 12, 3301 .

AMA Style

Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Meisam Amani. A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions. Remote Sensing. 2020; 12 (20):3301.

Chicago/Turabian Style

Amin Naboureh; Ainong Li; Jinhu Bian; Guangbin Lei; Meisam Amani. 2020. "A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions." Remote Sensing 12, no. 20: 3301.

Journal article
Published: 16 June 2020 in ISPRS International Journal of Geo-Information
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Landslides are one of the most detrimental geological disasters that intimidate human lives along with severe damages to infrastructures and they mostly occur in the mountainous regions across the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potential areas that are prone to landslides and could have an impact on decreasing the possible damages. The application of the fuzzy best-worst multi-criteria decision-making (FBWM) method was applied for LSM in Austria. Further, the role of employing a few numbers of pairwise comparisons on LSM was investigated by comparing the FBWM and Fuzzy Analytical Hierarchical Process (FAHP). For this study, a wide range of data was sourced from the Geological Survey of Austria, the Austrian Land Information System, Humanitarian OpenStreetMap Team, and remotely sensed data were collected. We used nine conditioning factors that were based on the previous studies and geomorphological characteristics of Austria, such as elevation, slope, slope aspect, lithology, rainfall, land cover, distance to drainage, distance to roads, and distance to faults. Based on the evaluation of experts, the slope conditioning factor was chosen as the best criterion (highest impact on LSM) and the distance to roads was considered as the worst criterion (lowest impact on LSM). LSM was generated for the region based on the best and worst criterion. The findings show the robustness of FBWM in landslide susceptibility mapping. Additionally, using fewer pairwise comparisons revealed that the FBWM can obtain higher accuracy as compared to FAHP. The finding of this research can help authorities and decision-makers to provide effective strategies and plans for landslide prevention and mitigation at the national level.

ACS Style

Meisam Moharrami; Amin Naboureh; Thimmaiah Gudiyangada Nachappa; Omid Ghorbanzadeh; Xudong Guan; Thomas Blaschke. National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making. ISPRS International Journal of Geo-Information 2020, 9, 393 .

AMA Style

Meisam Moharrami, Amin Naboureh, Thimmaiah Gudiyangada Nachappa, Omid Ghorbanzadeh, Xudong Guan, Thomas Blaschke. National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making. ISPRS International Journal of Geo-Information. 2020; 9 (6):393.

Chicago/Turabian Style

Meisam Moharrami; Amin Naboureh; Thimmaiah Gudiyangada Nachappa; Omid Ghorbanzadeh; Xudong Guan; Thomas Blaschke. 2020. "National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making." ISPRS International Journal of Geo-Information 9, no. 6: 393.

Journal article
Published: 25 August 2019 in ISPRS International Journal of Geo-Information
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The appropriate locations of road emergency stations (RESs) can help to decrease the impact of traffic accidents that cause around 50 million injuries per year worldwide. In this research, the appropriateness of existing RESs in the Khuzestan province, Iran, was assessed using an integrated fuzzy analytical hierarchy process (FAHP) and geographic information system (GIS) approach. The data used in this research were collected from different sources, including the department of roads, the department of health, the statistics organization, forensics, police centers, the surveying and geological department, remotely-sensed and global positioning system (GPS) data of accident high crash zones. On the basis of previous studies and the requirements of the Ministry of Health and Medical Education, as well as the department of roads of Iran for the location of RESs, nine criteria and 19 sub-criteria were adopted, including population, safety, environmental indicators, compatible area in RES, incompatible area in RES, type of road, accident high crash zones, traffic level and performance radius. The FAHP yielded the criteria weights and the ideal locations for establishing RESs using GIS analysis and aggregation functions. The resulting map matched the known road accident and high crash zones very well. The results indicated that the current RES stations are not distributed appropriately along the major roads of the Khuzestan province, and a re-arrangement is suggested. The finding of the present study can help decision-makers and authorities to achieve sustainable road safety in the case study area.

ACS Style

Abbas Naboureh; Bakhtiar Feizizadeh; Jinhu Bian; Thomas Blaschke; Omid Ghorbanzadeh; Meisam Moharrami. Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services. ISPRS International Journal of Geo-Information 2019, 8, 371 .

AMA Style

Abbas Naboureh, Bakhtiar Feizizadeh, Jinhu Bian, Thomas Blaschke, Omid Ghorbanzadeh, Meisam Moharrami. Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services. ISPRS International Journal of Geo-Information. 2019; 8 (9):371.

Chicago/Turabian Style

Abbas Naboureh; Bakhtiar Feizizadeh; Jinhu Bian; Thomas Blaschke; Omid Ghorbanzadeh; Meisam Moharrami. 2019. "Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services." ISPRS International Journal of Geo-Information 8, no. 9: 371.

Journal article
Published: 28 July 2019 in Fire
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Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product between 2012 and 2017 for Amol County, northern Iran. The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors (topographic, meteorological, vegetation, anthropological, and hydrological factors), was used to evaluate the potential of different machine learning (ML) approaches for the spatial prediction of wildfire susceptibility. The applied ML approaches included an artificial neural network (ANN), support vector machines (SVM), and random forest (RF). All ML approaches were trained using 75% of the wildfire inventory dataset and tested using the remaining 25% of the dataset in the four-fold cross-validation (CV) procedure. The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics (ROC) curve. The resulting CV accuracies were 74%, 79% and 88% for the ANN, SVM and RF, respectively.

ACS Style

Omid Ghorbanzadeh; Khalil Valizadeh Kamran; Thomas Blaschke; Jagannath Aryal; Amin Naboureh; Jamshid Einali; Jinhu Bian. Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches. Fire 2019, 2, 43 .

AMA Style

Omid Ghorbanzadeh, Khalil Valizadeh Kamran, Thomas Blaschke, Jagannath Aryal, Amin Naboureh, Jamshid Einali, Jinhu Bian. Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches. Fire. 2019; 2 (3):43.

Chicago/Turabian Style

Omid Ghorbanzadeh; Khalil Valizadeh Kamran; Thomas Blaschke; Jagannath Aryal; Amin Naboureh; Jamshid Einali; Jinhu Bian. 2019. "Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches." Fire 2, no. 3: 43.

Original paper
Published: 13 June 2017 in Arabian Journal of Geosciences
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The present paper is an attempt to integrate a semi-automated object-based image analysis (OBIA) classification framework and a cellular automata-Markov model to study land use/land cover (LULC) changes. Land use maps for the Sarab plain in Iran for the years 2000, 2006, and 2014 were created from Landsat satellite data, by applying an OBIA classification using the normalized difference vegetation index, salinity index, moisture stress index, soil-adjusted vegetation index, and elevation and slope indicators. The classifications yielded overall accuracies of 91, 93, and 94% for 2000, 2006, and 2014, respectively. Finally, using the transition matrix, the spatial distribution of land use was simulated for 2020. The results of the study revealed that the number of orchards with irrigated agriculture and dry-farm agriculture in the Sarab plain is increasing, while the amount of bare land is decreasing. The results of this research are of great importance for regional authorities and decision makers in strategic land use planning.

ACS Style

Amin Naboureh; Mohammad Hossein Rezaei Moghaddam; Bakhtiar Feizizadeh; Thomas Blaschke. An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain. Arabian Journal of Geosciences 2017, 10, 259 .

AMA Style

Amin Naboureh, Mohammad Hossein Rezaei Moghaddam, Bakhtiar Feizizadeh, Thomas Blaschke. An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain. Arabian Journal of Geosciences. 2017; 10 (12):259.

Chicago/Turabian Style

Amin Naboureh; Mohammad Hossein Rezaei Moghaddam; Bakhtiar Feizizadeh; Thomas Blaschke. 2017. "An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain." Arabian Journal of Geosciences 10, no. 12: 259.