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Papers by mohammad kakooei

Research paper thumbnail of Remote sensing technology for postdisaster building damage assessment

Computers in Earth and Environmental Sciences

Research paper thumbnail of Sentinel-1 Global Coverage Foreshortening Mask Extraction: an Open Source Implementation Based on Google Earth Engine

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

Research paper thumbnail of Spectral Unmixing of Time Series Data to Provide Initial Object Seeds for Change Detection on Google Earth Engine

2019 27th Iranian Conference on Electrical Engineering (ICEE)

Research paper thumbnail of Wetland Change Analysis in Alberta, Canada using Four Decades of Landsat Imagery

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

In this study, wetland trends in Alberta were investigated in the past four decades using Landsat... more In this study, wetland trends in Alberta were investigated in the past four decades using Landsat satellite imagery to produce updated information about wetland changes and to prevent further degradation of these valuable natural resources. All the processing steps and analyses were conducted in Google Earth Engine (GEE) to produce 16 wetland maps from 1984 to 2020. A comprehensive change analysis showed (1) approximately 18% of the province was subjected to change; (2) in terms of wetland classes, there was a decreasing trend for the Shallow Water and Swamp classes and an increasing trend for the Fen and Marsh classes; (3) in terms of non-wetland classes, there was a considerable decreasing trend for the Forest class and increasing trend for the Grassland/Shrubland class; (4) wetland loss was approximately 22,000 km 2 , which was mainly due to the conversion of wetlands to Forest and Grassland/Shrubland; (5) wetland gain was approximately 24,000 km 2 , which was mainly due to the conversion from the Forest class to wetlands, especially the Swamp and Fen classes; (6) The highest class transition was from Cropland to Grassland/Shrubland and vice versa (29,000 km 2), from Forest to different wetland classes (18,000 km 2), and from Fen to Forest (6,000 km 2). In summary, the results of this study provided the first comprehensive information on wetland trends in Alberta over the past 37 years and will assist policymakers to adjust the required/established policies to mitigate the potential wetland changes due to anthropogenic activities and climate-related events.

Research paper thumbnail of Evaluation of the Landsat-based Canadian Wetland Inventory Map using Multiple Sources: Challenges of Large-scale Wetland Classification using Remote Sensing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

The first Canadian Wetland Inventory (CWI) map, which was based on Landsat data, was produced in ... more The first Canadian Wetland Inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost-, time-, and computationally efficient approach. Although the initial effort to produce an CWI map was valuable with a 71% Overall Accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental nonprofit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in-situ data, photo-interpreted reference samples, Land Cover/Land Use (LCLU) maps, and highresolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in-situ data was 60%. Moreover, including reliable in-situ data, using an object-based classification method, and adding more optical and Synthetic Aperture RADAR (SAR) datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.

Research paper thumbnail of Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada

Remote Sensing

The ability of the Canadian agriculture sector to make better decisions and manage its operations... more The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Devel...

Research paper thumbnail of Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managin... more Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of Manuscript

Research paper thumbnail of Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples

ISPRS Journal of Photogrammetry and Remote Sensing

Research paper thumbnail of A two-level fusion for building irregularity detection in post-disaster VHR oblique images

Earth Science Informatics

Research paper thumbnail of Shadow detection in very high resolution RGB images using a special thresholding on a new spectral–spatial index

Journal of Applied Remote Sensing

Research paper thumbnail of A Fast Parallel GPS Acquisition Algorithm Based on Hybrid GPU and Multi-core CPU

Wireless Personal Communications

Research paper thumbnail of Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment

International Journal of Remote Sensing

Research paper thumbnail of A novel method of applying directional filter bank (DFB) for Finger-Knuckle-Print (FKP) recognition

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014

Research paper thumbnail of A parallel k-means clustering initial center selection and dynamic center correction on GPU

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014

Research paper thumbnail of A parallel k-means clustering initial center selection and dynamic center correction on GPU

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014

Research paper thumbnail of A parallel k-means clustering initial center selection and dynamic center correction on GPU

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014

Research paper thumbnail of Remote sensing technology for postdisaster building damage assessment

Computers in Earth and Environmental Sciences

Research paper thumbnail of Sentinel-1 Global Coverage Foreshortening Mask Extraction: an Open Source Implementation Based on Google Earth Engine

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

Research paper thumbnail of Spectral Unmixing of Time Series Data to Provide Initial Object Seeds for Change Detection on Google Earth Engine

2019 27th Iranian Conference on Electrical Engineering (ICEE)

Research paper thumbnail of Wetland Change Analysis in Alberta, Canada using Four Decades of Landsat Imagery

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

In this study, wetland trends in Alberta were investigated in the past four decades using Landsat... more In this study, wetland trends in Alberta were investigated in the past four decades using Landsat satellite imagery to produce updated information about wetland changes and to prevent further degradation of these valuable natural resources. All the processing steps and analyses were conducted in Google Earth Engine (GEE) to produce 16 wetland maps from 1984 to 2020. A comprehensive change analysis showed (1) approximately 18% of the province was subjected to change; (2) in terms of wetland classes, there was a decreasing trend for the Shallow Water and Swamp classes and an increasing trend for the Fen and Marsh classes; (3) in terms of non-wetland classes, there was a considerable decreasing trend for the Forest class and increasing trend for the Grassland/Shrubland class; (4) wetland loss was approximately 22,000 km 2 , which was mainly due to the conversion of wetlands to Forest and Grassland/Shrubland; (5) wetland gain was approximately 24,000 km 2 , which was mainly due to the conversion from the Forest class to wetlands, especially the Swamp and Fen classes; (6) The highest class transition was from Cropland to Grassland/Shrubland and vice versa (29,000 km 2), from Forest to different wetland classes (18,000 km 2), and from Fen to Forest (6,000 km 2). In summary, the results of this study provided the first comprehensive information on wetland trends in Alberta over the past 37 years and will assist policymakers to adjust the required/established policies to mitigate the potential wetland changes due to anthropogenic activities and climate-related events.

Research paper thumbnail of Evaluation of the Landsat-based Canadian Wetland Inventory Map using Multiple Sources: Challenges of Large-scale Wetland Classification using Remote Sensing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

The first Canadian Wetland Inventory (CWI) map, which was based on Landsat data, was produced in ... more The first Canadian Wetland Inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost-, time-, and computationally efficient approach. Although the initial effort to produce an CWI map was valuable with a 71% Overall Accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental nonprofit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in-situ data, photo-interpreted reference samples, Land Cover/Land Use (LCLU) maps, and highresolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in-situ data was 60%. Moreover, including reliable in-situ data, using an object-based classification method, and adding more optical and Synthetic Aperture RADAR (SAR) datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.

Research paper thumbnail of Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada

Remote Sensing

The ability of the Canadian agriculture sector to make better decisions and manage its operations... more The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Devel...

Research paper thumbnail of Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managin... more Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of Manuscript

Research paper thumbnail of Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples

ISPRS Journal of Photogrammetry and Remote Sensing

Research paper thumbnail of A two-level fusion for building irregularity detection in post-disaster VHR oblique images

Earth Science Informatics

Research paper thumbnail of Shadow detection in very high resolution RGB images using a special thresholding on a new spectral–spatial index

Journal of Applied Remote Sensing

Research paper thumbnail of A Fast Parallel GPS Acquisition Algorithm Based on Hybrid GPU and Multi-core CPU

Wireless Personal Communications

Research paper thumbnail of Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment

International Journal of Remote Sensing

Research paper thumbnail of A novel method of applying directional filter bank (DFB) for Finger-Knuckle-Print (FKP) recognition

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014

Research paper thumbnail of A parallel k-means clustering initial center selection and dynamic center correction on GPU

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014

Research paper thumbnail of A parallel k-means clustering initial center selection and dynamic center correction on GPU

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014

Research paper thumbnail of A parallel k-means clustering initial center selection and dynamic center correction on GPU

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014