Estimation and Mapping of Dates Palm Using Landsat-8 Images: A Case Study in Baghdad City

Shareef, Muntadher Aidi; Hasan, Sumaya Falih; Ajaj, Qayssar Mahmood

doi:10.1109/icoase.2018.8548787

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…To the best of the authors' knowledge, the vast majority of date palm mapping studies focus on the utilization of the traditional machine learning algorithms, such as traditional maximum likelihood supervised classification [130], spectral indices and thresholding analysis [131,132], hybrid per-pixel classification approach [133], fuzzy logic [134], and DT rule-based object-based image analysis [135]. Only limited studies have been dedicated to using deep learning techniques to detect date palm trees [9,136].…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

et al. 2021

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Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

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Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

et al. 2021

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“…Classification of image can be defined as grouping image pixels or objects into selected classes for producing a thematic map (Ajaj et al, 2017). Continuous supervision of retrieved locations offers valuable data on the change in vegetation cover (Shareef et al, 2018). GIS presents a regularly utilized means to examine knowledge obtained from the LULC through remote sensing data and to identify and map the LULC changes.…”

Section: Introductionmentioning

confidence: 99%

Change Detection and Gis-Based Fuzzy Ahp to Evaluate the Degradation and Reclamation Land of Tikrit City, Iraq

Shareef

Ameen

Ajaj

2021

Geodesy and Cartography

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LULC factors in Tikrit city (Iraq) and the neighboring municipalities are studied among 1989, 2002 and 2015 using various techniques of remote sensing, geographical information system (GIS), and fuzzy analytical hierarchy process (FAHP). Satellite imagery with GIS helped to assess the standard LULC changes in the long term period. FAHP permitted estimating the importance of various LULC by determination of the suitable weight for used factors and then producing the evaluating models. Using different techniques, two models were created (1) to estimate the degradation of the land (2) is generated to determine the reclamation of the area. The finding reveals that the a overall accuracy of 97.0939%, 98.9199% and 99.5817% or 1989, 2002 and 2015 respectively. The outcomes also revealed that urban, vegetation, and water features area are developed in the long term (1989–2015) about 4.35%, 4.28%, and 1.49%, respectively, while barren area is reduced about 5.57%.The degradation map index showed that the lands strongly debased are these converted from vegetation to barren, followed by moderate to high these changed from water areas to urban, while moderate degradation is noticed of urban transformed to barren soil. Contrary, the reclamation map index illustrated that the lands are powerfully transformed from barren to the vegetation and followed by those converted from barren to the water, while barren transformed to the urban is marked as moderate reclamation. The transformation from urban to vegetation or water was classified as the low and deficient class to evaluate the area. The study is also revealed that the integration of remote sensing and GIS produces a successful method for LULC monitoring and managing the environment.

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Deep convolutional neural networks and Swin transformer-based frameworks for individual date palm tree detection and mapping from large-scale UAV images

Gibril

Shafri

Shanableh

et al. 2022

Geocarto International

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Estimation and Mapping of Dates Palm Using Landsat-8 Images: A Case Study in Baghdad City

Cited by 4 publications

References 14 publications

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

Change Detection and Gis-Based Fuzzy Ahp to Evaluate the Degradation and Reclamation Land of Tikrit City, Iraq

Deep convolutional neural networks and Swin transformer-based frameworks for individual date palm tree detection and mapping from large-scale UAV images

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