Spatial and decadal prediction of land use/land cover using multi-layer perceptron-neural network (MLP-NN) algorithm for a semi-arid region of Asir, Saudi Arabia

Alqadhi, Saeed; Mallick, Javed; Balha, Akanksha; Bindajam, Ahmed Ali; Singh, Chander Kumar; Hoa, Pham Viet

doi:10.1007/s12145-021-00633-2

Cited by 49 publications

(28 citation statements)

References 89 publications

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“…The beginning of its Appearance after 2010. And predicting future land changes requires taking into account the past, present, and future scenarios [31][32][33][34][35][36]. Predictions of land changes have been made long, commencing in early 1930 [37].…”

Section: Related Workmentioning

confidence: 99%

Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE

Alshari

Gawali

2022

Journal of Sensors

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This study provided insight into the size of the difference between the actual and predicted changes in Landsat 8 satellite imagery for the case study Sana’a of Yemen. The LULC classification was created using data available in 2005, 2010, 2015, and 2020. It used the MOLUSCE tool for predicting land changes for the predicted for 2010, 2015, 2020, 2025, and 2030. The objectives of this study are 1) To compare the actual and predicted land changes in 2010,2015 and 2020. 2) To analyze and verify the tool’s performance (MOLUSCE). 3) To identify the size of effect which evented land changes in 2015 on land changes in 2020,2025 and 2030. The results were: 1/the effects of land changes in 2010 showed the accuracy and reliability of MOLUSCE for predicting land changes due to the low difference between the actual and predicted 2010 before the conflict in the region. 2/the actual changes for 2015 were negative and did not support the logical trend toward progress where it is natural that the human element progresses to the increasing construction. 3/identify prediction changes for (2020,2025,2030) are affected by events conflict, which showed in the results of the 2015 images.

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Section: Related Workmentioning

confidence: 99%

Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE

Alshari

Gawali

2022

Journal of Sensors

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“…Another work in [ 14 ] uses the MLP-NN modeling for a land prediction system. It uses images from a Landsat satellite to determine the land-usage.…”

Section: Related Workmentioning

confidence: 99%

Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

Hussein

Mohammed

Galal

et al. 2022

Sensors

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The Internet of Things (IoT) era is mainly dependent on the word “Smart”, such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models. By adding the Artificial Intelligence (AI) algorithms to IoT, the result is the Cognitive IoT (CIoT). In the automotive industry, many researchers worked on self-diagnosis systems using deep learning, but most of them performed this process on the cloud due to the hardware limitations of the end-devices, and the devices obtain the decision via the cloud servers. Others worked with simple traditional algorithms of machine learning to solve these limitations of the processing capabilities of the end-devices. In this paper, a self-diagnosis smart device is introduced with fast responses and little overhead using the Multi-Layer Perceptron Neural Network (MLP-NN) as a deep learning technique. The MLP-NN learning stage is performed using a Tensorflow framework to generate an MLP model’s parameters. Then, the MLP-NN model is implemented using these model’s parameters on a low cost end-device such as ARM Cortex-M Series architecture. After implementing the MLP-NN model, the IoT implementation is built to publish the decision results. With the proposed implemented method for the smart device, the output decision based on sensors values can be taken by the IoT node itself without returning to the cloud. For comparison, another solution is proposed for the cloud-based architecture, where the MLP-NN model is implemented on Cloud. The results clarify a successful implemented MLP-NN model for little capabilities end-devices, where the smart device solution has a lower traffic and latency than the cloud-based solution.

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“…In recent years, applications of deep learning neural networks have been gaining momentum in the classification and mapping of land cover and vegetation types. For this purpose, model architectures made up of neural-network layers such as multilayer perceptron [45,46], convolutional [47,48], recurrent [49,50], convolutional-recurrent [51,52], and attentionrecurrent [53,54] have been employed and shown outstanding performance. A review of the existing research and classification methods shows that most of the previous studies were implemented for local-scale classification and mapping of land cover and vegetation types.…”

Section: Introductionmentioning

confidence: 99%

Countrywide Mapping of Plant Ecological Communities with 101 Legends including Land Cover Types for the First Time at 10 m Resolution through Convolutional Learning of Satellite Images

Sharma

2022

Applied Sciences

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This paper presents next-generation mapping of plant ecological communities including land cover and agricultural types at 10 m spatial resolution countrywide. This research introduces modelling and mapping of land cover and ecological communities separately in small regions-of-interest (prefecture level), and later integrating the outputs into a large scale (country level) for dealing with regional distribution characteristics of plant ecological communities effectively. The Sentinel-2 satellite images were processed for cloud masking and half-monthly median composite images consisting of ten multi-spectral bands and seven spectral indexes were generated. The reliable ground truth data were prepared from extant multi-source survey databases through the procedure of stratified sampling, cross-checking, and noisy-labels pruning. Deep convolutional learning of the time-series of the satellite data was employed for prefecture-wise classification and mapping of 29–62 classes. The classification accuracy computed with the 10-fold cross-validation method varied from 71.1–87.5% in terms of F1-score and 70.9–87.4% in terms of Kappa coefficient across 48 prefectural regions. This research produced seamless maps of 101 ecological communities including land cover and agricultural types for the first time at a country scale with an average accuracy of 80.5% F1-score.

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Spatial and decadal prediction of land use/land cover using multi-layer perceptron-neural network (MLP-NN) algorithm for a semi-arid region of Asir, Saudi Arabia

Cited by 49 publications

References 89 publications

Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE

Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE

Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

Countrywide Mapping of Plant Ecological Communities with 101 Legends including Land Cover Types for the First Time at 10 m Resolution through Convolutional Learning of Satellite Images

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