Using Convolutional Neural Networks on Satellite Images to Predict Poverty

Okaidat, Arwa; Melhem, Shatha; Alenezi, Heba; Duwairi, Rehab

doi:10.1109/icics52457.2021.9464598

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Remote sensing imagery has been used in many geoscience observation fields such as land cover classification [1,2], environment monitoring [3], change detection [4,5], forest canopy closure estimation [6], and poverty prediction [7,8]. However, remote sensing imagery is inevitably affected by many factors, such as cloud occlusions, weather, and climate effects.…”

Section: Introductionmentioning

confidence: 99%

CTGAN : Cloud Transformer Generative Adversarial Network

Huang

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Cloud occlusions obstruct some applications of remote sensing imagery, such as environment monitoring, land cover classification, and poverty prediction. In this paper, we propose the Cloud Transformer Generative Adversarial Network (CTGAN), taking three temporal cloudy images as input and generating a corresponding cloud-free image. Unlike previous work using generative networks, we design the feature extractor to maintain the weight of the cloudless region while reducing the weight of the cloudy region, and we pass the extracted features to a conformer module to find the most critical representations. Meanwhile, to address the lack of datasets, we collected a new dataset named Sen2 MTC from the Sentinel-2 satellite and manually labeled each cloudy and cloud-free image. Finally, we conducted extensive experiments on FS-2, the STGAN dataset, and Sen2 MTC. Our proposed CTGAN demonstrates higher qualitative and quantitative performance than the previous work and achieves state-of-the-art performance on these three datasets. The code is available at https://github.com/come880412/CTGAN

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Section: Introductionmentioning

confidence: 99%

CTGAN : Cloud Transformer Generative Adversarial Network

Huang

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Artificial neural network (ANN) in forecasting of poverty line and economic-energetic efficiencies into the maize-based agroecosystems

Purroy Vasquez,

Aguilar Lasserre,

Meza Palacios

et al. 2023

Archives of Agronomy and Soil Science

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Deep Transfer Learning of Satellite Imagery for Land Use and Land Cover Classification

Yifter

Razoumny

Lobanov

2022

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Deep learning has been instrumental in solving difficult problems by automatically learning, from sample data, the rules (algorithms) that map an input to its respective output. Purpose: Perform land use landcover (LULC) classification using the training data of satellite imagery for Moscow region and compare the accuracy attained from different models. Methods: The accuracy attained for LULC classification using deep learning algorithm and satellite imagery data is dependent on both the model and the training dataset used. We have used state-of-the-art deep learning models and transfer learning, together with dataset appropriate for the models. Different methods were applied to fine tuning the models with different parameters and preparing the right dataset for training, including using data augmentation. Results: Four models of deep learning from Residual Network (ResNet) and Visual Geometry Group (VGG) namely: ResNet50, ResNet152, VGG16 and VGG19 has been used with transfer learning. Further training of the models is performed with training data collected from Sentinel-2 for the Moscow region and it is found that ResNet50 has given the highest accuracy for LULC classification for this region. Practical relevance: We have developed code that train the 4 models and make classification of the input image patches into one of the 10 classes (Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial, Pasture, Permanent Crop, Residential, River, and Sea&Lake).

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Using Convolutional Neural Networks on Satellite Images to Predict Poverty

Cited by 3 publications

References 21 publications

CTGAN : Cloud Transformer Generative Adversarial Network

CTGAN : Cloud Transformer Generative Adversarial Network

Artificial neural network (ANN) in forecasting of poverty line and economic-energetic efficiencies into the maize-based agroecosystems

Deep Transfer Learning of Satellite Imagery for Land Use and Land Cover Classification

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