Slum image detection and localization using transfer learning: a case study in Northern Morocco

Moudden, Tarik El; Dahmani, Rachid; Amnai, Mohamed; Fora, Abderrahmane Ait

doi:10.11591/ijece.v13i3.pp3299-3310

Cited by 3 publications

(2 citation statements)

References 39 publications

(51 reference statements)

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“…Through harnessing this pre-training, the model can be fine-tuned or employed as a feature extractor for a range of computer vision assignments, including image classification, object detection, and image segmentation [29], [30]. There are several CNN-based methods that are widely used for various applications [31]- [33], but in this study, we focus on ResNet50. We will include other deep learning methods on future work agendas.…”

Section: Methodsmentioning

confidence: 99%

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

Sanmorino,

Kesuma

2024

Bulletin EEI

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Distributed denial-of-service (DDoS) attacks pose a significant risk to the dependability and consistency of network services. The utilization of deep learning (DL) models has displayed encouraging outcomes in the identification of DDoS attacks. Nevertheless, crafting a precise DL model necessitates an extensive volume of labeled data and substantial computational capabilities. Within this piece, we introduce a technique to enhance a pre-trained DL model for the identification of DDoS attacks. Our strategy’s efficacy is showcased on an openly accessible dataset, revealing that the fine-tuned model we propose surpasses both the initial pre-trained model and other cutting-edge approaches in performance. The suggested fine-tuned model attained 95.1% accuracy, surpassing the initial pre-trained model as well as other leading-edge techniques. Please note that the specific evaluation metrics and their values may vary depending on the implementation, hyperparameter settings, number of datasets, or dataset characteristics. The proposed approach has several advantages, including reducing the amount of labeled data required and accelerating the training process. Initiating with a pre-existing ResNet50 model can also enhance the eventual model’s accuracy, given that the pre-trained model has already acquired the ability to extract significant features from unprocessed data.

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Section: Methodsmentioning

confidence: 99%

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

Sanmorino,

Kesuma

2024

Bulletin EEI

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“…DL models automatically extract discriminative high-level semantic features without the need for complex feature engineering of the input data [25], outperforming previous techniques in the capacity to extract information. Studies [29][30][31][32][33] on slum mapping based on DL have also generated fine results. Wurm M. et al [29] employed a fully convolutional neural network (FCN) to extract slums and obtained an overall accuracy (OA) of 90.64% on Quick Bird images with a resolution of 2 m and an OA of 86.71% on Sentinel-2 images with a resolution of 10 m. Wurm M.'s study demonstrated the advancement of DL on slum mapping.…”

Section: Introductionmentioning

confidence: 99%

A Geoscience-Aware Network (GASlumNet) Combining UNet and ConvNeXt for Slum Mapping

Lu,

Hu,

Peng

et al. 2024

Remote Sensing

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Approximately 1 billion people worldwide currently inhabit slum areas. The UN Sustainable Development Goal (SDG 11.1) underscores the imperative of upgrading all slums by 2030 to ensure adequate housing for everyone. Geo-locations of slums help local governments with upgrading slums and alleviating urban poverty. Remote sensing (RS) technology, with its excellent Earth observation capabilities, can play an important role in slum mapping. Deep learning (DL)-based RS information extraction methods have attracted a lot of attention. Currently, DL-based slum mapping studies typically uses three optical bands to adapt to existing models, neglecting essential geo-scientific information, such as spectral and textural characteristics, which are beneficial for slum mapping. Inspired by the geoscience-aware DL paradigm, we propose the Geoscience-Aware Network for slum mapping (GASlumNet), aiming to improve slum mapping accuracies via incorporating the DL model with geoscientific prior knowledge. GASlumNet employs a two-stream architecture, combining ConvNeXt and UNet. One stream concentrates on optical feature representation, while the other emphasizes geo-scientific features. Further, the feature-level and decision-level fusion mechanisms are applied to optimize deep features and enhance model performance. We used Jilin-1 Spectrum 01 and Sentinel-2 images to perform experiments in Mumbai, India. The results demonstrate that GASlumNet achieves higher slum mapping accuracy than the comparison models, with an intersection over union (IoU) of 58.41%. Specifically, GASlumNet improves the IoU by 4.60~5.97% over the baseline models, i.e., UNet and ConvNeXt-UNet, which exclusively utilize optical bands. Furthermore, GASlumNet enhances the IoU by 10.97% compared to FuseNet, a model that combines optical bands and geo-scientific features. Our method presents a new technical solution to achieve accurate slum mapping, offering potential benefits for regional and global slum mapping and upgrading initiatives.

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New unfreezing strategy of transfer learning in satellite imagery for mapping the diversity of slum areas: A case study in Kenitra city—Morocco

Moudden,

Amnai,

Choukri

et al. 2024

Scientific African

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Slum image detection and localization using transfer learning: a case study in Northern Morocco

Cited by 3 publications

References 39 publications

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

A Geoscience-Aware Network (GASlumNet) Combining UNet and ConvNeXt for Slum Mapping

New unfreezing strategy of transfer learning in satellite imagery for mapping the diversity of slum areas: A case study in Kenitra city—Morocco

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