Encoding Geospatial Vector Data for Deep Learning: LULC as a Use Case

Cutchan, Marvin Mc; Giannopoulos, Ioannis

doi:10.3390/rs14122812

Cited by 3 publications

(2 citation statements)

References 36 publications

(81 reference statements)

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“…These networks utilize filters (kernels) that traverse input images to detect relevant patterns and features, crucial in tasks like land use classification. Pooling layers, such as Max Pooling, are employed to down sample spatial dimensions, reducing computational complexity while preserving essential features and enhancing network robustness and efficiency (Mc Cutchan and Giannopoulos, 2022). Following convolutional and pooling layers, fully connected layers facilitate learning high-level representations and making predictions based on earlier extracted features (Krivoguz et al, 2023).…”

Section: Convolution Neural Network (Cnn)mentioning

confidence: 99%

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Nigar,

Li,

Jat Baloch

et al. 2024

Front. Environ. Sci.

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Classifying land use and land cover (LULC) is essential for various environmental monitoring and geospatial analysis applications. This research focuses on land classification in District Sukkur, Pakistan, employing the comparison between machine and deep learning models. Three satellite indices, namely, NDVI, MNDWI, and NDBI, were derived from Landsat-8 data and utilized to classify four primary categories: Built-up Area, Water Bodies, Barren Land, and Vegetation. The main objective of this study is to evaluate and compare the effectiveness of comparison of machine and deep learning models. The machine learning models including Random Forest achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.90. It accurately classified 2.7% of the area as Built-up Area, 1.9% as Water Bodies, 54.8% as Barren Land, and 40.4% as Vegetation. While slightly less accurate, Decision Tree model provided reliable classifications. Deep learning models showed significant accuracy, of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The CNN model achieved an impressive overall accuracy of 97.3%, excelling in classifying Water Bodies with User and Producer Accuracy exceeding 99%. The RNN model, with an overall accuracy of 96.2%, demonstrated strong performance in categorizing Vegetation. These findings offer valuable insights into the potential applications of machine learning and deep learning models for perfect land classifications, with implications for environmental monitoring management and geospatial analysis. The rigorous validation and comparative analysis of these models contribute to advancing remote sensing techniques and their utilization in land classification tasks. This research presents a significant contribution to the field and underscores the importance of precise land classification in the context of sustainable land management and environmental conservation.

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Section: Convolution Neural Network (Cnn)mentioning

confidence: 99%

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Nigar,

Li,

Jat Baloch

et al. 2024

Front. Environ. Sci.

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“…Thailand's diverse landscape includes dense forests, fertile farmland, and urban areas 7 . Remote sensing, with its ability to provide frequent and synoptic coverage of large areas, is a valuable tool for LULC mapping [8][9][10] . However, the vast amounts of data generated by remote sensing platforms, such as Landsat, require efficient and accurate processing techniques.…”

Section: Introductionmentioning

confidence: 99%

MeViT: Medium-Resolution Vision Transformer for Semantic Segmentation on Landsat Satellite Imagery for Agriculture in Thailand

Panboonyuen

Charoenphon

Satirapod

2023

Preprint

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Semantic segmentation is a fundamental task in remote sensing image analysis that aims to classify each pixel in an image into different land use and land cover (LULC) segmentation task. In this paper, We propose MeViT (Medium-Resolution Vision Transformer) on Landsat satellite imagery for the main economic crops in Thailand as follows: (i) para rubber, (ii) corn, and (iii) pineapple. Therefore, our proposed MeViT enhances Vision Transformers (ViTs), one of the modern deep learning on computer vision tasks, to learn semantically rich and spatially-precise multi-scale representations by integrating medium-resolution multi-branch architectures with ViTs. We revised mixed-scale convolutional feedforward networks (MixCFN) by incorporating multiple depth-wise convolution paths to extract multi-scale local information to balance the model’s performance and efficiency. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on the publicly available dataset of Thailand scenes and compare the results with several state-of-the-art deep learning methods such as AutoDeeplab1, SwinTransformer2, Twins3, CSWinTransformer4, SegFormer5, and HRViT6. The experimental results demonstrate that our proposed MeViT outperforms existing methods and performs better in the semantic segmentation of Thailand scenes The evaluation metrics used are precision, recall, F1 score, and mean Intersection over Union (IoU). Among the models compared, MeViT, our proposed model, achieves the best performance in all evaluation metrics. MeViT achieves a precision of 0.9222, a recall of 0.9469, an F1 score of 0.9344, and a mean IoU of 0.8363. These results demonstrate the effectiveness of our proposed approach in accurately segmenting Thai Landsat-8 data. The achieved F1 score of overall using our proposed MeViT is 93.44% that is a major significance of this work.

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Incorporating ideas of structure and meaning in interactive multi scale mapping environments

Touya

Potié

Mackaness

2023

International Journal of Cartography

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Encoding Geospatial Vector Data for Deep Learning: LULC as a Use Case

Cited by 3 publications

References 36 publications

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

MeViT: Medium-Resolution Vision Transformer for Semantic Segmentation on Landsat Satellite Imagery for Agriculture in Thailand

Incorporating ideas of structure and meaning in interactive multi scale mapping environments

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