A Deep Learning Pipeline for Drought Assessment using Spatial Satellite Images and Vision Transformers Article Swipe
Anisha Doshi
,
J. Kim
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.47611/jsrhs.v13i3.7440
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.47611/jsrhs.v13i3.7440
700 million people are in danger of being displaced due to inept drought prediction and prevention systems. Current research on drought assessment focuses solely on factors such as soil moisture and rainfall, which require painstaking measurements and lab samples, and can often be misleading. This research eliminates this requirement by proposing an end-to-end pipeline to detect and prevent droughts in at-risk areas using satellite images and vision transformers. The dataset is comprised of over 86,000 satellite images labeled by pastoralists and divided with an 80-20 ratio for training and validation. First, using feature filtering, normalization, and a Gaussian filter, the images in the dataset are modified to yield a better performance. Next, a deep vision transformer model with multi-headed attention is constructed, consisting of four heads, three transformer layers, and a patch size of five. The final MLP head produces logits for drought severity prediction level. Overall, the best transformer model achieves 78.3% accuracy in predicting drought conditions on a validation set of 10,000, unseen satellite images. In addition, this method outperforms state-of-the-art convolutional neural networks on this classification task, as compared to VGG-16, ResNet-50 and DenseNet-121 models. The model harnesses AWS cloud computing, deep vision transformers, and specific image augmentation to achieve state-of-the-art results in drought prediction and prevention. With this research, scientists have the potential to assess droughts quickly and accurately, revolutionizing our ability to provide resources and care to those affected by the increasingly common droughts caused by the climate crisis worldwide.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.47611/jsrhs.v13i3.7440
- https://www.jsr.org/hs/index.php/path/article/download/7440/3439
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413562545
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413562545Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.47611/jsrhs.v13i3.7440Digital Object Identifier
- Title
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A Deep Learning Pipeline for Drought Assessment using Spatial Satellite Images and Vision TransformersWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-31Full publication date if available
- Authors
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Anisha Doshi, J. KimList of authors in order
- Landing page
-
https://doi.org/10.47611/jsrhs.v13i3.7440Publisher landing page
- PDF URL
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https://www.jsr.org/hs/index.php/path/article/download/7440/3439Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://www.jsr.org/hs/index.php/path/article/download/7440/3439Direct OA link when available
- Concepts
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Artificial intelligence, Deep learning, Pipeline (software), Computer science, Transformer, Satellite image, Computer vision, Remote sensing, Satellite, Environmental science, Geography, Engineering, Electrical engineering, Voltage, Aerospace engineering, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.conditions | 157 |
| abstract_inverted_index.consisting | 122 |
| abstract_inverted_index.eliminates | 46 |
| abstract_inverted_index.end-to-end | 52 |
| abstract_inverted_index.filtering, | 93 |
| abstract_inverted_index.predicting | 155 |
| abstract_inverted_index.prediction | 13, 144, 207 |
| abstract_inverted_index.prevention | 15 |
| abstract_inverted_index.scientists | 213 |
| abstract_inverted_index.validation | 160 |
| abstract_inverted_index.worldwide. | 244 |
| abstract_inverted_index.accurately, | 222 |
| abstract_inverted_index.misleading. | 43 |
| abstract_inverted_index.outperforms | 171 |
| abstract_inverted_index.painstaking | 34 |
| abstract_inverted_index.prevention. | 209 |
| abstract_inverted_index.requirement | 48 |
| abstract_inverted_index.transformer | 115, 127, 149 |
| abstract_inverted_index.validation. | 89 |
| abstract_inverted_index.DenseNet-121 | 186 |
| abstract_inverted_index.augmentation | 200 |
| abstract_inverted_index.constructed, | 121 |
| abstract_inverted_index.increasingly | 236 |
| abstract_inverted_index.measurements | 35 |
| abstract_inverted_index.multi-headed | 118 |
| abstract_inverted_index.pastoralists | 79 |
| abstract_inverted_index.performance. | 110 |
| abstract_inverted_index.convolutional | 173 |
| abstract_inverted_index.transformers, | 196 |
| abstract_inverted_index.transformers. | 67 |
| abstract_inverted_index.classification | 178 |
| abstract_inverted_index.normalization, | 94 |
| abstract_inverted_index.revolutionizing | 223 |
| abstract_inverted_index.state-of-the-art | 172, 203 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.3032067 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |