A New Mask R-CNN-Based Method for Improved Landslide Detection Article Swipe
Silvia Liberata Ullo
,
Amrita Mohan
,
Alessandro Sebastianelli
,
Shaik Ahamed
,
Basant Kumar
,
R. S. Dwivedi
,
G. R. Sinha
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2021.3064981
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2021.3064981
This article presents a novel method of landslide detection by exploiting the Mask R-CNN capability of identifying an object layout by using a pixel-based segmentation, along with transfer learning used to train the proposed model. A data set of 160 elements is created containing landslide and nonlandslide images. The proposed method consists of three steps: augmenting training image samples to increase the volume of the training data; fine-tuning with limited image samples; and performance evaluation of the algorithm in terms of precision, recall, and F1 measure, on the considered landslide images, by adopting ResNet-50 and 101 as backbone models. The experimental results are quite encouraging as the proposed method achieves precision equals to 1.00, recall 0.93, and F1 measure 0.97, when ResNet-101 is used as backbone model, and with a low number of landslide photographs used as training samples. The proposed algorithm can be potentially useful for land-use planners and policymakers of hilly areas where intermittent slope deformations necessitate landslide detection as a prerequisite before planning.
Related Topics
Concepts
Landslide
Computer science
Artificial intelligence
Segmentation
Pixel
Pattern recognition (psychology)
Set (abstract data type)
Measure (data warehouse)
Residual neural network
Data set
Image segmentation
Image (mathematics)
Precision and recall
Object detection
Computer vision
Data mining
Deep learning
Geology
Geotechnical engineering
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2021.3064981
- https://ieeexplore.ieee.org/ielx7/4609443/9314330/09373966.pdf
- OA Status
- gold
- Cited By
- 132
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3135903521
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3135903521Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/jstars.2021.3064981Digital Object Identifier
- Title
-
A New Mask R-CNN-Based Method for Improved Landslide DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Silvia Liberata Ullo, Amrita Mohan, Alessandro Sebastianelli, Shaik Ahamed, Basant Kumar, R. S. Dwivedi, G. R. SinhaList of authors in order
- Landing page
-
https://doi.org/10.1109/jstars.2021.3064981Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/4609443/9314330/09373966.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/4609443/9314330/09373966.pdfDirect OA link when available
- Concepts
-
Landslide, Computer science, Artificial intelligence, Segmentation, Pixel, Pattern recognition (psychology), Set (abstract data type), Measure (data warehouse), Residual neural network, Data set, Image segmentation, Image (mathematics), Precision and recall, Object detection, Computer vision, Data mining, Deep learning, Geology, Geotechnical engineering, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
132Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 37, 2024: 43, 2023: 21, 2022: 19, 2021: 12Per-year citation counts (last 5 years)
- References (count)
-
57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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