dc.contributor.author |
Wijesinghe, N |
|
dc.contributor.author |
Perera, R |
|
dc.contributor.author |
Sellahewa, N |
|
dc.contributor.author |
Talagala, PD |
|
dc.contributor.editor |
Piyatilake, ITS |
|
dc.contributor.editor |
Thalagala, PD |
|
dc.contributor.editor |
Ganegoda, GU |
|
dc.contributor.editor |
Thanuja, ALARR |
|
dc.contributor.editor |
Dharmarathna, P |
|
dc.date.accessioned |
2024-02-05T03:38:02Z |
|
dc.date.available |
2024-02-05T03:38:02Z |
|
dc.date.issued |
2023-12-07 |
|
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/22152 |
|
dc.description.abstract |
Research involving anomaly detection in image
streams has seen growth through the years, given the proliferation
of high-quality image data in various applications. One such
application that is in urgent need of attention is deforestation.
Detecting anomalies in this context, however, remains
challenging due to the irregular and low-probability nature of
deforestation events. This study introduces two anomaly detection
frameworks utilizing machine learning and deep learning for
the early detection of deforestation activities in image streams.
Furthermore, Explainable AI was used to explain the black box
models of the deep learning-based anomaly detection framework.
The class imbalance problem, the inter-dependency between the
images with time, the lack of available labelled images, a datadriven
anomalous threshold, and the trade-off of accuracy while
increasing interpretability in the black box optimization methods
are some key aspects considered in the model-building process.
Our novel framework for anomaly detection in image streams
underwent rigorous evaluation using a range of datasets that
included synthetic and real-world data, notably datasets related
to Amazon’s forest coverage. The objective of this evaluation was
to detect occurrences of deforestation in the Amazon. Several
metrics were used to evaluate the performance of the proposed
framework. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. |
en_US |
dc.subject |
Anomaly detection |
en_US |
dc.subject |
Image time series |
en_US |
dc.subject |
Machine learning |
en_US |
dc.subject |
Deforestation |
en_US |
dc.subject |
Explainable AI |
en_US |
dc.title |
Early identification of deforestation using anomaly detection |
en_US |
dc.type |
Conference-Full-text |
en_US |
dc.identifier.faculty |
IT |
en_US |
dc.identifier.department |
Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. |
en_US |
dc.identifier.year |
2023 |
en_US |
dc.identifier.conference |
8th International Conference in Information Technology Research 2023 |
en_US |
dc.identifier.place |
Moratuwa, Sri Lanka |
en_US |
dc.identifier.pgnos |
pp. 1-6 |
en_US |
dc.identifier.proceeding |
Proceedings of the 8th International Conference in Information Technology Research 2023 |
en_US |
dc.identifier.email |
nethmiw.17@itfac.mrt.ac.lk |
en_US |
dc.identifier.email |
nethmiw.17@itfac.mrt.ac.lk |
en_US |
dc.identifier.email |
nethmiw.17@itfac.mrt.ac.lk |
en_US |
dc.identifier.email |
priyangad@uom.lk |
en_US |