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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


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    International Conference on Information Technology Research (ICITR)

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