Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest

Mohla, Satyam and Mohla, Sidharth and Guha, Anupam and Banerjee, Biplab (2020) Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020, 11 October 2020through 14 October 2020, Toronto.

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Abstract

Detection of burn marks due to wildfires in inaccessible rain forests is important for various disaster management and ecological studies. Diverse cropping patterns and the fragmented nature of arable landscapes amidst similar looking land patterns often thwart the precise mapping of burn scars. Recent advances in remote-sensing and availability of multimodal data offer a viable time-sensitive solution to classical methods, which often requires human expert intervention. However, computer vision based segmentation methods have not been used, largely due to lack of labelled datasets.In this work we present AmazonNET - a convolutional based network that allows extracting of burn patters from multimodal remote sensing images. The network consists of UNet- a well-known encoder decoder type of architecture with skip connections commonly used in biomedical segmentation. The proposed framework utilises stacked RGB-NIR channels to segment burn scars from the pastures by training on a new weakly labelled noisy dataset from Amazonia.Our model illustrates superior performance by correctly identifying partially labelled burn scars and rejecting incorrectly labelled samples, demonstrating our approach as one of the first to effectively utilise deep learning based segmentation models in multimodal burn scar identification. © 2020 IEEE.

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IITH Creators:

IITH Creators	ORCiD

Item Type:

Conference or Workshop Item (Paper)

Additional Information:

Authors thank Prof. Subhasis Chaudhuri, IIT Bombay & Paulo Fernando Ferreira Silva Filho, Institute for Advanced Studies, Brazil for discussion and productive comments. This work was partially completed as part of TechForSociety initiative at Koloro Labs. Satyam Mohla and Sidharth Mohla acknowledge support from Microsoft for AI for Earth Grant & Shastri Indo-Canadian Institute for SRSF research fellowship.

Uncontrolled Keywords:

Amazon rain forest; Classical methods; Cropping patterns; Disaster management; Ecological studies; Learning-based segmentation; Remote sensing images; Segmentation methods

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

16 Nov 2022 05:57

Last Modified:

16 Nov 2022 05:57

URI:

http://raiithold.iith.ac.in/id/eprint/11268