@proceedings {1523134,
	title = {Green is the new Black: Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest},
	journal = {AI for Social Good Workshop},
	year = {2020},
	abstract = {
	Detection of burn marks due to wildfires in inaccessible rain forests is important for various disaster management and ecological studies. The fragmented nature of arable landscapes and diverse
cropping patterns often thwart the precise mapping
of burn scars. Recent advances in remote-sensing
and availability of multimodal data offer a viable
solution to this mapping problem. However, the
task to segment burn marks is difficult because of
its indistinguishably with similar looking land patterns, severe fragmented nature of burn marks and
partially labelled noisy datasets.
In this work we present AmazonNET {\textendash} 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. 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.



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},
	author = {Satyam Mohla and Sidharth Mohla and Anupam Guha}
}
